If you've ever used an app to find the best way through traffic or received product recommendations while shopping online, you're already familiar with data analytics from the consumer end. According to some estimates, each person creates at least 1.7 megabytes of data per second on average. That roughly translates into over 2.5 quintillion megabytes of data being produced every single day worldwide. As such, there is a huge demand, now and for the foreseeable future, for people who can organize data and interpret the stories locked within. As you approach this career pathway, you're likely to bring practical experience in problem solving, decision making, allocating resources, time management, and many other skills that are particularly suited for the job of a data professional. Many companies are searching for candidates to fill positions in this fast-growing, high-paying field. My name is Cassie and I've been a data scientist since before we called it data science. I lead decision intelligence here at Google and I'll be your instructor for the first course of this certificate program. Before I became Google Cloud's chief decision scientist, I worked as a data scientist in Google Research where I was involved with over 400 projects all across Google.  There are so many different flavors of project and challenge. Some of us choose to work on one project for years, others get involved with several new projects every week. The possibilities are endless. A data professional is a term used to describe any individual who works with data and or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and or building machine learning models. Machine learning is an alternative approach to automation, expressing the way you want a task done by using data instead of explicit instructions. Machine learning is an important component of the modern data professionals toolkit. To train a machine learning model, specialists put a bunch of potential data inputs through algorithms, tweak the settings, and keep iterating until promising outputs are produced. But training a model is only one small step in the professional machine learning journey. Machine learning techniques can also be used for data analytics and exploration with far fewer steps, and that's what you'll learn in this program. You'll discover additional opportunities to explore machine learning through the course resources, so do check those out. Data professional work spans a wide range of industries and impact affects a multitude of products and services. As we'll discuss later, there are also lots of different roles and titles that focus on data professional work. Think of them as data detectives, analyzing and interpreting their findings to reveal the stories within. I'm excited for you to get to meet some of them in this program. Google career certificates are designed by industry professionals with decades of experience here at Google. You'll have a different expert from Google to guide you through each course in this program. We'll share our knowledge in videos, help you practice with hands-on activities, and guide you through scenarios that you might encounter on the job. This certificate is designed to prepare you for a job in three to six months if you work on the certificate part-time, so this program is really flexible. You can complete all of the courses on your own terms and at your own pace. Throughout the program, we'll give you resources that will prepare you to advance in your career as a data professional. As you progress, you'll also build a repository of portfolio projects and a comprehensive capstone project that will showcase your abilities beyond your resume. You'll also have a supportive network of peer learners taking the certificate with you, and you can connect with them in the discussion forums. This program is designed to give you experience by building upon the knowledge and skills that you've developed to this point. Regardless of your experience with data and analytics as you begin the program, you'll learn about different experiences that are relevant and helpful for starting or advancing your career. In addition to building your skill set, we'll examine how teams of data professionals collaborate and contribute in the workplace. By the end of this program, you'll be ready to pursue a position in the data career space. By completing a Google career certificate like this one, you'll develop the skills and knowledge necessary for a job in this expanding career field. Once you graduate, you can connect with hundreds of employers who are interested in hiring Google career certificate graduates. Whether you're seeking to switch careers, level up your skills, or start your own business, the Google career certificates can help you take that next step. Throughout this first course, I'll be here to help you gain foundational knowledge needed to succeed in the field. Again, I'm so glad you're here. I'm excited for you to take these next steps forward in your career. I'll see you in the next video. Now that you have a general understanding about this program overall, let's talk a bit more specifically about what you can expect in this course. We'll start with the basics and a little background on what the data field offers. While you may already be familiar with work within the data field, I'm excited to dig deep into some history that shows us where we've been, where we are, and where we're going in this field. These key developments and applications will really showcase all the opportunities this program will help prepare you for. In this first course, we'll discuss the specific skills and characteristics that organizations look for in future employees. You'll develop the core skills necessary to advance on your journey as a data professional and integrate these skills with your own pre-existing abilities. We'll focus specifically on technical and workplace expectations. There will also be plenty of chances to practice along the way. Then we'll explore your job market opportunities. You'll learn about the variety of roles and positions that match your skillset. You'll also investigate the responsibilities and ethics that underpin all roles within the data career space. With the increasing number of industries that are turning to the data professions, you're bound to find a role that fits your interests. And we'll also explore some of these industries so you can see where data professionals are employed to make more informed decisions. Then we'll examine how larger organizations create teams of data professionals to approach larger scale projects. We'll also peek into the future of data careers and the trajectory of the field in general, so that you have a good sense of what the future holds for you after completing this program. We'll also investigate elements of effective communication and discover how it can empower you as a data professional. Throughout this course and the whole program, you will see how effective communication can elevate productivity and promote general understanding during the data analytics process. As you progress, you will gain hands-on data analysis experience through your portfolio projects, beginning with the first one in this course. We've designed a few different options for you to apply your data skills to actual scenarios with data shared by our industry partners. And you can use these projects to showcase your skills to future employers. Lastly, our instructors will provide a few career tips and tactics to guide you on your journey. That's a short preview of what to expect later in this course. Next, you'll have the opportunity to go over some resources that will help you get the most out of this program. I'll see you in the next video. I am playing with Excel and I had this gemstone collection and I would love putting the data into a spreadsheet. And this collection would grow not for the gemstones but definitely to be able to put it into the spreadsheets. I get excited. Oh, a purple one. I don't have an amethyst yet. You know, now I get to put purple into the color columns. I was a weird kid. But to me, data was the most beautiful thing on earth. But as my career progressed, I began to think a lot about the why of data because I took it for granted that data is pretty. But there has to be something important to motivate action. And the important bit was decision making. If a data point falls in the forest and doesn't lead to any kind of action, in my opinion, there's no point to it. It becomes valuable when it's related to decisions or real world actions. And so that's why I got really interested in the decision sciences and studied those alongside what we today call data science. Even though back then that was statistics and big data eventually showed up as one of them as well. So I studied all of those things together. I remember back when I was in college, some career counselors asked me, I know, what even what major is this? What you can't even get a job with this? Well, today, combining the decision sciences, thinking carefully about the why, plus the data sciences, the information piece, you get to use information to drive better action. And that is what I'm passionate about. A lot of people don't realize that without any courses in data analytics, they are already data analysts. We're all already data analysts.   When you open it with the correct software, in this case, your browser, you are extracting meaning sense from that raw data and you're learning something. So right now in this very second, you are doing data analysis. There are so many different ways to work with data. There are so many different ways to make it useful. Some of them are going to fit your personality better than others. Just guide yourself towards what's most fun, because there's also a lot of different people in these careers and it's a team sport overall. So they're going to cover the parts that you are less inclined towards. And so just lean in to the most fun that you can have pursuing your individual data personality. Just make it useful, and all the good things will follow. Now again, let's discuss some of the course items you'll encounter in your learning journey. In this program, you'll code in Python, discover the stories that data holds, develop data visuals, use statistical tools, build models, and even dabble with some machine learning. Along the way, you'll build a portfolio full of data projects, in addition to this program's capstone. Whether you're looking to switch careers, start a new career, improve your skills, or advance beyond your current role in a company, the Google career certificates can help guide you as you take steps towards new opportunities. We've gathered some amazing instructors to support you on your journey, and they'd like to introduce themselves now. Hello, I'm Adrian, and I'm a customer engineer at Google. Together we will explore one of the fastest growing programming languages, Python. You'll learn the basics, which will help you write scripts that perform a number of key mathematical operations on data sets. All designed to help you unlock the stories within data. Hi there, I'm Rob, I'm a consumer product leader. I work on marketing projects here at Google. I'm excited to talk to you about how to tell stories using data. We'll discuss the six practices of exploratory data analysis, and how to identify the trends and patterns in it. We will also learn about the importance of designing and presenting data visualizations using Python and Tableau, which can help you understand your data and convey it to others. Hello, my name is Evan. I'm an economist, and I consult with various teams across Google. Statistics helps you generate more complex ideas from the data itself. In our time together, you'll discover how you can generate insights, draw conclusions, make inferences, create estimates, and make predictions. Hello, I'm Tiffany, and I'm a marketing science lead, and I work with marketing data here at Google. I will guide you through the process of modeling relationships between variables. Together, we'll explore different regression models and hypothesis tests. We'll also talk about model assumptions, construction, evaluation, and interpretation as the means for answering data driven questions. Hello, I'm Sushila. I'm a data scientist, and I work on projects for YouTube here at Google. I'll guide you through building systems that can learn and adapt without a specific set of instructions. We'll discuss how machine learning is transforming the process of data analysis as you construct your own models. Hello, I'm Tiffany, and I lead teams focused on building AI responsibly here at Google. I'll introduce you to career resources and portfolio projects, and guide you through the capstone course at the end of the program. I'll assist you with different opportunities and tools that will set you up for success on the job market. And of course, you already know I'll be guiding you through course one. This is such a great time to grow and advance your career as a data professional. Your path to a career full of new opportunities awaits. Earlier, I referenced the tremendous amount of data generated each day.  For companies and organizations, all this data can provide insight into the ways they operate and ultimately interact with their users and consumers. To obtain these insights, organizations need people with the ability to access, interpret, and share the stories within their data. Organizations understand that data can inform decision making and explain consumer trends and user behavior. Data professionals use data insights to optimize products or services. There's a common phrase in data-driven decision making that references the untapped potential of data. The phrase is, imagine if we knew what we know. Basically, it's a way of asking the following question. How can we take all that data that may already exist and translate it into meaningful and actionable insights? To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Businesses seek those who can access data and understand its metrics. As a reminder, metrics are methods and criteria used to evaluate data. Both are necessary before creating predictive models that can identify trends and inform best practices. And that's where you'll come in. The combination of all these skills, from statistics and scientific methods to data analysis and artificial intelligence, all fall within the category of data science. Data science is the discipline of making data useful. To me, the idea of usefulness is tightly coupled with influencing real-world actions. Some individuals with these skills may work on developing business insights and supporting strategic decision makers. Others may use data skills to fuel automation, testing, and analytic tool development. Still, others may focus on the analytic process itself by adapting modeling approaches to incorporate new and emerging technologies. Data is the foundation for making future decisions. It's through our actions and decisions that we affect the world around us. Businesses and organizations need people like you who can think critically and analytically about how to directly address challenges and opportunities through data-focused projects. The work of data professionals can provide businesses and organizations with details about their practices that can promote new approaches and innovation. This might make a little more sense if we take a closer look at an example. A global delivery company, for instance. Generally speaking, delivery companies are responsible for transporting goods to consumers. A company as complex as this is going to have a number of different data inputs or streams that influence, impact, and affect the ways the business operates. These data inputs and streams may include, but are not limited to, weather and traffic patterns, which affect when deliveries are projected to arrive, gas price trends and fuel economy, which affect shipping costs and profit margins, truck loading times in relation to the number of workers available, which affects the time it takes for the delivery to reach its final destination, how users interact with the company's app to track their packages, which affects the customer experience and the company's ratings, and whether users engage with marketing emails sent after they make specific purchases, which impacts future and repeat sales. My point is, each of these variables affects the way organizations harness data to transform decisions, automating and adapting machine learning where applicable. The ability to unlock transformative information within data is a skill that businesses seek. As you progress within this program, you'll discover how data professionals can make meaningful contributions to almost any organization by finding action-oriented solutions within data. All professions require a certain set of tools for success and data-driven work is no different. In this video, we'll open our analytics toolbox and look at some of the most common items. Before we begin, I want to emphasize that each of the items serves their own individual purpose. However, when used together, they help build and tell stories with data, which can then inform, influence and impact business decisions. Programming languages are the first tools we'll investigate. They allow data professionals to work efficiently within and dissect large datasets. Most languages have been developed over time and each data professional has their own preferences. We'll mention two in this video that have become very popular for data analysis, the R programming language and Python. R is a programming language that's used extensively by researchers and academics. It was my primary language during graduate studies in statistics, and some people say that R captures the statistician's mindset. I'd say there's something to that sentiment. If you're after implementations of the latest statistical breakthroughs, R is a great place to look, but it's used for more than statistics. You'll find many new technologies and ideas programmed with it. One of the best futures of R is that you can create complex statistical models from just a few lines of code. If you're curious about R or need a refresher, be sure to check out our Google Data Analytics certificate, also offered here on this platform. This program teaches the Python programming language. It's a great choice for a few reasons. First of all, it emphasizes readability, making it one of the easiest programming languages to learn and write. Second, unlike R, Python wasn't born in the data community. While this might sound like a minus, it can also be a huge plus. In the modern world, data is used in increasingly creative ways. So there's a massive advantage to learning a programming language that's capable not only of handling the data side of things, but can also be used to build and deploy the applications that data will be fueling. Although R was my first love, these days I find that I lean more heavily on Python because of its flexibility. Python can perform a wide variety of data-related tasks, which makes it very popular among data professionals. If you're a novice or new to coding completely, Python is a very approachable language. Its formatting is visually uncluttered. It's one of the most beginner-friendly languages and it has enormous online communities and plenty of resources to help you if you get stuck. We will interact with Python within a web-based computing platform, also called Jupyter Notebooks, which allows you to run code in real-time and helps identify errors easily. To visualize the stories in the data, we're going to teach you how to share complex data through a graphical interface. Those who experienced our data analytics program will be familiar with a platform called Tableau. In this program, we'll take a more detailed look at how this powerful tool can help others understand the results of your analysis. Additionally, we'll look at effective communication in data-driven careers. At first glance, it might seem like less of a concern, but describing the sometimes complex processes of data analytics to non-technical stakeholders may be one of the most important skills a data professional can have. Since communication is something we all do regularly, it's easy to forget about the importance of how data professionals share and process data stories. Our goal here is to strengthen the communicative skills that you already possess so that you can leave this program equipped to excel. In this course specifically, and across other segments of this program, communication will be a key component that is directly tied to the work you'll do as a data professional. Programming languages allow data professionals to interact with and interpret data. Visual data tools like Tableau enrich the stories within data with visual elements that bring attention to specific details, but the most important element of any story is the storyteller. That's you. Your prior experiences and knowledge inform your storytelling abilities and your distinct background is what will set you apart from others in these roles. Regardless of your eventual career path, remaining determined and developing the proper skills is essential to personal and professional transformation, and the tools we're offering you in this program will also help you along the way. I'm thrilled to continue alongside you in your journey. The best is yet to come. I'll see you soon. Congratulations on completing the first part of this program. You've officially begun your journey to new opportunities in the data field. Let's revisit what we've covered so far. First, we covered the basic logistics of what's included in the certificate. You met each of your instructors and we previewed some of the different course topics you'll encounter. Next, we looked at the basics of data focused work and some of the industries that are incorporating data insights. We also discussed the future of data driven careers. In addition to exploring what it's like to work in the data field, we also discussed the data professionals toolbox and the skills this program will help you develop. Congratulations on your progress so far. I can't wait to see you in the next video. My name is Loyanne and I'm an industry intelligence lead at Google. What that means is that I help to make both our internal teams and our partners and advertisers a lot smarter about the industry that they work in. Early in my analytics career, it was actually pretty lonely and so I had a really big need to connect. I could not find a job immediately out of college and I had done lots of things, worked different places, didn't really build deep enough connections to have a community of people in this area. Early on, I started joining some networks. Networking is critical when entering a new field. For me, what was most jarring about this new thing was just the newness of being new. So you feel like a fish out of water. You are also subject to that very pesky imposter syndrome. I'm not good enough to be here. They're going to find me out. Having a network who was able to reign me back in and say, no, you're qualified and hear all the reasons why you're qualified and actually here's how these other skill sets you have make you an asset here was immensely valuable. In my role currently, I collaborate with tons of different people in data analytics. Collaboration is important because there are so many diverse parts of the story. And so you need people who are willing to and able to and good at asking the questions, which are, what exactly are we looking for? Are we looking in the right place? You need people who are then able to query and find a set of data, find the needle in the haystack. You need other people to find the pattern and to find what the insight is from the data. And then you need people to tell the story. Practicing curiosity is something that will always help you in any role that you're in, especially when you're dealing with data. I highly recommend participating in the forums and this certificate program because it's a great way to practice curiosity. Asking questions, getting comfortable asking questions, getting comfortable working on a team and asking questions of your team is super important. And beyond that, you have this opportunity to connect with your cohort.  And so getting to know one another, getting to understand what you're all thinking, and even opening up the door to innovate together is a super important and exciting opportunity. As you know, this program asks you to complete a graded assessment at the end of each section and course. And now's the time to prepare for your first one. This assessment will effectively verify your understanding of key data analytics concepts. It will also help build confidence in your understanding of data analysis while identifying any areas where you can continue to improve. Assessments can sometimes feel overwhelming, but approaching them with a strategy makes them more manageable. Here's a list of tips to set yourself up for success. Before taking an assessment, review your notes and the videos, readings and the most recent glossary to refresh yourself on the content. During the assessment, take your time. Review the whole test before filling in any answers. Then answer the easy questions. Skip the ones you don't know the answer to right away. For multiple choice questions, focus on eliminating the wrong answers first. Also, it's a good idea to read each question twice. There are often clues that you might miss the first time.  One way to do that is by completing a simple math problem in your head or spelling your name backwards. This also helps you recall information more easily. Before you submit the assessment, check your work, but be confident. Sometimes people change an answer because it feels wrong, but it's actually correct. Your first instinct is often the best one. Finally, remember to trust yourself. Often people know a lot more than they give themselves credit for. Everyone learns at different speeds and in different ways, but it's important to maintain your momentum. So take the time you need. And when you feel ready, keep moving ahead. You've got this. Great to have you back. I'm so delighted to be your guide as you continue on your way to becoming a data professional. As you've been discovering, data can be used in many ways. But no matter how it's applied, the key thing to keep in mind is that knowledge is power. The power to improve your business, your work, your personal life, and the world around you. With all of the data surrounding us, there's just so much potential waiting to be unlocked. I'm so glad that you've chosen to learn more and to be a part of it. And speaking of your learning journey, in this section of the course, we'll start by finding out more about data careers. We'll explore different industries and examine some direct applications of data-driven work. And we'll investigate where data careers are headed in the future. It's such an impactful and rewarding field. And it just keeps getting better. I'm so excited for you to get started. Let's go. How's it going? My name is Adrian and I'm a customer engineer at Google. A customer engineer at Google is the bridge between the technical and business components or aspects of corporate America. We help customers leverage our technology to meet their business needs. One of the most exciting projects and even interesting that I worked on was creating a unified patient record. And this project was for a lab diagnostics company that was really looking into creating a 360 view of a patient. Our project focused on consolidating information across different systems. You can imagine taking your lab diagnostics data for your most recent routine blood work or even something that's a little bit more specific, you know, a diabetes or glucagon test. Taking that and all that information that you've provided in terms of biological data, getting that centralized and standardized in one environment so that if it is your physician looking at the data or your radiologist or your nutritionist, they have a complete view of all of your data and they can not only just access it but they can visualize it in real time. One of the key components of this project was the use of virtual reality headsets as the main visualization component. For me what this meant was I got the ability to do some game design, create some human skins in terms of the body and use different colors and programming functionality to indicate, for example, the system is looking healthy or if it's looking like it needs concern or intervention. We can see over time how the diagnostic test results have changed and the impact that this has on our patients. At the end of the day what we had was a project that unified all of the data for the patient and gave the key medical professionals a way to visualize it. The best piece of advice that I have for you is to remain confident in yourself. While you might be new to this new role or this new company, you're coming with skills that are repeatable, that are transferable, whether it be the way that you approach problem solve, whether it be the rapport you're able to build with your stakeholders and the soft skills and ensuring that you can build those relationships or even when it comes to communication and writing a succinct and concise email, there are going to be ways where you're going to take what you've done before and apply them in your new role. You might have to think outside of the box but you'll get that. Data professionals are so valuable to their companies. They determine which data streams are most important to specific business projects, challenges and initiatives. They identify key goals for the future and they give their organizations the ability to take meaningful action by reimagining processes and improving operations. To do all of this, data teams require individuals with diverse skills, knowledge and interests. Therefore, they're countless different data focused roles, responsibilities and project types which are further differentiated by the industries and businesses they support. Among all of these possibilities, data careers can be loosely categorized into two complementary types of work, technical and strategic. In this video, we'll investigate both. First, let's find out about the folks whose work requires a heavy emphasis on technical skills. Some examples of these professionals are machine learning engineers and statisticians. These people provide high effort solutions to specific problems. Through their expertise in mathematics, statistics and computing, they build models and make predictions. Using tools such as R and Python, they help their teams extract value from business data sets. The result is a solution that has a direct positive impact. Another highly technical role is the expert data analyst whose work involves exploring vast and complex data sets to identify directions worth pursuing in the first place. They ensure that an organization's data science efforts are directed as efficiently as possible, bridging the gap between other technical data professionals and the strategic work we'll cover shortly. Like most technical data professionals, you'll learn how to acquire, scale, organize, structure and manipulate data so that it's packaged in a way that others can work with. In other words, you'll know how to transform raw data into something useful for decision-making. Okay, now let's consider data professionals on the more strategic side.  Strategic data professionals use their skills to interpret information that affects an organization's operations, finance, research and development and so much more. Their work aligns closely to the overall business strategy and involves seeking solutions to problems through data analytics. In short, strategic data professionals maximize information to guide how a business works. Sometimes you'll find a company has roles that blend specialist technical knowledge with strategic data expertise, often in unusual and very creative ways. Soon we'll learn more about some of these opportunities as well as the more specialized, technical and strategic roles and of course, we'll discover some proven ways to tap into them as a data professional. Lots more to come. Nowadays, it seems that data is generated everywhere. Computer functions have been integrated into a multitude of everyday technologies. Smart home voice assistants, step trackers, TV streaming apps, even some coffee makers are connected. All of these touch points create data that businesses can use to understand trends and advance their products and services. And with every update, the ability to gather environmental and user data expands even further. Within this massive reserve of data is a wealth of untapped potential, awaiting data professionals and the organizations they support. That's why these people are so in demand. Companies need them to use data to refine business strategies, meet consumer preferences, react to emerging trends and re-align internal efforts. Let's consider some examples of how data professionals use their expertise to transform industries. First, the world of big finance was an early adopter of the power of data science. And with the way information drives this industry, it's easy to understand why. Data professionals help financial institutions, assess investment risks, monitor market trends, detect anomalies to reduce fraud, and create a more stable financial system overall. Hundreds of millions of financial transactions occur in the financial world each day. And data is part of each and every one of them. As another example, data analytics is key in healthcare. And here, the data benefits can actually be in life-saving. For instance, the information collected by smartwatches is making a huge difference in the lives of many people. Sensors in these devices record biode data, such as heart rate and oxygen levels. And of course, all this information can be shared with healthcare professionals. Together, the patient and the physician can better understand sleep trends, stress levels, and much more. Then individualized wellness plans can be created and modified for the patient's well-being. Plus, on a larger scale, data analytics is helping healthcare organizations process large amounts of clinical data, which supports the early detection of a health condition and leads to more precise diagnoses. Thirdly, data has a big impact in manufacturing. Data professionals predict when to perform preventative maintenance to avoid production line breakdowns, use data to maximize quality assurance and defect tracking, and artificial intelligence models help respond to logistical issues and reduce delivery truck miles on the road, advancing key sustainability goals. And in a time when supply chains reach every corner of the world, data enables clear and near real-time communication with suppliers, retailers, and other network partners. It also helps supply chains maintain optimal levels of inventory to avoid stockouts and empty retail shelves. Data professionals are also advancing approaches to agriculture. With data insights, farmers develop new ways to approach crop production, livestock care, forestry, and aquaculture. The inclusion of autonomous machinery, tractors, and irrigation systems is improving harvesting technologies as well. If you'd like to keep learning about how various industries use data analytics, refer to the course resources on this topic. And here's a little piece of advice for me. Don't miss an opportunity to learn from someone in real life. I love asking business owners, store managers, and client support professionals about how they use data each and every day. Who knows? One of these conversations could open a door to a future opportunity for you. Recently, you've been learning about how businesses use data to guide decision-making, answer questions, and solve problems. In this video, we'll investigate how nonprofits use data analysis to pursue their unique goals. Non-profit groups are created to further a social cause or provide benefit to the public. As the name suggests, their main purpose is not about profit, but to foster a collective, public, or social advantage. There are some very rewarding and inspiring opportunities for data professionals in the nonprofit sector. In particular, data can be applied in order to help these groups more effectively anticipate and respond to the greatest areas of need. For instance, maybe a U.S. charity that provides bicycles for children would like to determine which neighborhoods are most in need. They could ask their data professional to access the U.S. Census Bureau. The professional would use their talents to navigate the census database, identify key metrics, and summarize findings with analyses and data visualizations. This report would highlight where there are larger numbers of school-aged children in need who would benefit from the resources of this program. And there you go. Data insights led to informed decisions about where this nonprofit can do the most good. Now nonprofits do more than use data. Many of them also collect it. As you likely know, public entities and government agencies can be excellent resources for useful data. And much of it is open data that's available for general use. As you likely know, open data is data that is available to the public. It's free to use and guidance is provided to help navigate the datasets and acknowledge the source. While sourcing open data is a good way to interact with data on your own, there are other opportunities that enable you to refine your skills while helping others. Data volunteers contribute to many projects that help nonprofits benefit communities all over the world. To find out more, here are some organizations to check out. First, the Data Science for Social Good Foundation was launched at the University of Chicago back in 2013. In 2020, they trained forces with UNICEF to analyze various aspects of air pollution around the world to help monitor children's health. DataKind launched in 2011 in New York City with chapters in the United Kingdom, Bengaluru, San Francisco, Singapore and Washington, D.C. This organization analyzes the cost of environmental cleanup in different underserved communities to guide restorative efforts. You can view both foundations latest efforts through the links in the transcript for this video. Another option for putting your data skills to good use are hackathons. A hackathon is an event where data professionals and programmers come together and collaborate on a particular project. The goal is to create a solution to an existing problem using technology. Some examples include developing better tools for predicting extreme weather events, creating tech to help elementary school kids learn important reading skills and identifying ways that community development groups can use their data to advance home accessibility and affordability. Volunteering your data skills to public projects is an excellent way to contribute to the greater good while gaining experience and networking with others in your field. Coming up, we'll take a deeper look at some data-oriented projects in the public sector and how they're making an impact around the world. One of the most important responsibilities for those of us in data-centered careers involves how we protect our organizations, manage and protect data. This has a lot to do with communication exchanges between a company and its customers. As you've been learning, almost all communication generates data, whether it's a shopping receipt, confirmation of an order, or even earning customer loyalty points. And businesses have a big responsibility to their customers, especially when it comes to maintaining and protecting user privacy. Any data gathered from individuals or consumers is referred to as personally identifiable information or PII. PII permits the identity of an individual to be inferred by either direct or indirect means. This includes things like biometric records, user names, and social security or national identification numbers. Because this information is often associated with medical, financial and employment records, PII is sensitive and must be managed with great care. After all, when someone's personal data is improperly handled, they become vulnerable to identity theft, fraud and other issues. Recently, there have been great efforts to take a wider view of data collection practices and protect individuals. Industries are trending towards aggregate information. This is data from a significant number of users that has eliminated personal information. By aggregating the data and removing PII, this protects people and gives them more control over their own data. Similarly, as more industries become interconnected, the amount of data available to them increases. And just as with aggregate information, the more data collected, the more likely it is that it will be representative of a wider population rather than a single user. A key thing to keep in mind is that data gathering is a task managed by humans. And that process can be informed by different backgrounds, experiences, beliefs and worldviews. These and other types of biases can affect the way that data is communicated and how the results are shared, which in turn can have an impact on business decisions. Effective data professionals know that, with a collecting, analyzing, interpreting or communicating sensitive data, bias should always be considered. So be very careful when interpreting data where there is a clear source of bias and be on the lookout for subtle biases as well. In addition to thinking through bias in the data, data professionals should also try to emphasize that there can be a multitude of possible interpretations for every data insight. So the main trick is avoid jumping to conclusions until you've really done your homework. One method of addressing bias is to make sure that the data that you're working with has the same characteristics as the greater population that you're interested in. In data analytics, this is called a sample. A good sample is a segment of a population that is representative of that entire population. Here's an example. A clothing company is analyzing sales in their highest growth market. They want to determine what color shirts will be most popular in the upcoming season. One person notes that red and blue shirts accounted for 80% of their sales in this market over the past three months. This is a big number so they suggest ordering lots of red and blue shirts. But another person points out that the local sports teams' colors are red and blue and this team had recently won a championship. It's very likely that sales of red and blue shirts will have spiked as consumers purchased teas to support the local team. Plus, they note that although this market is high growth, it only represents 40% of the retailer's total sales. With all this information in mind, decision makers at this retailer instead choose to evaluate color popularity over a full year and across all markets. This will provide a much more complete picture. We'll investigate more about bias later in this program. And as you progress, you'll discover many more strategies for ensuring that you're aware of bias and proactively working to counter it in all of your data work. When investigating a possible new career path, one of the most important things to consider is its outlook and potential for growth. Predictions about careers related to data analysis show that there is no shortage of need for professionals in this field. Over the last decade, data-focused careers have surged. According to estimates by LinkedIn, the data science field grew by over 650% between 2012 and 2017. Many experts believe that we have not yet seen the full potential of these careers. In fact, the US Bureau of Labor Statistics stated that data science is one of the fastest growing career fields in the United States, projecting a 30% increase over the next decade. Among the data science professions, one of the fastest growing is artificial intelligence and machine learning. And we've seen significant advances in these areas in recent years. At its core, artificial intelligence, or AI, is the development of computer systems able to perform tasks that normally require human intelligence. Thanks to growth in the data sciences, AI is now becoming more commonplace. These technologies will continue to evolve and provide more accurate results and richer insights. And as AI increasingly becomes an essential component of data work, it's important to be aware of the human bias that can be imprinted within your work. To counter this, organizations benefit most from building diverse teams of professionals from different backgrounds and different life experiences. Incorporating a wide range of perspectives and worldviews promotes wider representation and yields more accurate results. As we study the future of the data professions, I want to emphasize that data professionals have yet to realize the full potential of artificial intelligence. As these types of technological innovations continue to evolve, we can expect that organizations will grow and adapt their business practices accordingly. With wider and wider adoption of data analysis techniques, the most likely area for growth is in specialization. And we expect to see further subdivision of roles within data-focused teams. Ultimately, what I want you to keep in mind is this. The world is generating more and more data every year, so it's reasonable to expect labor that extracts business value from it to be able to earn its keep. More data means more demand for the three main activities covered by the data professions, statistical inference, machine learning, and data analytics. So those skills will stay very relevant, though their names might evolve over time. In addition, constant innovation in the field offers you the opportunity for perpetual learning, growth, and development. As you may already know, being a data professional means that your growth and success in this field depend on a desire to keep learning. In fact, that just might be the reason you enrolled in this program.  Continue to explore opportunities to evolve throughout your career. Be proactive in acquiring new skills, keep growing, and you will always be ready for the future. In this section of the course, we explored many different facets of data careers. You learned that data professional is a broad term that encompasses different roles and responsibilities in the data space. You discovered that the work we do in this field involves countless possibilities, such as determining important data streams, identifying and focusing on future business goals, and reimagining internal and external processes. You also thought about some of the ways that organizations are being transformed by data professionals and how these talented individuals use their skills to positively impact communities around the world. You've come so far already, but there's so much more to learn. Thanks for joining me on this exciting exploration, and I'll catch up with you again soon. Welcome back. I'm really excited to share this section with you. We'll start by looking at some of the skills needed in the data field today. We'll look at what happens after you land a data position and what to expect in those first few weeks on the job. We'll investigate some of the roles within the data professions and take a closer look at the general categories that you'll encounter. Then we'll explore the importance of networking and building relationships within an organization. We'll also look at the responsibilities of different data professionals. So let's get started. See you in the next video. All data professionals share a love of data and a desire to solve problems. While wearing their analytics hat, data professionals lay out the story that they're tempted to tell and then they poke it from several angles with follow-up investigations to see if it holds water before bringing it to their decision makers. In doing so, they rely on their programming and investigative skills to guide others towards informed decisions. Data professionals also combine a knowledge about how to do practical tasks with an awareness of what makes communication and collaboration successful. Later, we'll dig deeper into the elements of communication and discuss the ways communication enhances and structures your work as a data professional. For now, let's examine some skills and attributes that are applicable across data-driven careers. Working in data analytics requires a mix of business sense and knowledge in gathering, manipulating and analyzing data. Our goal is to prepare you to develop the competencies needed to succeed. Let's start by discussing some interpersonal skills. Often, these are referred to as people skills. They focus on communicating and building relationships. Interpersonal skills are critical. In this field, there's a high degree of interaction between stakeholders. This is especially relevant now with team members often working collaboratively across the globe. Very often, work conversations are the starting point and the fuel that drives projects. And because of the cyclical processes within data analysis, communication is always ongoing. Another important skill is active listening. This means allowing team members, bosses, and other collaborative stakeholders to share their own points of view before offering responses so that each exchange improves mutual understanding. You can actually practice active listening. Next time you speak with someone, put extra effort into listening beyond their words. Focus on what they're trying to communicate. Your listening and communication skills will play a huge role in helping you capture effective insights and informed decisions. We'll take a closer look at communication a little later in this course. There are other things you'll need to consider. As a data professional, you'll search for information hidden within a large amounts of data by applying critical thinking skills. Along the way, you'll investigate the connections between a variety of different data sources as you search for trends and indicators. Think of yourself as a data detective. Project data can come directly from your organization or from other sources. You might be lucky and receive a well-formatted spreadsheet or database, but quite often, you will need to prepare the data to get started. This process is known as data cleaning. This is where the data is reorganized and reformatted. The goal is to remove anything that could create an error during analysis. This process includes tagging and consolidating duplicates, irrelevant entries, structural errors, and empty space. Once you have everything in the proper format, you can then filter out unwanted material. Now your data is ready to be analyzed. It's time to look for trends and tendencies. Often, it's very helpful to render the data visually to reveal additional insights through charts, dashboards, and reports. Graphic tools will be very useful in identifying patterns, as well as in sharing information with others. You will explore this in greater detail later and have opportunities to practice compiling visualizations You'll also learn about more advanced skills like building models and machine learning algorithms. These tools will help you and other data professionals assess information accuracy, analyze specific data segments, and predict future business outcomes. Your hard work will assist leaders and other decision makers in your company, providing them access to a rich variety of perspectives on different sets of information. With demand for data analytics increasing across all types of companies and businesses, you will likely find opportunities in an industry that you are personally interested in. Next, we'll take a look at working in the data field. My name is Cliff and I'm a workforce planning and people analytics lead at Google. I use data to help our employees be more productive, more connected, and just overall improve their well-being. I also use data to improve our HR practices and focus on our hybrid work policies as well as our location strategy. I've always been interested in issues of workforce development, people strategy, human resources, but I didn't anticipate what a central role analytics would play in my work and how much it would come to love analytics. One of the things that has helped me develop a confident voice in this space has just been an understanding that we work in teams who work cross-functionally and I don't need to bring all of the solutions to a problem. I'll bring a perspective on how we can use data to solve this problem, but I'm working with people who also bring a wealth of skills and looking at it really as a partnership it's really about leveraging the best of everyone on the team. That's helped me bring a lot of confidence to the work. My go-to strategy for communications when working with partners is to first set up a few low stakes meetings just to understand what their broader business goals are. I'm not even thinking about the specific project we're working on but more broadly, how do they define success? That helps me understand where the work that we're doing fits into the context of their bigger picture. The second thing I do from a communication standpoint is to try to play back what I think I heard somebody say sort of to repeat it back to them if whether it's my understanding of their question or the output that they're trying to see from the data just to test if I actually really understand their goals. When I'm working with somebody and I feel we're not getting to the root of a question or a problem what I find is really helpful is laying out from a data perspective a set of different options or possibilities for them and engaging in a conversation around which of those really resonate for them and so it's finding a balance between listening and telling as a way to unlock some ideas that they might not have thought about themselves. You can learn a lot about a career by looking at job postings. If you've searched for opportunities in the data space you may have noticed different data related job titles with similar responsibilities or postings with similar titles listing different responsibilities. Here's an example. At one company the role of data analyst will focus on using statistics and models to craft insights that inform business decisions. Another job with the same title at a different company may focus on optimizing the tools and products that automate analytical processes. One reason for these inconsistencies is that data tasks and responsibilities are dependent on an organization's data team structure and how they make use of insights and analytics. As such some organizations choose to be very specific with responsibilities. Others leave job tasks quite broad in scope. That's why this program refers to the field as a career space. With that said when you're comparing positions that have similar titles I encourage you to classify them based on the skills used in their day-to-day activities. Two of the most common titles are data analyst and data scientist. These can cover a wide range of job responsibilities many of which you'll gain experience with in this program. Traditionally a data scientist was expected to be a three-in-one expert in data analytics, statistics and machine learning. But not all employers use these conventions when writing their job descriptions. Generally any role that includes analytics expects candidates to be able to function as technically skilled social scientists looking for patterns and identifying trends within big data sets. Also, they develop new inquiries and questions as they uncover the stories inside their data. Their hard work can help steer companies, future actions and guide decision-making. They allow their organizations to keep a finger on the pulse of what's going on in the business interpreting and translating key information into visualizations such as graphs and charts allowing every stakeholder to understand their findings. At times they may be tasked with creating computer code and models to recognize patterns in the data and make predictions. When you investigate job postings you'll encounter other titles with similar responsibilities. For example, junior data scientist, data scientist entry level, associate data scientist or data science associate. All of these roles include a mix of technical and strategic skills to help others make informed decisions. In your career you might encounter other professionals in roles that use data and analytical skills. Some of these may overlap with the skills you will learn but these roles are specific to certain tasks or are supervisory positions. Let's take a look at a few. Data scientists depend on systems within their companies to collect, organize and convert raw data. Designing and maintaining these processes are some of the most important responsibilities of a data engineer. Their goal is to make data accessible so that it can be used for analysis. They also ensure that the company's data ecosystem is healthy and produces reliable results. These positions are highly technical and typically deal with the infrastructure for data usually across an entire enterprise. You also need to have the ability to get data before it even makes sense to talk about data analysis. Most of the technical work leading up to the birthing of the data may comfortably be called data engineering and everything done once some data have arrived is data science. Similar to how a data engineer oversees the data infrastructure, there are data roles that manage all aspects of data analytics projects for a company. Insights managers or analytics team managers often supervise the analytical strategy of the team or of the organization as a whole. As a data analyst, you will likely report to someone working in this capacity. They are often responsible for managing multiple groups of customers and stakeholders and they're often a hybrid between the data scientist and the decision maker. Since this combination of skills is rare, these positions are often more difficult to fill. This role can have other titles like analytics team director, head of data or data science director. You may encounter another job role in your scan of job postings, business intelligence engineer or business analyst. This role is highly strategic, focused on organizing information and making it accessible. BI analysts synthesize data, build dashboards and prepare reports to address specific needs for a business or requests from leadership. If you're interested in learning more about business intelligence and its opportunities, I encourage you to look into the Google Business Intelligence Certificate. Now that you have some idea of the roles found within the data analytics career space, we will begin to take a closer look at how data professionals function within their larger organizations. Compared with many other professions, the data career space is relatively young. This application of data-driven work in organizations has grown exponentially in the last several decades, which means there are many different opportunities and much job security for you in the future. Now that organizations have the technical capacity to take on their own data-focused work, they're looking for people like you with the right skills to fill these jobs. Traditionally, companies have filled jobs in the data career space with those from computer engineering backgrounds or from statistics. Increasingly, there's been a shift towards de-emphasizing engineering and instead promoting analytical skills. These skills can be learned in different forums, like the program that you're currently enrolled in. Let's look at a scenario. Let's say that an enthusiastic and enterprising person, that's you, is starting a new position at a company as a data professional. Your company is a recognized leader in its industry. Its workforce spans the globe and you are its newest member. It's your first day on the job and you are ready to start working. During your orientation, your company grants you systems access and onboarding documentation. You're starting to have a clearer picture of how information is generally shared with employees. You still have many questions about the responsibilities of the position. Later, you watch a video from the quarterly review meeting led by a company executive. Watching the presentation, you get insight into the quarterly budget, recent client interactions, and some general information on an upcoming project. You now have a broad understanding of the company. At this point, you still lack details about your specific responsibilities. During your first week, you're invited to a virtual meeting of the data professionals involved on the project that you've been assigned to. As each data professional outlines their job responsibilities, you take note of the differences among them. After each participant speaks, you begin to realize that not all data tasks are universal and that many data professionals end up adapting to meet the needs of the current project and the needs of the data. When you're new to a job, I would discourage you from over-specializing immediately. Instead, taking on a variety of tasks within a project is a great way for newer data professionals to continue developing their skillset. As a member of a larger group of data professionals, you're able to observe and learn from your team members. Once the analytical process is complete, the results of the project will need to be shared, allowing everyone in the organization to have access to the information. This includes building user-friendly interfaces and communicating the findings to different departments. Working for a large company means that there's a good chance that you will be dealing with vast amounts of information. This will require more work than a single data professional can reasonably provide. Because of this, you might encounter scenarios where organizations have created teams of data professionals. Throughout the rest of this section, you'll take a closer look at how complex organizations are incorporating data professionals through data teams and the division of responsibilities within these teams. Hi, I'm Tiffany, and I lead teams focused on building AI responsibly here at Google. I've served in the United States Army, worked as a consultant, and have worked as a program manager in privacy and machine learning fairness. Data and having a rich understanding of data has always been an important part of my job. Today, we have more data available to us than ever before, and it's important to be able to derive insights to help decision makers make the best possible decisions. I'm so glad you're here, and I really hope this program is giving you all kinds of new possibilities to think about. You've already learned so much. We've covered the basics of data-driven fields and looked at career roles, how data professionals are being used by different industries, and how those in the field can make a valuable contribution. You're gaining a vast range of knowledge and skills, which is going to be extremely valuable as you prepare to join us in the amazing field of data-driven careers. At this point in the program, I encourage you to take some time to reflect on how your experiences so far are setting you up for a great career. And one way to do that is by enhancing your current online presence. In the Google Data Analytics certificate, we covered numerous job-related materials, including how to create an effective resume and LinkedIn profile. This video is about improving your existing career assets. Those of us who were involved in the Google Data Analysis certificate always love receiving learner feedback, especially when it has to do with someone else's professional success. I remember one person who took the initiative to refine her LinkedIn profile as soon as she began the program. She noted that she was currently working through her program and she added to her profile many of the technologies she had become familiar with. Well, not long after, she saw an advertisement for her dream job. Even though she was early on in her D.A. education, she decided to apply for it. And she got it. The hiring manager told her that the fact that she had familiarity with those data tools really set her apart from other candidates. There are tons of stories just like this one that prove the value of having a compelling and professional LinkedIn presence. So let's get into that now. A professional online presence enables you to better connect with others in the field. You can share ideas, ask questions, or provide links to a useful website or an interesting article in the news. These are great ways to meet other people who are passionate about data-focused jobs. Even if you're already part of the community, strengthening your network makes even more dynamic. LinkedIn is an amazing way to follow industry trends, learn from thought leaders, and stay engaged with the global data analytics community. And of course, it has job boards and recruiters who are actively looking for data professionals for all sorts of organizations and industries. So it's a good idea to always keep your profile up to date and to be sure to include a professional photo. Beyond that, consider including a link to some of the relevant projects you've done in data analytics, such as the portfolio project you'll work on during this program. As you continue expanding your online presence to represent the work you're doing in data analytics, the connections you make will be an important part of having a truly fulfilling networking experience. Plus, there are also many rewarding in-person networking opportunities, which we'll explore soon. See you then. My name is Tiffany, and I work at Google, and my job is to ensure that our products are fair and inclusive. My first role in data analytics probably harkens back to my time spent in the United States Army. And there I did a lot of work with data, trying to understand which decisions I should make for my soldiers and for my unit, making sure I was making the best data-driven decisions to ensure their safety and well-being. Coming out of the military, I felt a tremendous amount of imposter syndrome. I felt insecure. I wasn't sure of what I could be good at since I had such a highly specialized job, but I talked to a lot of mentors, a lot of friends that encouraged me and told me about transferable skills. Some of the skills that I gained in the Army were very, very clearly helpful to me in my career today. The one that stands out to me the most is the ability to frame a problem. So the ability to think about what someone needs, the data that you have, and how to connect in the middle, how to frame it out so there's no scope creep, so that you have a very crisp and clear articulation of the problem and a very clear and crisp articulation of that solution set. And I learned that in the military and I continued to build upon my skill base, continue to go online and read books and shore up that knowledge and over time I became more and more confident of what I could accomplish and the things that I could reach for. All of the courses that I took, all of the hard work, all of the imposter syndrome really all led me to the job, my dream job that I have today. It's important for people that have non-traditional backgrounds or non-traditional paths to get into a certificate program such as this because we know that education is uneven. Opportunities are uneven. I'm one of the first people in my family to go to college to get an education and being able to do so has opened up so many doors for me. And so many of you may be in the same situation. If I were to give anyone advice as they're transitioning out of the military and into a data analytics career I would tell them to use some Google products. So Google has a career translator where you can put your military service, your branch, your job into that translator and it will spit out transferable skills that you may have that you can place on your resume. I'd encourage you to take the leap of faith and get rid of and shed the imposter syndrome and apply for as many jobs as you can. And finally, I would encourage everyone to try to find someone who works at the company, try to get that referral, be bold on LinkedIn, be bold on other platforms and just try to make your way into the job that you see yourself in. Recently you learned about the value of maintaining a professional online presence and connecting with others in the data field. As I noted, there are many professional networking sites such as LinkedIn that are well worth your time and involvement. But here's something that many people don't realize. Some of the best opportunities are never actually shared on a networking site. Sometimes there are professional opportunities that are not publicly advertised by the employer. There are lots of reasons why some positions are not posted. Maybe an employer is concerned about revealing details about confidential projects to its competitors through a job posting. Or perhaps the company HR department doesn't have the resources to review a flood of applications. Often a business may choose to use a recruiter instead of posting jobs. So let's start exploring how you can increase your visibility to access more opportunities through building valuable relationships. After all, the more people you connect with professionally, the greater your chances are for being referred. Be sure to follow best-in-class organizations and visionary business leaders on Twitter, Facebook, and Instagram. Interact with them and share their content. If there's a post you like, consider commenting with a response or a thank you. You can also search for data field webinars featuring interesting speakers and many of these events are free. This is another fascinating way to learn while connecting with peers, colleagues, and experts. And there are also lots of blogs and online communities that focus on data fields. Data and tech companies will often talk about what's new and important from their point of view, but there's a growing community of bloggers and podcasters who offer great perspectives of their own. Now let's move to in-person networking opportunities. The easiest way to find events is by simply searching for data science or data analytics events in your area. You'll likely discover a wide range of engagement opportunities for more formal conferences and seminars to casual meetups and get-togethers. Non-profit associations are also wonderful resources and may offer free or reduced-rate memberships for students. In addition to networking, learning from a mentor can positively influence your career and life. As you may know, a mentor is someone who shares knowledge, skills, and experience to help you grow both professionally and personally. Mentors are trusted advisors and valuable resources. The first step in finding a mentor is to determine what you're looking for to narrow down your potential list. Think about any challenges you face or foresee and how to address them in order to advance professionally. Then, consider who can help you grow in these areas as well as fortify your existing strengths. Share these things openly when you formally ask someone to be your mentor. It's also helpful to note any common experiences. Perhaps you grew up in the same city. Maybe you both worked in the same industry. Your mentor doesn't have to be someone you work with currently. Many people find mentors on LinkedIn, an association mentorship program or at a mentor matching event. This really taught me the value of mentorship. I also learned that having a successful mentorship experience requires effort and investment in time, whether you're preparing to ask the right questions internalizing the feedback or scheduling follow-up sessions. But it's well worth it. Always be open to connecting with new people. You never know where a single conversation will lead. Congrats on your progress so far and on taking meaningful action to advance your career. I wanted to let you know about some of the great career-building activities and resources you'll encounter in the rest of this program. In the next course and those that come after it, you'll have the chance to complete a number of hands on activities based on data-driven scenarios. They'll let you put what you're learning into practice and help you discuss your skills with hiring managers in a concrete way. Be sure to save your work from these activities. They'll be useful to you as you near the end of the program and start thinking about the next stage of your data-driven career. When you get to the last course in the program, we'll go in-depth on preparing for a job search. We'll cover how to find and apply for jobs that interest you. I'll also share some tips to help you prepare for the interview process so you'll know what to expect going in. You'll learn how to put together an online portfolio that will help you demonstrate your knowledge and experience. You'll also complete a scenario-based project from beginning to end that you can put in your portfolio and use to present your working process to potential employers. With your past working and educational experiences, your career journey will be unique to you. But whatever path you choose, the knowledge and resources you gain from this program will give you a strong start. You've accomplished so much already, and there's so much more to come. Good luck on the next part of your journey. I'm excited to meet up with you again soon. As you approach the end of this section, let's take a few moments to review some key concepts before moving ahead. We saw that the data career space has experienced amazing growth over the last decade. Future predictions indicate that this should continue. You discovered that all kinds of companies and organizations are looking to drive their future decisions with data, which is creating opportunities for data professionals. Additionally, we saw that innovation is the engine driving this continued growth. You also discovered that data skills and tools are becoming more universal. At the same time, experts foresee a continued specialization of roles within the different fields. We were introduced to artificial intelligence and saw how it has become an important tool for data professionals. We also took a brief look at some of the common roles and responsibilities in today's data professions. While we reflect on the information we've covered so far, please remember that you're not taking these steps of personal and professional growth alone. One of the most beneficial resources available to you is our discussion board. Take advantage of the opportunity to interact with like-minded learners and gain knowledge from their experience. Coming up, we'll take a closer look at the skills needed by data professionals and we'll investigate how larger organizations incorporate data analysis. I'm looking forward to joining you as we continue your journey. I'll see you in the next video. We began our journey into the data professional world by covering the basics of data science, exploring careers, and discussing skills needed to succeed. In this section, we'll investigate the workflow within data-driven careers. You'll be introduced to a helpful organizational tool called the PACE model that can provide structure while allowing great flexibility when working on projects. We'll explore the elements of effective communication and some best practices when sharing your ideas with others. All of these skills and tools will help you prepare for your upcoming portfolio project. I know you're excited to continue. So, let's get started. The most important part of any project is preparation. That involves thinking through all the necessary steps and anticipated tasks. Let's say you were planning a dinner party. You would start by planning the theme, menu, the guest list, and all the other details. Next, you might check the reservation list, dietary restrictions, or take a trip to the grocery store for ingredients. Afterward, you would return home to prepare your dishes, clean your space, set up the table, and get ready. Because you set up so much beforehand, the evening will be awesome. While this scenario may be fictional or not, it offers some wise advice. As a data professional, being able to visualize data, predict outcomes, and quickly pivot away from obstacles makes you a problem solver and a great asset to any organization. Benjamin Franklin once said, by failing to prepare, you are preparing to fail. After almost three centuries, it still applies to something as simple as a dinner party or as complex as a deep space mission. Regardless of the project, having a structural framework in place for how to get that work done can be immensely beneficial. I've done a lot of data science consulting in my time, and one of the most common problems I've seen is teams coming to me thinking that they need advice on which tool or equation to use, and all of us finding out during the meeting that they're solving entirely the wrong problem. All the math in the universe won't help you if you don't point it in the right direction, but it's easy to get excited about those nitty gritty details and rush headlong into a waste of time. The best teams I've worked with have adopted a framework to help them focus on the most impactful actions in the most efficient order, and they've had the discipline to use it to stay on track instead of running off into the weeds. For those of you who completed the Google Data Analytics certificate, you'll recall a structure for the data workflow that was divided into six phases. Ask, prepare, process, analyze, share, and act. This framework is quite useful for a multitude of projects, but often with bigger datasets you need more freedom and flexibility. The PACE framework designed for this program offers the same workflow and structure, but in a simpler way. PACE is a framework developed with input and feedback from our team of data professionals. The intent of PACE is to provide an initial structure that will help guide you through projects. The goal is to lay a foundation upon which you will develop your own workflow practices. The PACE framework helps you solve problems and make judgments quickly and efficiently. PACE is an acronym. Each one of the letters represents an actionable stage in a project. Plan, analyze, construct, and execute. In the plan stage, you'll define the scope of your project. You'll begin by identifying the informational needs of your organization. This is where you'll want to ask yourself questions like the following. What are the goals of the project? What strategies will be needed? What will be the business or operational impacts of this plan? Taking inventory of the project and the tasks required will help you get a better understanding of the context of your work and prepare for success. During analyze, you'll engage with the data. You'll start by preparing it for your project. Here you'll acquire the necessary data from primary and secondary sources. And then you'll clean it, reorganize it, and transform it for analysis. Then you'll conduct a methodological examination of the data. You'll also engage in exploratory data analysis or EDA for short. This will involve converting the data into usable formats, assessing the quality of the data, and then diving into the data to find as many potentially useful insights and directions as possible. You'll then work with your stakeholders to see which of those areas are worth pursuing in more detail, which brings us to the construct stage, where you're going to pursue a limited subset of all those potential insights that looked interesting to you in your EDA. And here is when you will work with other data professionals, potentially statisticians and machine learning engineers to do things like building machine learning models and revising those, uncovering relationships within your data, and doing statistical inference about those relationships. Finally, in the execute stage, you will share the result of your analyses and your collaboration with your stakeholders, as well as the value that you've unlocked from your data. Here you'll present findings to internal and external stakeholders, answering questions and considering different viewpoints. You'll also have an opportunity to present recommendations based on what you found in the data. You may discover that you revisit the planning and analysis stages as you refine models and incorporate feedback. A good way to visualize the PACE framework is as a completed circuit. Each of the four stages must be engaged for it to function correctly. The electricity or flow of energy in the PACE circuit is the communication between you, your team, and all the other stakeholders and collaborators involved. When you look at the stages in this manner, you might think that communication only flows one way. Well, you always do start with planning, but don't be afraid to go back and iterate. With PACE, new information and feedback can be incorporated in any part of the process. You might need to return to Analyze to clarify some aspect of the data, and then jump back to Execute to present this aspect to your stakeholders without the need to construct new models or dashboards. Along the way, you'll see how the PACE framework can be scaled to fit within the scope of any project. The model's adaptability will prepare you for a dynamic profession that requires a high degree of professional flexibility and communication. Regardless of where your career takes you, the PACE framework is a tool that provides a clear foundation and structure. Through the continued application of the PACE framework, you'll prepare for each course's portfolio project. Then you'll have the opportunity to practice your evolving skills. Each portfolio project will introduce opportunities to develop and strengthen your organizational methods. As you develop your own intuitive workflow, the PACE framework can be a great organizational tool. Next, we'll look at how communication is so crucial. When I was a kid, I used to love to take things apart so that I could see how everything came together on the inside to make something work. I actually still love doing that today. And this is so much easier to do with tangible things that you can hold and look at, like a pen, for example. I can twist the tip to pull each section apart. From there, I can pull the ink cartridge out and check out the way that the spring works in tandem with the top part of the pen to make it click. Studying the way things work becomes much harder when we start thinking about abstract concepts like communication. And you're probably wondering why it's important to talk about it in the first place. In your role as a data professional, you are the direct connection between the information inside the data and other project stakeholders. So let's take a closer look at some key considerations when crafting a message. All the communicative exchanges have three key elements that we need to keep in mind. Purpose, receiver, and sender. When we think about purpose, I want you to think about the reason why the communication is taking place. In analytically oriented settings, you might find yourself in situations where technical pockets of information require analysis or reporting. On the other hand, there may be contexts that depend on strategic insights which will be used to direct a company's financial or organizational efforts. The receiver is your audience. When you think about the receivers of your messages, I want you to think about who you are talking to. It's helpful when crafting communications to ask, what does my audience already know? What do they need to know? And it's important to keep in mind that every exchange can cause a rippled chain of events. As a data professional, you're often working as part of a distributed team across an organization. And that's why a message shared with one receiver may be used for reporting with or to others. The sender is the person responsible for crafting that message or communication. Yes, you. You're the sender. The sender is a crucial part of any communicative exchange. As the sender, I encourage you to think about the following. What's your relationship to the receivers? What's your role in this exchange? Are you reporting insights? Are you pitching ideas? Or are you identifying potential data inputs? Also, what personal biases might affect the message you're trying to share? At the heart of the relationship between purpose, receiver and sender is the message or communication that you intend to share, which is impacted by all three elements. For this reason, the same message might be shared in dramatically different ways when the purpose and the receivers change from scenario to scenario. For example, you might know a data professional working on a complex project. They've been involved since the project was sparked by an idea in a meeting. The way they articulate the project pitch, structure, research, models and findings will change depending on whom they're talking to. With non-technical audiences, they're much less likely to focus on details about the code used to program the model and instead they'll focus on the impact of the project. When the audience shifts to other colleagues, your friend may instead choose to be very detailed about the code and project logistics. In both cases, the overall message exchanged, that is, information about the project was the same. How it was crafted, the details included, and the way the information was organized, that's what was different. Now that we've examined the key elements of communication, we can really start to think about some of the best practices that will set you up for success when you communicate in your future job. My name is Molly and I'm an analytics manager on Google Photos. We use data to make the Google Photos app better for our users so that they can reminisce on all their memories of their life. Communication is everything in data analytics. You could say that coding is just communicating with computers and at the end of it, the what the computer gives you, you have to then communicate back to other humans. Most of the time, they don't have a background in analytics or statistics. So you really have to put it in a way that anyone could understand. Being able to communicate not only what to do, how to do it, but then what the results are, I think is as important if not more important than the actual code. I would say communication is first really important when you're working through what your priorities are. So as an analyst, you'll get a lot of requests. Everyone's going to be your best friend and asking you for a lot of things. But it's really important to kind of take a zoom out of the trees and look at the forest and say, what is the most important thing I could be spending my time on? And usually, that's not the request of the person next to you asking for something. So instead of saying yes to everything, which I did when I was early in my career and then just try to burn the midnight oil and do everything in order of just first in, first out, I now say no to a lot of things. It's better to say, sorry, this is not one of my top priorities and I'm not going to be able to get to it in the next couple of weeks. That's better than saying yes and then not being able to get to it in the next couple of weeks. So communication is important. Usually you have to be transparent and if you emulate transparency and trust, you'll generally get that back as well. You want to be transparent in what you're working on and keep your updates coming in frequently. There's very few times I've been annoyed that I got too many updates on something. Most of the time it's the opposite and you're like, what is the status of this? So I haven't heard on this and your stakeholders are thinking that as well. We create these requirements documents for every analytics project and you get signed off and you say, this is what I'm going to do. Here's what it's going to look like. Here's how long it's going to take. It's a little, you know, over communication but being on the same page is really important. There's a lot of data out there and it is only growing. So the amount of people who can translate what's going on from zeroes and ones on a computer into telling a data story into producing really beautiful visualizations into finding problems. You know, at the end of the day humans write code and humans make coding errors. So how do you spot those in a way that a computer couldn't or how do you think about what are the new things that we haven't even touched on that we could work on? There's unlimited amounts of things you can do with data and I think what excites me is 10 years ago nothing that I'm doing today was even possible. So what I'm doing 10 years from now is probably going to be not possible today and the opportunity to get into the data field really means opportunity to keep learning and growing at all times. Have you ever stopped to think about what makes communication successful? Surely you can think of a time when you witnessed a communication failure. I know I can. Communication is one of the most essential components in the exchange of ideas and concepts. It's a large part of everyday life and the foundation of all human interaction. It's also an important element of analytically focused work that isn't discussed as often as technical skills. In this video I'm going to share a few tips for successful communication. My hope is that you'll rely on this advice in your future role as a data professional. Having a clear vision of why you're communicating is the first thing you need to consider. Your why depends on the context set by the business or organization that you work for as we discussed in earlier videos. Your why is also informed by the project's mission statement that is the goals orienting the project. And when crafting any form of communication use your why to guide the main ideas so that your audience can identify how to act or respond with purpose. Time is a currency in the professional world. It's very important to be efficient. As you draft or prepare to communicate I encourage you to use direct language minimize wordiness and avoid unnecessary details. You should always strive for clarity. Use proper grammar and punctuation. Regarding vocabulary keep it simple choosing specific terms and avoiding technical language where possible. Break complex ideas into shorter sentences especially when addressing a diverse audience of professionals from outside data analytics. This will make the points within your communication easier to understand and remember. You may recall from earlier videos that I mentioned the three key elements of communication. These are the sender receiver and purpose. Now I want to add to that list and consider the setting for your communication as well. Are you having a discussion at lunch with a co-worker? Are you creating an email to update all the stakeholders about your progress or presenting the results of your analysis to a boardroom of executives where and how you'll be delivering your message will have a direct impact on the way you shape it. Let's use a simple example to help illustrate effective communication. Like using a hose to water a plant the straighter the hose the better the water flows. Now bend the hose or tie a knot into it and the flow of water is interrupted. In this analogy the water is like the intended message. The hose is whatever method you've selected to deliver your message. Knots in the hose symbolize misunderstandings within the mind of your audience. When the knots are created the free flow of information stops even if you continue to deliver information. As a data professional you may find that a significant aspect of your work involves your ability to communicate with non-technical audiences. Your goal should be to meet your audience where they are. What I mean by that is you want to be efficient in your communication and tailor it to the people you're sharing information with. Depending on how familiar they are with what you're sharing you'll likely want to break down technical concepts into simpler terms. Avoid jargon acronyms and technical buzzwords. When everyone understands the intended message you can avoid the knots and the flow of ideas can continue. Carry these practices through all types of interactions email, messaging and both virtual and in-person communications. As you continue throughout the program consider ways that you would communicate the technical concept you're acquiring to different audiences. Also remain focused on the purpose and setting of your communication. All these factors add up and make a big difference to how effectively you communicate as a data professional. Previously you were introduced to the PACE framework. As a reminder PACE has four stages plan, analyze, construct and execute. We also examined the rationale and key elements involved in effective communication. Now let's investigate how they can be integrated together within data projects. At first glance the PACE framework easily enables a wide view of an entire project's workflow that is each stage from planning to executing is logically sequenced with the beginning middle and end of a data project. As you may recall though PACE is incredibly flexible and it doesn't need to be employed in a linear progression. Often iteration makes sense in projects. So let's unpack that with an example. For reference we can compare it to the construction of a building. First a blueprint is created. This is where the planning happens. Next we lay the foundation. We can do this once we've analyzed the lot space and considered other variables like cost. Then we add the frame. After that the roof can be installed. By the time the structure is ready for move in the builder will have progressed through the entire workflow planning, analyzing, constructing and executing the client's vision. In practice the PACE workflow is meant to serve as a navigational tool. We created it with the goal of helping you understand the data professionals workflow and as an aid you can consult in your future role. Now let's return to the example. With the roof completed that makes it possible to begin the insight construction like installing drywall, electrical components and plumbing systems. Each of these jobs also has its own PACE workflow demonstrated on its own planning document within the larger blueprint each requiring planning, analyzing, constructing and executing. Just like the building example we've been discussing as data projects move into the construct and execute stages on the global level you may need to return to the earlier stages to incorporate additional data or feedback from other stakeholders. Even while the global project is transitioning into a new phase of PACE there can still be upcoming tasks that are just beginning their PACE cycles. Regardless of where you might be in the PACE workflow communication is what drives the framework to the realization of the project. At each stage within the framework there will always be a need for communication to improve the workflow. This could be asking questions about your data gathering additional sources updating stakeholders on progress or presenting findings and receiving feedback. One of the most important considerations behind the development of PACE was providing a flexible structure that allows you to adjust changes within a project. Let's return to our building example. Let's say that during the installation of the electrical system the property owner has communicated that they wish to have an additional charging port for electrical vehicles added to the plan. To facilitate the change you would revisit the PACE framework to plan, analyze, construct and execute this new request. Just like in this example requests from other stakeholders can come in at any time. Regardless of the timing of an additional request or task data professionals need to be available and accessible throughout the entire project cycle. Sometimes you may need to speak at a meeting or participate in a progress update. Additionally you may update your progress within a tracking system email conversations and chat discussions will keep others involved and up to date with where you are in your workflow. I'm excited for you to develop some hands-on experience practicing different communication strategies through each stage of PACE. You'll have opportunities to do this later in the program. For now I want you to remember that a good data professional is a proactive communicator who responds to questions in a timely fashion. Keeping other stakeholders up to date with clear explanations can make you the most effective data professional that you can be. You're nearing the end of your first course and we've discussed some key concepts especially in the areas of communication and data workflow through the PACE framework. As you're preparing for your first portfolio project I'd like to point out a vital consideration when using the PACE framework. Each stage of the workflow is not mutually exclusive. Different roles, teams, projects and workflows may put emphasis on different stages of the PACE framework. So, although you may be working on a task that is primarily focused on for example analyzing data there will still be elements from the other stages of the PACE framework that will affect your work as well. Let's take a look at the way this program applies the PACE framework. We can take a global perspective and say that the earlier courses are heavier on planning and analysis and the later courses are for construction and execution but that's a very wide view. If you take a closer look you'll discover that each course is operating within its own PACE framework. In your Python course you'll be acquiring knowledge to enable you to use the language for data analysis. Because you're acquiring new skills much of the course may be classified in the planning stage of PACE. As you move on to that course's portfolio project you'll shift into analysis but this will have elements of planning, constructing and even execution as you create the final product. Next you'll learn how to prepare data to reveal the stories within. Here you'll lean into the analyze stage. As you become familiar with the foundations of statistics you'll continue analyzing data and you'll construct some new tools for your toolbox that you'll apply in your portfolio project. While you're exploring regression you'll extend your experience in the analysis stage and put it into practice by constructing data models. You'll also practice executing by summarizing the results and insights to provide value to your stakeholders. Advanced modeling will further expand your analysis and construction skills within that particular portfolio project. And even while you're expanding your knowledge of career resources you'll be operating in the planning stage as you collect information the analysis stage while processing information about the job market and the constructing stage while assembling your resume and portfolio. You'll have an opportunity to bring all your newly acquired knowledge and skills together in the capstone project. Here you'll have the chance to use the entire PACE framework through scenarios and data provided by our industry partners. Even though some stages are more prominent than others in each course you'll still see evidence of all the stages throughout. You can look at PACE as the scaffold that surrounds the exterior of a building. With this scaffold in place different tasks within the building can use the same workflow structure without disrupting the entire project. As you can see the PACE framework is an excellent way to help guide you as you gain knowledge and confidence. Understanding how to classify tasks and proactively interact will help you develop good habits and eventually develop your own professional workflow. You've reached the end of the data workflow overview. You've covered a lot of material and concepts. Great job! You saw how the PACE framework can be used to help structure and guide you through projects. Next we learned about effective communication and how it can be used within the PACE framework. We looked at the external factors that can have an impact on data analysis. We also looked at the external factors that can have an impact on data analysis like bias within data. As you're preparing for the weekly challenge don't forget that you can review any of the materials we covered. In the next section you'll begin working on your first portfolio project. I wish you the best of luck. Attending a class, watching an instructional video or reading information are all great ways to gain new knowledge. However, there's nothing like applying that knowledge. When you actually do something this really helps confirm that you understand what you've learned. This concept is called experiential learning which simply means understanding through doing. It involves immersing yourself in a situation where you can practice what you've learned. Further develop your skills and reflect on your education. Experiential learning gives a broader view of the world provides valuable insights into your particular interests and passions and helps build self-confidence. In the context of this program experiential learning will give you the opportunity to discover how organizations use data analysis every day. This type of activity can help you identify the specific types of industries and projects that are most interesting and gain the confidence necessary to discuss them with potential employers. This can really help you stand out during a job search. Soon you'll put experiential learning into practice by working on a portfolio project. A portfolio is a collection of materials that can be shared with potential employers. Portfolios can be stored either on a public or personal website. They can be linked within your digital resume or any online professional presence you may have such as you're linked in. Your portfolio project for this course will involve using the PACE model to set up tasks of a project. Creating a portfolio project is a useful opportunity since companies will often ask you to complete some type of project during the interview process. Employers commonly use this method to assess you as a candidate and gain insight into how you approach common business challenges. Completing this portfolio will prepare you if you encounter the situation when applying for data-focused jobs. Coming up, you'll be introduced to the specifics involving your portfolio project. You'll also receive clear instructions to follow. As you begin working, consider the knowledge and skills you've acquired in this course and how they can be applied to your project. Within each portfolio project, you'll prepare a PACE strategy document. This will help you identify key points within each project to share with a hiring manager such as the many transferable skills you've gained. A transferable skill is a capability or proficiency that can be applied from one job to another. Highlighting your transferable skills is especially important when changing jobs or industries. For instance, if you learned how to solve customer complaints while working as a host at a restaurant, you could emphasize the transferable skill of problem-solving when applying for a job in the data field. Or maybe you learned how to meet deadlines, take notes, and follow instructions while working in administration at a non-profit organization. You could discuss how your organizational skills are transferable to the data analysis field. The point is you've developed the ability to problem-solve or keep things organized in one role. You can apply that knowledge anywhere. There are all kinds of transferable skills that you can add to your resume. Reflecting on your transferable skills and the notes you take in your PACE strategy document will help you consider how to convey technical concepts clearly. This will also help you demonstrate how you would apply your expertise across all kinds of tools and scenarios in the data career space. And by the time you're done, you'll not only have a very useful data analysis process document, but also a comprehensive set of artifacts for your portfolio. Sounds exciting, doesn't it? Let's get going. When I interview people for jobs here at Google, I love checking out their online portfolios. I find that I feel more confident in candidates who can demonstrate their knowledge in a clear and compelling format. Having a portfolio has become incredibly common in the data field. During a job hunt, it's so valuable to showcase your ability to understand business scenarios, communicate effectively, and use tools to solve complex problems. Your portfolio can really help you stand out from other candidates. So far in this course, you gain lots of knowledge and job-ready skills to help you excel. You've discovered the role of data professionals within an organization and typical career paths. You've explored core analysis practices and tools and witnessed how data professionals use them to make a positive impact. All of these things will help you successfully complete your portfolio project. In addition, you will apply what you've learned about team members, stakeholders, and clients such as their particular roles or priorities. You'll begin by reading the specific project you'll be working on. This reading will describe the type of organization you're working with, the people involved, the business problem to be solved, and other key details. This will enable you to further define the project, understand the stakeholders, and consider key questions to answer. In order to achieve a successful result. Then you will create a PACE Strategy document outlining the project's purpose, stakeholders, deliverables, and much more. In this document, you will begin to integrate the PACE model to identify steps at each stage in the project. For each portfolio project, you will continue to use the PACE model to guide you. By completing each PACE Strategy document, you will be well on your way to developing your own data analytic workflow. Then, in later courses, you will continue working on your portfolio project and continue using the PACE model to guide your process.  Plus, you'll have a dynamic example of your data analytics skills, demonstrating your thought process, approach to the problems, the key skills you've gained, and lots more. These are all great things to talk about during an interview. All right, let's get started. It's time to discover how you will help an organization advance through the exciting world of data. You've completed the first portfolio project. Congrats. Planning and documenting your approach to the example situation is a useful experience as you begin thinking about your future job hunt. Soon, you'll be able to impress hiring managers by discussing your data professional experience, including understanding stakeholder requests, establishing a clear and straightforward project plan, and completing an effective strategy document. In addition, you'll understand how to plan out and organize the necessary workflow. This is a big part of a data professionals process. Also, as you've learned, it's helpful to communicate about your transferable skills with potential employers. The information you've added to your pay strategy document will be valuable during job interviews. There will be another opportunity to complete a portfolio project at the end of the next course. At that point, you will use the pay strategy document that you started here to continue developing your skills as a data professional. Then, at the end of the program, you'll bring everything together in order to finalize your unique approach to this example situation. The goal is to have a great example of your work that clearly demonstrates your skills to potential employers. Congratulations again, and I hope you have a really rewarding experience as you continue working on your portfolio project throughout this program. Congratulations on finishing this first course. You've already learned so much, and now you're ready to take your new knowledge and skills and keep moving forward. But remember, if one day you feel like you need a refresher or just some extra practice, these videos, readings, and activities will still be here whenever you need them. Now I'm excited for you to begin working with the instructor for the next course. They're ready to help you take your next step towards finishing this program and continuing your journey as a data professional. This course builds directly on all the interesting topics you've learned so far. It will give you more insight into the Python programming language and how it can be used to enable data analytics. As you progress, you'll continue building your data professional skillset. And by the end of the course, you'll be prepared to take the next step towards your portfolio project. Before you get started though, I'd like to thank you for joining me in this course and choosing to pursue this exciting learning opportunity. I strongly believe that education is a lifelong journey and I've no doubt that all the time and effort you put into experiences like this one will better equip you for anything you choose to pursue. You've come a long way, so take a moment to celebrate everything that you've already accomplished. And then when you're ready, head on over to the next course. Hey there, welcome to the next stage of your learning journey. Congratulations on completing your first course. You've learned how data professionals contribute to the success of an organization and the main tools and techniques they use on the job. Now you'll learn how to use one of the most powerful tools available to data professionals, the Python programming language. Computer programming refers to the process of giving instructions to a computer to perform an action or set of actions. You can use different programming languages to write these instructions. You might choose a specific language based on the project you're working on or the problem you want to solve. The Python programming language is super useful for working with data. Data professionals use Python to analyze data in faster, more efficient, and more powerful ways because it optimizes every phase of the data workflow from exploring, cleaning, and visualizing data to building machine learning models. This course will give you a strong foundation and Python fundamentals and prepare you for more advanced data work in your future career. If this is your first experience with the Python programming language, welcome. This course does not assume you have any prior knowledge of Python. We'll begin from the beginning and work through each concept step by step. Take it one step at a time and go at your own pace. As you develop your Python skills, you'll apply what you learn to gain valuable practice working with data. And if you have experience with Python, that's great too. I'll help you apply your knowledge in a new way and demonstrate how to use Python for data analytics specifically. Let me introduce myself. My name's Adrian. I work as a customer engineer at Google Cloud. This means that I work with our customers to understand the technologies they have at their disposal to solve data analytics needs. I first learned Python when I went to create an electronic journal. I got tired of buying a new physical one every year. I learned how to password protect it and to this day it's still one of my proudest moments of using Python. Throughout your career as a data professional, you'll have the opportunity to continually learn and grow. To me, that's one of the coolest aspects of the job. And learning Python is one of the most rewarding parts of that growth process. I'm still learning new ways to use Python all the time, both at work and for fun. Now let's review what you will learn. We'll start with a general introduction to Python and discuss why it's such a popular programming language among data professionals. You'll learn fundamental coding concepts such as variables and data types and how they help store and organize your data. You'll also get a chance to start writing your own Python code. Next, you'll explore functions or reusable chunks of code that let you perform specific tasks. Functions help you work with data quickly and efficiently. You'll also learn about conditional statements which tell the computer how to make decisions based on your instructions. Then, you'll discover the power of loops which repeat a portion of code until a process is complete. You'll also learn how to work with strings which are sequences of characters such as letters and punctuation marks. After that, you'll explore data structures in Python which are methods of storing and organizing data in a computer. You'll review the most useful structures for data professionals such as lists, sets, dictionaries, and data frames. Finally, you'll apply your Python skills and an end-of-course project that you can add to your professional portfolio. The end-of-course project features a unique dataset based on a workplace scenario. In future job interviews, you can share your project as a demonstration of your skills and impress potential employers. Learning Python will take your data analysis skills to the next level. It'll also be a great addition to your resume. Knowing how to use Python is a key credential for data professionals and will give you a big boost as a job candidate. I'm here to help you every step of the way and remember, you set the pace. Feel free to watch the videos as many times as you like and review topics that are new to you. By the end of this course, you'll know how to use Python to explore and analyze data. Let's get started. Hi there. I can't wait to explore Python with you. During our journey together, you'll learn how to use the Python programming language to power your data analysis. This course will give you a foundation of essential Python skills that you can continue to build on throughout this program and your career. We'll begin with an overview of Python's main features and capabilities and discuss the basics of Python programming. You'll discover how Python can help you work with your data quickly and efficiently. Then we'll consider Jupyter Notebooks, an interactive environment for coding and data work. You'll learn about the useful functionality of Jupyter Notebooks and how to write Python code in the notebook environment. Python is an object-oriented programming language based on objects that contain data and useful code. Next, you'll explore the benefits of object-oriented programming for data professionals and become familiar with its basic concepts. After that, you'll discover variables, one of the building blocks of Python programming. You'll learn how variables help you store and label your data and how to assign specific values to variables. We'll also review the conventions or widely accepted rules for naming variables. Learning these conventions will help you write code that is clear, precise, and consistent. Finally, we'll explore fundamental data types such as integers, floats, and strings. You'll learn how to convert and combine data types to organize your data. When you're ready, join me in the next video. My name's Adrian. I work as a customer engineer at Google Cloud. This means that I work with our customers to understand the technologies they have at their disposal to solve data analytics needs. A couple of non-traditional backgrounds started out in nursing. I spent several years there before I realized that while dealing with patients and helping patients was definitely fulfilling, it wasn't exactly what I wanted to do for the rest of my life. There were a number of transferable skills that I learned from my previous career as a nurse. First, being critical thinking, second, problem-solving, and third, assessment. Within advanced data analytics, critical thinking and problem-solving becomes key when trying to debug. Furthermore, when looking for answers online, you need to be able to assess what information you're being given and understand how to apply it to fix your problem. Another skill that I was able to bring over from nursing is soft skills or interpersonal skills, which are critical in advanced data analytics as you try to work with others in a collaborative space. I got into this career because I had gained skills related to programming just throughout my life. I didn't really understand how I could apply it or the fact that I could actually apply it. When I got an undergraduate degree in English and History and it was after realizing what the job opportunities were for humanities that I had to think again about what my next step would be. That's where technology came into play and I found that I could actually use technology and integrate that with what I was doing from a humanities point of view with English and History and get into digital humanities. That led me to do data analytics through the process of or the concept of knowledge management where in a space where every company is going to become a data company whether you're working in medicine whether you're working in retail whatever it might be data and the ability to manipulate and leverage it is going to be critical. My favorite thing about data analytics is it can be incredibly entry level. Once you understand those basics you can get started on your own. You don't need to have a formalized university degree. You don't need to have years and years of experience. Getting started is something that can be done as long as you put in the work to get the elementary foundation down. Python is a powerful coding language that has become one of the preferred tools of data professionals worldwide and for good reason. In this video we'll explore what Python is and why it has become so popular. But first let's discuss some basic elements of programming languages in general. Programming languages originated with the development of electronic computers. They were and still are the words and symbols that we use to write instructions for computers to follow. Communicating with the computer ultimately relies on computer hardware. A transistor is the most fundamental component of the computer because it controls the flow of electricity through a circuit. A transistor can exist in two states on or off like a switch. When a transistor is on electricity passes through it. When it's off it blocks the electricity. This duality defines how computers operate. If you chain enough transistors together each either on or off you can create complex logic. So how does this relate to computer programming? Well because computers are just billions of transistors or switches they understand things only in binary terms. You may have encountered this concept before binary. Binary is represented as ones and zeros. These numbers are just an easier way to refer to the on and off sequences of transistors when a computer receives instructions from a program. Computers are powerful but they still need to be given instructions and they can only understand instructions that are given in binary. The engineers who first designed computers encountered this and discovered a problem. Computers are great at understanding binary. Humans are not. This problem is what gave rise to the first programming languages. The very first programming languages were difficult to use. Required lots of training and often only worked on the specific machine that each was designed for. These types of languages are known as low level languages. Over time new coding languages developed to simplify and generalize programming instructions. The programming languages became easier to learn because they were designed with simpler rules and structures known as syntax. Modern programming languages use syntax that's much more familiar to humans. These languages are known as high level languages and this brings us back to Python. Python is a high level language that's versatile and easy to learn. Simply put, Python is friendly. In fact, some people might think that the name itself is scary but the creator of Python didn't name it after a giant snake. He named it after a British comedy trope, Monty Python because he wanted to be easy and approachable. In addition to Python being versatile and easy to learn it's also powerful. This combination of qualities has made it a favorite not only of data professionals but of scientists and web developers too. Part of what makes it so powerful is that it's open source and developers have created many libraries and tools to make many jobs requiring Python easier. A library is a reusable collection of code. For instance, you could hand code a function that takes two numbers, add some together and returns the sum but what if now you want to add three numbers or four? You can write a more complex function that lets you input any combination of numbers and it will return the sum but summing is a super common task so you can save yourself a lot of time by just using a math library that contains a sum function. There are thousands of Python libraries that contain code for tasks as simple as summing numbers and as complex as building a neural network for an artificial intelligence application. You'll learn more about libraries soon and you'll learn about neural nets and AI and how they fit into the world of data analytics in a later course. This certificate program focuses on advanced data analytics so you'll learn to use Python as it's most often applied in data analysis work. You'll also learn about NumPy, Pandas, stats models, Matplotlib, Seaborn, Scikit-learn and more. These are code libraries that are used every day by data professionals on the job. You'll explore these in detail later. The ease of learning, ease of use, versatility and power of Python make it one of the most used coding languages today. Because it's so widely used it has a large and active community of users who are willing to help and provide support which makes it a great coding language to discover and explore. As you move through this course and the entire certificate program always remember that coding is simple and complex. In other words each line of code represents a small simple idea but together those lines of code can express very complex logic. Coding can be frustrating at times but also a lot of fun and very rewarding. You will practice coding a lot in this course so you can get better and better. Lastly don't be afraid to make a mess. Experimenting is part of the process and practicing will help you quickly improve your coding skills. Previously you learned that Python is a high-level programming language. This means that Python uses more human friendly syntax and more closely resembles a spoken language. In fact Python is simple enough that you can learn some of its basic concepts just by example. In this video I'll demonstrate some of the fundamentals of Python. I'll introduce you to some new terms but we won't take into account formal definitions and processes. We'll consider these things in more detail later. For now just take a moment to get familiar with the code and how it works. The first thing we'll do is print to the console. If we tell the computer to print Hello World it will output Hello World for us. In fact the print function will output whatever we enter in its parentheses. Of course Python is also capable of performing computations. If we divide the sum of five and four by three we get three. And Python we can also assign variables. You can think of a variable like a container that you can name. A container's contents are known as its value. For example here we create two new variables. The first variable is called country and we're assigning to it the value of Brazil. The second variable is called age. We'll assign it the value of 30. Now we can refer to the values Brazil and 30 by their variable names. When I give the country variable to the print function it returns Brazil. Similarly when I give the age variable to the print function it returns 30. Python can also be used to evaluate statements. For example I can check if 10 to the third is equal to 1000. It is so the computer returns true. Notice that so far I've used the plus sign for addition the forward slash for division and two asterisks to indicate an exponent. Like in mathematics these signs are known as operators. A lot of them are straightforward however some might not be so simple. Take this last case for example I use two equal signs to check the equivalence of two values. When used properly the computer will return either true or false. What happens when I use one equal sign? I get a syntax error. You might have noticed from earlier examples that the single equal sign is reserved for assigning variables. Just like in spoken languages Python has rules that govern its construction. You'll learn many of these rules in this course but I won't discuss them in detail now. You won't easily learn a new spoken language by studying a big book of its rules. You're more likely to learn it by speaking it hearing it reading it and writing it. It's also a lot more fun that way. It's the same with Python so for now don't worry about memorizing any rules. First let's observe Python in action. As you might expect if I make a false equivalency like 10 times 3 equals 40 the computer will return false. I can even use a variable I defined previously, age in a new statement. 10 times 3 does equal age. You'll recall we assigned age a value of 30 so the computer returns true. Python also lets me perform actions based on conditional logic. Here I tell the computer that if the value of my age variable is greater than or equal to 18 then it should print the word adult. Otherwise it should print the word minor. The value stored in the age variable was 30 so the computer returns adult. Another common task in Python is looping. Looping performs the same action to each element of something. Here's a simple loop. For each number in this list of 1 through 5 we print the number. The computer outputs 1, 2, 3, 4, 5. Here's another example. This time I'll create a list containing the numbers 3, 6, and 9 and assign it to a variable named myList. Now I'll loop over my list and for each number in the list the computer will print that number divided