Hello, everyone. Thanks again for joining us after lunch. So we're going to be a very important task of making sure no one falls asleep. My name is Monica Ugui. I'm responsible for the product and design teams at Radar Stack, which is a customer data platform. So let's start by introducing ourselves when we start from you, Vijay. Sure. My name is Vijay Amapathy and I'm a Director of Product at Heap. We're a digital insights product, and we help you understand the customer journey and make it better. My name is Nils Boat. I head up growth and automation solutions at trade.io. For those of you who don't know, trade.io is a low-code integration and automation platform. We know that integration is really important to product teams, and that unfortunately the overhead of delivering on your integration roadmap is often quite high, and so our goal is to help you deliver on your integration roadmap much faster. I'm Evan Michener. I'm really excited to be in the same room with a whole bunch of product people too. This is great. I'm the head of product at Full Story where we do digital experience intelligence. So we're combining the quantitative with the qualitative to help teams make decisions faster. All right. OK, so we're going to be talking about digital transformations, but this is a really complex concept. So in the interest of 30 minutes, let's narrow it down to talking about how data plays a role in digital transformations. So hopefully that little curveball does not cause any chaos for us. And so let's start with the first question. What role have you seen data playing in helping companies along the data majority journey? Yeah, I can start. So I think I can first of all explain a little bit about how we think about data maturity at heap. So it's actually not just about tools or about the data itself. It's about tools, people and processes, right? And so, for example, at one end of the spectrum, you have the organizations ruled by the hippo, right? This is the highest paid person's opinion is making all your decisions. You as a PM are kind of getting yanked around, right? It's no fun for anyone. On the other end of the spectrum, or the promised land, right, is a world where you've built this machine that generates reliable improvement in your business. That's increased revenue, reduced costs. And that comes as a result of understanding and quantifying opportunities up front, making a portfolio of investments and then having some portion of that portfolio pan out, right? And people are all over the spectrum in between these two states. And so in terms of where data can help, I think step one is understand customer behavior. That's the backbone in my opinion is what are your customers doing? What are they not doing and where are they getting stuck? What do you guys think? Yeah, that makes sense to me. I think there's maybe another sort of way we could think about framing it, particularly for this audience. Like you guys are the face of digital transformation for many of the companies that are your customers. And in some sense, data is the love language of digital transformation, right? Like it's the common thing that binds, right? It's both sort of the blessing and the product of it, but also like a huge challenge, right? So if you think about some of the organizations where digital transformation is like a major thing for them, healthcare, highly regulated industries, like this is very scary to them, right? And they need your vision and your guidance, like you're doing things in a fundamentally different way than they are. And they're trying to figure out how to tackle it and your experience and how to think differently about democratizing your data about open platforms and open systems and prioritizing that for them is extremely important. And just being pretty sensitive to the fact that they're very concerned about the health of their data. We all are as well, but it's particularly important for them. Well, I'll build on that a little bit.  Great, glad we're talking about this. I think, I mean, when we talk about digital transformation, I completely agree with both of you. I mean, you're talking about the willingness to kind of challenge how you're creating value today, which is applicable whether your company's 100 years old or in healthcare or something else, or you're literally in a day old startup. So it's constantly trying to leverage the latest technology and data is central and core to that. And then making that data accessible to different teams in different ways. So let's get rolling. Excellent, thank you. So first question is, what best practices or what advice do you have for product managers to be at the forefront of chairing their teams to accelerate their path again on the data maturity journey or to adopt a transformative digital setup? Cool, so I got a whole bunch. I'll preface this with that. And if you want to talk about like 50 other ones, come find me at the boots afterwards. But I'll tell you about one that I'm super excited about that we've been using a lot recently, which we call the work backwards plan. And so essentially what this is, is you can think of it as a lightweight financial planner for your quarter and for hitting your OKRs. And so by the way, important thing to think about too, for all of you is if you're kind of living in a world where your OKRs are like ship this feature or do this thing that's like kind of zero or one, they're like push the code or I didn't push the code and it doesn't have a tie to impact, then at some point you really want to start revisiting those goals and trying to orient them as clearly as you can to business impact and ideally revenue I would say. And so let's say for example, you do have a goal that's oriented to business impact like revenue. The whole idea with the work backwards plan is that you're not in this intermediate state where your quarter consists of one bet that is trying to drive that impact. And by the way, it's good to make progress to be in that state, but you want to get to a place where you have like three or five of these bets. And if any two or three of them pan out, then you are going to reliably hit your goals, right? And so what a work backwards plan does is you look at all the different things that you want to change. You look at what the impacted population is, right? What's the reach of those changes? And then you start to rank things based on, this starts to actually, this is skewed heap towards orienting ourselves towards smaller changes that are further up in the funnel and that are reaching far more users. And it's allowing us to achieve smaller bits of incremental improvement far more reliably than before we were using this tool. So that's why it's kind of my favorite pet tool of the moment, but we can talk about lots more later. So I think the question was what advice might we have for product teams who are thinking about and helping companies? Best practices. Best practices, please. Okay, so I evaluate a lot of tools and I actually often start in developer docs. So I'm a huge, huge, huge advocate for highly open platforms, right? So you better give me all the web hooks and all the rest APIs. I wanna be able to do everything that I can do inside your application through an API and I want really good documentation, not gated, highly available, highly open because honestly, there are just so many people on the front lines of different types of businesses and there's different types of ways that those businesses are trying to work and make your product work with inside their organization and it takes a very open mindset and you don't know how they're gonna do it. So you better be pretty open and both sort of technically speaking and documentation-wise on how to actually interface with that open platform that you're creating. Well, I'll add one other flavor on top of both of those and it's you need somebody to champion data. It might be you, whether you like it or not but you need somebody in your organization to champion exactly what we're talking about here which is getting that data open, accessible, making sure people trust it, know how to get to it so that teams can move faster and one of the things that we try to do at Full Stories make that as easy as possible for you. So since we have a room of product folks here, the last time that you shipped a feature or capability and then the next day you realized, oh, I forgot to tag it and all of a sudden you're missing analytics and missing measurements and CEOs asking what the numbers are and you don't know that's one of the things we try to do it at Full Stories, help the data champion out. We use a technology called auto capture which means you don't need to explicitly tag and instrument all of these things. So it's one less thing that data champion needs to work for but that's, I guess just one practical bit of advice is you need somebody internally to help champion the data. That's such an important point. I actually want to drill in for the final statue, Evan. The concept of having a champion is crucial to having broad scale change happen on teams. So now very specifically to product managers, how do you, as a product manager, how do you influence your entire team to rally around a goal? Let's say you're trying to up level your data platform. Well, I'll take a stab here. It's not unlike what we do every day which is building product piece by piece by piece and you're kind of decreasing risk and increasing confidence as you go. So I guess my encouragement is just look at it like you're building a product if your product is building trust in the data. Great. So I might take a slightly different take on it, right? So I think there's a bit of a skills gap out there. So we've all gotten to this point where data is highly portable, right? So there's this sort of great abstraction happening, right? Of course, we're data platforms where they're sort of acting as a hub to be able to facilitate and move it around and all these sorts of things, right? It's far more accessible than it is today, but if you get like on the front lines of an organization, what you'll find is that there's still a lot of walled garden mentality where there's people who really see the promise of what's possible, particularly again in some of these legacy organizations where they're just dealing with all kinds of challenges and using their data. So there's a lot of people who see the promise but we haven't prioritized the skill sets on how to actually work with it. Like how do I work with an API? How do I write some basic SQL? And that skills gap is applicable both to these big organizations trying to do digital transformation, but it's applicable honestly in a lot of product organizations like product operations folks, there's a huge, huge opportunity for them to become more literate on using data and take advantage of some of these new, far more accessible platforms on how to get it and move it around and work straight process. And if we prioritize those skill sets, like you're gonna find that innovation and the digital transformation is gonna snowball. So I think we need to start prioritizing, teaching more people the skill sets required to take advantage of it. Yeah. And I think, I'd say a couple of things that I'd add to those, right? One is, I think it's really important to find a good foothold in terms of a pain point. It's really, really easy with data to boil the ocean and try and solve all of your company's data problems at once and I've never seen anyone do it successfully. And so I recommend that you figure out, what is a dire problem that you wanna solve? And I'd say also, don't just think about the product team. Think about the constellation of teams that surround product, right? So you have customer success, you have support, you have growth marketing, right? All these teams are very adjacent to product management and oftentimes when you're setting up the set of tools and processes to up level, you're actually benefiting more than one persona, more than one group here. So think about both of those when you're figuring out your foothold of how to start delivering impact and you'd be surprised how quickly you can scale up from there. Such great advice. I really like an underlying thread here, which is when we think about data, we're getting so much better at extracting tons and tons of data. There are all these companies that are building amazing products to process. And so it seems very logical that we should use data to make decisions, at least to inform decisions. But I have noticed as a product manager, it's really important to remember that you're trying to drive change, you're trying to change the behavior of your team. And so even though you're talking about something very logical, it's still important to understand you're dealing with human beings on your team that may not be familiar with all the new systems or all the new ways to gather data. And so make sure your EQ ears are perked and you're understanding all of the stakeholders, all of the different parties on your team that might be having resistance. Get to the fear or the source of concern and make sure you address that and don't assume that it's gonna be in no brainer to push forward like your strategy. But amazing, that leads me to our third question, which is what's your advice for combining qualitative and quantitative signals in decision-making? Second, I can start to take this one. I think that, so first of all, I would say that qualitative and quantitative data are extremely complementary, but they have different strengths. So I think quantitative data is much better at helping you find out where you wanna focus, right? The customer journey can be very broad. Your customer segments can be very varied and it is really easy if you start with just individual anecdotes to find yourself in a situation where you're fixing something that was frustrating one person but didn't actually impact your business as a whole. And if you rinse and repeat and scale that out, you get largely nothing. And so it's really important, in my opinion, to start with using quantitative data and quantitative tools to help you figure out where you wanna focus. And then qualitative tools really shine, in my opinion, at helping give you ideas for what you actually wanna do about it, right? What experiments do you wanna run? What do you wanna change? It's so much easier to step into the user's shoes and actually experience what they saw to help you get to that next step of, okay, here's the experiment I wanna run. And so they're very complimentary in that way. And full stories kind of started on the other side of that, more on the qualitative. So starting with session replay where you're observing what the user's doing which is kind of the ultimate empathy generator, by the way, kind of like you mentioned, PJ. So, and then we've moved into the quantitative as well over the past couple of years. The power of that is that you can get in there and see, oh, here's a problem or here's a bug or here's a friction point somewhere where the user's got it frustrated or you have an opportunity to improve that. And then immediately say, what's the scope? Like what's the impact of this thing? So you're hopping directly into the quantitative view of that qualitative piece.  And huge full story fan, by the way, we're customers and I think you're 1,000% correct. Like there's really interesting platforms now. Full story is a great example where you're layering both qualitative, right? You have a visual sense of what somebody's actually doing with quantitative and how fast is the screen reacting and where the error point's happening. Gong's another great example of this. Gong is like my favorite qualitative tool ever, right? You can literally get in there and hear in your customer's words exactly what they're saying when they're first coming into the funnel versus what's happening when their account manager is talking to them a year later. And so I tend to be of the mind that qualitative is where I tend to start and then I use quantitative to basically validate the qualitative insight. And so if I'm hearing a pattern that keeps coming up on a Gong call or seeing something that keeps coming up in full story, then I can develop that into a hypothesis and then go find the data and say like, okay, edge case or common, right? And it truly is those two things converging that makes for good insight.  And Gong's a great example too on whether it's customer support or on the sales side, being able to talk to a user and then immediately tie that back to what they were just doing on your mobile apps or website. That's like the whole world opens up because all of a sudden you're actually seeing what the user's talking about instead of trying to play that translation game. So yeah. Yeah, actually I really like, I like Gong. I think one thing I would also mention too is I think Surveys and Voice of Customer is a really underrated channel. I think a lot of times often people will jump to, you know, they'll jump to like starting with customer interviews, which I think is great. It's the accessible thing, especially when you have a sales team, it doesn't involve learning any kind of new tool because you're just getting on a call face to face. But it is really time consuming. I think I'm a big fan of if you have access to some level of scale, I think Surveys are really powerful in terms of speed, low effort, low time commitment. If you need a decision on a relatively rapid time scale. And I think also just to speak a little bit more to the idea of speed, I think an important thing, especially when you think about qualitative data is the rituals that you have in place, like the weekly watch session meetings and things like that. It's really important to think about how much time you're spending preparing for those moments of insights versus the time that you're spending experiencing them. It's crucial to find yourself in a situation where you're not spending three hours of time to prepare for like 10 seconds of value. And so that's where I think a lot of times the merging of that quantitative and qualitative is really important because the quantitative can help reduce that search time. So you can get to those moments of value a lot faster. I like to point on the rituals, like key in Surveys as well, right? Like distribute that information very publicly. Like I think in some of the organizations I've been in, like there's like one group who's responsible for like combing through the survey data and then like distilling that into insight. And again, that's that walled garden kind of mentality to that qualitative data, like put it in a Slack channel. Like literally every time that somebody fills out that survey, it needs to be in a public Slack channel. And every time like this keyword shows up in Gong, like link it straight in that same public Slack channel so people can watch it, right?  A lot of people inside an organization don't have direct customer insight. And so distributing that out to them in a very public manner is a ritual that in cultural precedent that I think like you should all be thinking about. I love this idea of speed too because I mean, looping back to the original question around digital transformation, the intersection of data, speed is a metric of success that you can use internally. Like how quickly can you act on an insight, quantitative or qualitative learning? And that's, I mean, that's the product loop is learn and act, learn and act, learn and act. So speed might be one of those metrics of success as you look internally to is the data out there do people know how to get to it? Is it accessible? Is it trustworthy? Do people, are they gonna spend the next 10 minutes arguing about whether it's the right data or is it accurate? So speed, crucial measure of success, yeah. Amazing. Well, we're right about time. So I'm going to ask you one last question.  What advice do you have for aspiring product managers? Even regardless of data specifically? Yeah. I think the number one thing I would say is try to spend some time thinking like your CEO and thinking like your investors. And what that means is don't just think about the domain or the feature or the page or whatever that you start out in your organization owning because that's gonna change over time. All of that's gonna feel like a blip on the radar in the rear view mirror like as the years go by and your scope of ownership is going to increase. But if from day one you understand how does your company generate revenue? How does it tick? How do your digital experiences feed into that? How does my page in this workflow and this part of the customer journey drive impact for my business and you view your job and you operate with that lens?  So that's my advice to you is zoom out and think like your CEO and your investors. So I've had the privilege of meeting a few of you especially on the aspiring side of this since one of my favorite conversations to have. So I guess I would, I love that P.J. too. I would encourage you to keep the curiosity alive. So much of product work is like the core DNA is curiosity. Are you staying curious about the customer about your product especially if it's a product that you happen to use every day? How do you stay curious about that? So you don't overlook the speed bumps that your users are feeling. And for the aspiring PMs here, all product work is combining the business outcome with the customer need. And you can do that, no matter what role or title you have today, you can find ways to do that.  It's super easy to get that tunnel vision on shipping, shipping, shipping and projects and lose the greater focus on the customer, which is what this whole thing is about anyway. So my advice would be stay curious. Yeah, I'd agree with that. So I don't know if it came through. I'm not a product manager by trade, but I'm quite the curious individual, right? I like to play an experiment with different technologies. And I've always approached my career as I'm not gonna really ask for a lot of permission. Like I'm just gonna go for it. And I think that mentality in your career goes a long way. Like there's a lot of people I see who come in young in their career sort of looking for permission. I like to kind of like, you know, try it first and not necessarily, I'm not saying like don't communicate or anything like that, but just be bold, right? Like don't always worry about asking for permission first. Like just kind of go grab it. Those are the types of people I think that really thrive and grow inside organizations, right? They just kind of have this entrepreneurial go get it mindset. And people notice it, like managers notice it. You get more responsibility, more impacts on your organization.  You're doing it, you're making it happen. And you learn by doing. And so just go get it. Amazing. I think we must end on that, go get it. Thank you very much. Thank you. Thank you.


way yet and season sailors were excited to take them out and take them under our wing and show them how we conduct business up the sea.


Welcome back Boilermakers. I hope your holiday was as pleasant and safe as you enabled the campus to be last fall. I have to start by thanking yet again our faculty and staff for heroic work teaching and operating the campus under the toughest circumstances ever and you students for your indispensable adherence to the Purdue pledge. A special word of welcome to those hundreds of international students who were prevented by travel restrictions from being with us last fall. It's great to have you back. I wish I could use this message just for celebration and congratulation but we all know a new year doesn't mean we've left old problems behind. In fact for all we can tell this semester could be as tougher tougher than the last one. It's an Indiana winner so we'll all be inside somewhat more. There's a risk that despite the pretesting you've all helped with holiday activities will have created another surge of the virus and new variants of the COVID virus have as biology teaches us they inevitably would begun to show up. Our medical experts advise that so far these mutated strains are no more dangerous than the version we've been dealing with. But we'll be rigorously vigilant and we'll ramp up the intensity of our in semester testing. Please cooperate whenever you're asked to do so. It goes without saying that all the practices that got us through the fall masking distancing hand washing immediate reporting of symptoms will be every bit as important this spring. As I've said so often I can't wait till we can lift these impositions and the new vaccines may bring that day nearer. But for your safety and that of others please show the world again how disciplined and considerate of others boiler makers can be. 2020 was a lousy year in more than one way. Many Americans engaged in embarrassing and unacceptable conduct unbecoming of a self governing people and the very worst such actions came just 10 days ago in our nation's capital. This week therefore marks a new chapter not just on our campus but for the entire country. We congratulate the new president on a campaign fairly and indisputably won and wish him and his entire administration all the best in pursuing the public interest as they perceive it. And we hope that in this new year all our new leaders will lead the nation in leaving behind the acrimony and divisiveness of the miserable year just ended and charting the course of magnanimity and reconciliation and true tolerance. That of course is out of our hands but what is within our control is the chance to conduct ourselves here in this community with civility and mutual support and genuine affection for one another that celebrates our diversity in all its forms. We'll start with a program optional but open to all that examines the history and meaning of Inauguration Day and the orderly peaceful transfer of power represents. That was the example a newborn United States gave to a surprised world in 1796 and which remains the hallmark of any truly free democracy. I hope you'll take part. For now, welcome back Boiler Up.


Okay, thanks for coming. Yeah, so this is really just an info session about a new course. When you build a new course, you have to first figure out what's in the course, and then you have to tell everybody. So now is my chance to tell everybody. We've actually completed a pilot of this course this semester with about 100 people in it, and the instructors are really excited about it. The students seem to enjoy it, so now we're scaling it up and thought we'd tell you what's going on in case you decide you want to try it out. So, despite this rather formal setting, I think you could just interrupt me at any time if you have questions. The first thing I'm gonna do is talk about some kind of course logistics. What is this course? Who is it meant for? Who should take it? When you should take it, that kind of stuff. And then I'll tell you a little bit about the course content, and it has an interesting structure, and that it's not just one course, but many. So we have some of the instructors from the other parts of the course here as well, and you can ask them questions too. Okay, so here's the structure. It's quite unusual, but it's working wonderfully.  So the core course, which we call Data 8, is a four-unit course. Everybody kind of takes the same thing. It's a lecture-based course called Foundations of Data Science. We gave it a different numbering this semester, so if you're trying to track its history, it's very confusing. It's currently called Stat or CS 94. 94 is Berkeley's secret code for course, which does not yet have a number. So now it does have a number, which is exciting, because we're through the full approval process for this course to persist for a semester after semester. So eight is the permanent number, but it is a joint offering between multiple departments. So this is offered in statistics, CS, and information, and it doesn't matter which course you enroll for. If you're enrolling in the same course, it's just cross-listed to demonstrate that we're really all working on this together. And there is one primary instructor. I'll give most of the lectures, but the curriculum was developed by a whole team, and occasionally members of that team will come in and give guest lectures, and it's really a fantastic group of folks who are involved. So that should be pretty fun. So the lectures are held 10 a.m., Monday, Wednesday, Friday, and 1.55 to an L. There's also a two-hour lab section, which is kind of a melding of discussion where one of the GSIs will tell you her or his take on what's going on in the class, and then also some practical period where you're sitting in front of the computer with a partner and trying to solve problems or explore some data sets. So that's why it's called a lab. And right now, enrollment is restricted to freshmen and sophomores, but at the end of phase two, it'll open up. And the enrollment cap, we set very high so that if people want to take this course they can, currently the maximum enrollment is 500. So I think there's a place for you, even if you're currently on the wait list, because of enrollment restrictions, okay? So that's four units, take that. You can take it without any prior experience. This course was designed for people who don't already know statistics, don't already know computer science, and is something that's meant to fit into the lives of all Berkeley students. So that not only means it has no prerequisites, but also that it's not designed to consume your entire life like some other courses are, including one that I have taught many times in the past. So, yeah, so it's a course that's designed to fit into a schedule where people are studying other things at the same time. Because we really feel like this is a foundation that everybody needs. Now, if you are focused on statistics or computer science, it's still a good class for you, but it's designed for the kind of core subset of those disciplines that are important to everybody. Okay, so I said that's a course. There's also what are called connector courses. Each one is two units, and they're being offered in different departments from around campus, each with an individual faculty member running the course, or sometimes teams. They're designed to be taken concurrently with data eight. And most of them meet just once a week for two hours in kind of a lecture slash lab meeting where you learn some stuff and then you try some stuff. That's kind of the best way to learn. And all those sessions are led by faculty. And typically have restricted enrollment of 25 to 30 students. So this is an excellent chance for you to get kind of direct exposure to working with someone from the Berkeley faculty in a small group setting. So you don't have to sign up to it for a connector, but you're crazy not to. You should take data eight, then you should also take the connector that looks most interesting to you. We have 10 of them lined up for the spring, and some of the faculty are here today. So we'll give you a preview of a couple of them just so you know what they're like, but then you can ask questions about other ones and maybe the faculty are here. Okay, I'll pause for a moment. Any questions so far? Yes. Can you just take the connector? So all the connectors list as a pre or co-requisite the core, but anything could be overridden by the consent of the instructor. So you would have to talk to the instructors individually about whether that really makes sense or not. It's true that all the connectors kind of rely on you having learned stuff from the core in order to understand what's going on in the connector so they can go through really interesting stuff even though they only meet once a week. The core course won't change much either from this current fall semester where it's being taught now through the spring and next fall, but the connector courses do rotate. So some of them are the same and some of them are different. For some of these connector courses this might be the only time you ever get a chance to take them, but so far we've seen great retention and that some of the faculty who are teaching connectors this semester decided to stick around and teach them again because they had a good time and some of them have even grown enrollment a little bit. So that's why we have more connectors now than we did in the fall. So I'm hopeful that they'll stay, but we don't make any guarantees about that. Yeah, so once you've taken the core you can take connectors either concurrently or afterwards. We haven't gotten to the point where we're putting restrictions on how many connectors you can take. That's plausible that that would happen later on, but if you get in early maybe you'll work it out. So yeah, that's true, right? Yeah, so it's possible you could take another connector later. Great. So a question that comes up a lot when I talk to students about these courses should I take it in addition to or instead of other courses and what is it gonna teach me and what is it gonna teach me that I already know and what is it gonna teach me that's new? So let me just try to walk through a little bit of that and see if you have questions about that by comparing data eight to some existing courses and seeing what makes sense to take at the same time or before or afterwards. So this is an introductory course. It was designed for freshmen, but if you've never worked on this kind of stuff before then you're coming in fresh as well and it's a combination of introductory computer science and introductory statistics plus a bunch of other stuff including information visualization and a lot of social implications of what this data mean to the world around us and so you can think of it as kind of doing both of these things at the same time. If you look through the curriculum for data eight versus the curriculum for the introductory course, that too, data eight really covers what's in stat two except for it does it in a different way. So it uses a set of techniques that assume you have a computer instead of assuming you don't. It turns out you have a computer. So one of the motivations for building this course was that the statistics department, they were excited about what they were teaching but they knew that they were teaching techniques that weren't kind of the state of the art that they could be teaching assuming that students could program at the same time. But we're teaching you to program in order to learn some of those concepts with a new lens about computation. That means different techniques that are more widely applicable. They also happen to be easier to understand and you let the computer do the work for you, your life is good, you can focus on the real problem. Relative to the course CS61A, which I typically teach, I'd say that about a quarter of the content from that course is in this course. It's a really important subset of 61A. That's kind of exactly what you'd need in order to focus on data processing, data manipulation, data visualization, kind of applications. And it's a lot about programming for the sake of getting something done, learning about the world, instead of programming for the sake of programming, which is really what 61A is about. So they're not the same course. But it's a focus subset where you certainly learn to program in data eighth. You don't learn as many techniques but the ones you learn, you get to keep exercising over and over again on different applications, which means you really build up some great core skills. But there's new stuff. So it's not just like stat two plus 61A and we glued them together and called it a day. No, we kind of talked to a lot of people from around campus about what was important to their disciplines and what everybody should know. And we came up with it. There should be a lot of data visualization. There should be simulation where you can kind of see what happens if you try different processes and then we should explore some of the social implications around privacy, around art, this kind of stuff. Okay, questions. Now, people. No questions. Okay. Okay, so this is Ani. She typically teaches stat two and I'll say loudly what she just said quietly to me, which is that yes, data eight covers what's in stat two, but data eight covers a ton more stuff. Is that accurate? Yeah, okay. So you just learn more. That's not bad. Okay, so what about paths through life?  I would suggest taking data eight if you're thinking about taking stat two. If you're thinking about taking stat 20, there's a way to learn everything that's in stat 20, but it's not all in data eight. That was too much of a constraint and made us have to leave out some things we wanted to keep in. So Ani has built a connector called stat 88. She'll tell you about it in a few minutes. And that is something that you can take in connection with data eight instead of stat 20. Now what about if you want to learn some computer science? Well, I think everyone should learn some computer science. It's up to you how much, but a little bit is a good idea. We have a great course for non-majors called CS10 and I recommend that if what you really want to learn about is computer science or you could think about taking data eight if what you really want to learn about is data science, you know how to manipulate data. There's nothing preventing you from taking both of these courses, but I just wanted to give you some place to start if you're considering both courses and you're not sure which. What about 61A, which is a computer science course that's an introduction for majors? Well, I think data eight makes sense as an alternative to that for students who want to program for the sake of understanding the world instead of programming for the sake of building programs, which is kind of what 61A is about. So if you think of yourself as someone who's trying to learn to program because you want to do stuff, understand the world, run experiments, gather information, manipulate that information, then data eight is probably the right core material for you there. Or you could take data eight before or concurrently with CS 61A. I think that makes a lot of sense. We have students in both courses right now and they seem to think that they're complimentary without being bored out of their minds because there's enough difference. So you could try that. I think taking it before makes more sense than taking it at the same time is because there's a lot of stuff all at the same time. And it's a great way to kind of build up experience before you take CS 61A. So CS 61A is something a lot of students take these days but people who come in with no prior computing experience really have to work hard. And now you can get your prior computing experience and then you might be able to enjoy it a little bit more. Or you could take data eight after CS 61A. There's nothing wrong with that. If you want to apply your computing knowledge that you've gained in 61A to some new domains, learn about how data processing works using some of the tools you've already acquired and I think there are good synergies there. Or you could just ignore all this stuff and take a data aid and a connector that relates to whatever you're already planning. So when we designed this course we were focused on everybody. Like what is it that is important to data manipulation and processing and analysis that everyone from every discipline on this campus probably needs to know. So that was the motivation in the first place and we've worked out a way for it to mesh well with all the existing statistics and computer science courses. But it is not the case that you should ignore this course if you weren't planning to study statistics or computer science already. Instead, this is a great introduction for people who aren't planning to take a lot of stats or CS. They just wanna learn what they really need to know to make progress. Now questions. Yes. Are there follow up courses? Yes. Courses take a while to design and a while is like they have to, people have to think about it for a semester and then teach it. So there's no way that in the spring there'll be a follow on course. That's plausible by next fall that we'll have a course in place. Certainly if you're early in your Cal career and you're hoping to take more data science courses we would very much like to have those ready for you and they're meant to be kind of fairly direct continuations from data aid. But there's a lot of work before that happens. Yeah, so that's a good question. What's the relationship between data science and data structures? So CS61B which teaches data structures is about computing techniques in order to solve an array of computing problems that you encounter when you're building large programs. Most of data science is focused on building isolated smaller programs where the goal is not to build an application that a million people use, but instead to gather some insight about the world. And so it typically doesn't require the same kind of data structures knowledge that you need in order to build a search engine or something like that. Instead what you need to learn to do is figure out what questions you're supposed to be asking, how you answer them, what kind of simulations will give you a useful answer. And so it's basically completely orthogonal information. The only relationship is they both have the word data. Just highly confusing. Yes. You should attend a lab you signed up for. Yeah, let's minimize the chaos for the first time around. But you can always send me an email if you have a real constraint. Yep, in the back. Oh, good question.  And you probably won't enjoy it. You might. You can come in and shop it for a couple of weeks, but it wasn't designed for CS majors who already took some stats and now they want to see how they combine. You can already figure out how they combine. This is a more efficient path to reaching the level of understanding that might have required several courses for other people. I would strongly recommend this class for someone who has no stats experience and virtually no computer science experience. That's the whole idea. You're starting from scratch. We're building support structures just for you and we're trying to pace it in such a way that it's comfortable. I mean, it won't be boring. It won't be easy, but it's not meant to really push people and kind of make them feel like they can't do it or something like that. Instead, we're trying to build it up and scaffold it in such a way where it's the right way to learn from scratch, statistics, and computer science at the same time. Yep, great question. So can you take data aid instead of stat two and have a fulfill the requirements for your major? Depends when you're graduating. If you're graduating this semester, I don't know. If you're graduating anytime far in the future, I'm quite confident but not certain that this will be the case because the statistics department has already recommended that this be so and it just takes a while for this information to propagate throughout the world. So it's an awfully safe bet, but I can't guarantee you that it will all work out. Does anyone else with more knowledge want to say anything on that topic? The question was does data aid replace stat two as a major requirement? And the answer is it depends on the major. I'm going to show you a demo in two seconds about how we've investigated that issue. Yeah, oh, if you already know a lot of CS and you already know a lot of stats, there's upper division courses. For instance, there's a data science course called 194 and CS that you could take that kind of expects you already know how to program. It might be a better fit for you. So the question is if you're a CS major, what benefits do you get from taking this course? Most people who study computer science are interested in lots of things, myself included. Whenever you start running experiments in computer science research or whenever you try to start applying computer science ideas in cross-disciplinary ways, you find yourself running experiments looking at data, trying to understand patterns in those data. And so it's kind of immediately applicable there. It's also the earliest introduction to machine learning that you can really get. Because normally in the computer science curriculum you have to wait for quite a while to take a machine learning course. Well, here you can take it very early on if you want to see kind of what it means to make predictions automatically. In the back. There are two exams in the lecture course. There's a midterm and a final. The midterm is in class. The final is a final. And the connector courses are free to examine you however much they want. But in practice, they haven't done a whole lot of examining, I think. But they've done some. Just the right amount. Always. Yeah, I mean, this is kind of a standard formula course where you do take exams. You do have weekly homeworks. There are a couple of projects. And so it will take a fair amount of your time. But I guess there are some courses on these campus that take more time than would be justified by the number of units that they're allocated. And those are often in the computer science department. This is not one of those. This is one of those that really is meant to be scoped so that it fits into your life as you're taking other courses at the same time. Yep. So the question is should you take step 133 and data eight at the same time? And the answer is? Probably not. Probably you should just take data eight and then if you want more content, you can take step 133. So the question is how does this relate to research methods in psychology? There are close connections there. Oftentimes when you're performing a psychological experiment, you collect some information based on trials and then you wanna try to make some conclusions about how the whole population of people work based on only the ones that you studied. And that involves coming up with hypotheses and then testing those hypotheses statistically. That's a major part of the course. It also involves building kind of confidence intervals about what you think is true about the population given what you've observed. That's true about this course as well. So kind of the core techniques that get used in psychology appear here as well. We do them in a slightly different way than most psychologists because like statistics, most of psychology was built up assuming you didn't have a computer because it's an older discipline than computer science. But we'll make those relationships quite explicit so that you know when we're doing something a little bit different than what a psychologist might do and why it's really giving you the same answer even though it's this different technique. Okay. So what do we teach in this course? Well, I kind of told you computer science plus statistics plus visualization plus social implications. What are you supposed to be able to do at the end of the day? I mean, I can show you the syllabus and I can read it but it's pretty long and it's in a small font but there it is. If you wanna try to engage with that, you should ask questions instead of just seeing all these buzzwords and getting scared off because in fact, we're trying to make this understandable instead of not understandable. So instead of reading you the syllabus, I thought I'd just show you the kinds of things that you're supposed to be able to do after you take a class like this. And they're about going and finding information and then figuring out what it says. I had to do this recently because we were trying to figure out how many different departments we'd have to talk to in order to get this course to count first, Stat 2. And so we had to figure out what were all the departments out there, what degrees they conferred and how many degrees they conferred each. Well, the nice thing is that Berkeley provides a portal for all of the people in the Berkeley community to download data about what degrees are conferred. So I did that already and I'll read that in. Oh, it's too small, isn't it? Not too big, isn't it? Try that. So degrees is some table that says in a particular calendar year, some major issued this many bachelor degrees. So there were 17 degrees in African-American studies in the 2010, 2011 academic year. It's easy to find tables like this about anything that you want. The question is whether you know what to do with such a table when you encounter it. So part of this course, the beginning part of this course, is to build up basic programming skills by tackling the problem of manipulating tables like these. What did I want to do? Oh, I wanted to look at now, now. Well, it's actually not now, it's last year. Is the degrees where the year, which is a column in this table was 2014 to 2015. And the first thing we did was wonder, what are the big degrees on campus so we can sort this by the number of degrees conferred in descending order and then what is last year? Well, that's a new table based on the table I had before that only talks about 2014 to 2015 and tells us what bachelor's degrees were earned at Cal or 631 economics degrees, a whole bunch of integrative biology, some psychology, political science, electrical engineering, business administration, et cetera. Now, I just wrote a program. And this is the kind of program that I want you to be able to write comfortably. It's stringing together different expressions that take a piece of information, a table full of data and manipulate it to get you one that is more useful to you than what you had before. But reading a column of numbers is pretty hard. And by the way, this is only the first 10. What about the other 110? Well, the right way to explore a data set like this is to draw it, is to visualize it. Is to say, I don't care about the year anymore because I've isolated it to one year, but please draw me a bar chart that tells me the number of degrees conferred for each major. And computers do this for you quite quickly and in quite a useful way now so that you can see not only one of the top 10, but their relative sizes where each vertical hash mark here is another 100. And so we can see, oh, yes, economics and integrative biology are quite large, but there are other large majors as well. And if we scroll down for a while, we see there's some threshold where kind of, there are fewer than 100 degrees conferred and we reach that threshold right between chemical engineering and bioengineering. So if we wanted to worry about what are all the big majors on campus and go ask them about their requirements first, we now know that we have to only deal with that much of the chart. How much of the chart is that? Well, maybe I wanna know how many different degrees have about 100 people in them or about 200 people in them. That's another classic case of information visualization where we just select the degrees that we want to visualize and we draw what's called a histogram. A histogram tells you the number of degrees. That's what's on the horizontal axis. On the y-axis there, that's the number of different majors that have that many degrees. And we can decide exactly how we wanna bucket it. We can say I wanna start at zero, call the way up to 1,000 and have buckets of size 100. Now it's telling me, well, actually most majors have between zero and 100 degrees conferred in 2014, 2015. There were about 95 of those and then there were fewer of everything else. I mean, that's the kind of stuff that, we didn't have a computer and we didn't have access to this information. It might take a day of emailing somebody to figure this out and now we can figure it out in a few minutes. And that's powerful. What's powerful about doing it in a programming language is that you can exactly parameterize what you're asking for quite quickly. Like I could say, okay, I'm gonna do something crazy. I'm gonna say I'll worry about all these majors that have less than 100 people next year and I'm gonna focus on the ones that have at least 100 people in them. I just changed my bins to say I'm only interested in zooming in on that part of the chart that has majors that have between 100 and 200 people. So there are 14 of those that I have to call up and ask about their Stat 2 requirement and there's another five between 200 and 300 and another five between 300 and 400 and then there's integrative biology and economics. So that's the kind of program I want you to get used to writing. I'm not saying you know how to write it now that I've just typed it out for you but I'd like to communicate to you that the amount of code that you sit there and work with is not like volumes. Instead, it's just a few expressions that let you say what you want in order to understand some descriptive property of the world. Now once you've learned how to visualize information you can start asking more precise questions. You can ask, has it always been this way? Can I predict based on how many majors are being, how many degrees are being offered this year and how many people are interested in those majors in the future what are gonna be the degree levels three years from now? That's a machine learning or prediction problem that we do work on in the class. And you can also ask you know if this is different than the year before are they different just because of random variations or is there a real trend going on? And those are the kinds of things that the same questions come up in data science regardless of what you're asking the questions about. Whether you're doing a scientific experiment or in social science or you're trying to run a business or whatever it is you're always asking, is there a real trend here? What is the trend and how can I make predictions? But first you ask, what do my data look like? What's going on in this big pool of numbers? And you do that by putting it in a chart. But not only putting it in a chart we also work with text data. So we look at song lyrics and we're trying to understand how text behaves if you think about it quantitatively. We look at geographic informations we're trying to understand how California uses water in one of the projects this semester. And so we look at different water districts and their water usage. So you can take information basically from any area of the world and start to visualize it and understand it. And from there, then you start asking these statistical questions. Okay, so that's kind of like a lecture that is a flavor of the sort of stuff that we're gonna cover. Anyone have questions about that? That's a good question. One that I don't have a great answer to. Oh, you don't, oh, Ryan thinks probably not. Oh, so a lot of the same techniques will appear. So you'll kind of have seen that but if you've never learned how to program and kind of executed those techniques using a program well then maybe you'll learn something new. But it sounds like, I don't know the answer but it sounds like there's probably a lot of overlap between what you learn in econometrics and what you learn here. So we have a new point of view which is that it would complement it pretty well because this is far more applied in nature. Yeah, I mean we do really look at actual data sets. We download them, we see what happens. We see where the assumptions hold and where the assumptions fail. So this is not really learning the theory. I mean we do learn the theory but it's not just learning the theory. It's learning whether the theory really applies and how to apply it and how to make corrections when it doesn't. So the other thing I'd say is if you've seen some statistical concepts before it takes a lot of experience to learn how to apply them right, how to correctly formulate your null hypothesis and how to correctly test it. And so it's not such a bad thing to be taking multiple courses where these ideas appear just so you can build up some experience. But I can't tell you for sure, you gotta shop it for a while. Okay, I'm now gonna let a couple of connector instructors tell you about their connectors just so you get a flavor of what those look like. All 10 of them are different. But I can't do a parade of all 10 because that's too much. So instead we've just picked a couple that are willing to come talk to you for a little while. We'll start with Ryan Edwards who's gonna tell you about the health connector. Not the only theme, but I think one of the themes of the meeting today is shop it and come and try us out. So if you're not sure whether you'll learn things that are unique relative to it, the econometrics course that you may have taken already come and check us out. I think we will, especially in the connectors, adapt to your needs and desires. The beauty of this so far is that not only our class size is fairly small and we can adapt to the needs of the students, but you're tapping into an amazing interdisciplinary group of people. I mean, super interdisciplinary. We're talking about electrical engineers, social scientists, I'm an economist, a history professor, people from all across the spectrum. Can I actually get a show of hands? So I think a lot of the questions were from folks who were interested in computer science majors. So how many people are thinking about majoring in computer science? A large number, that's great. How about statistics? Super. Social science? The number, okay, fabulous. So this is a great heterogeneous crowd. And I also would exhort you to find your friends who are not sure what they want to do about their quantitative skills requirement for their major and suggest that they take data science. I wish that 25 years ago, I was sitting where you were sitting now. So I went to university. I took a computer science course having some expertise in physics and math, but ultimately being a social scientist at heart, so I'm an economist. I'm visiting from Queens College in the City University of New York and I was a Cal graduate myself. So I learned from the very best in computer science, but at the time it was geared toward computer scientists. This is a course that is broadly geared and I couldn't be more excited to pitch it to you. It's a great way of learning applied statistics. It's not as focused as say an econometrics course or a sociology stats requirement or something like that, but it could be if you show up and ask us in the connectors to do this. So let me say a few things in particular about my connector, which I'll be offering again in the spring. And so when I was thinking about what to say, this is what I came up with. Well, there's data science in the main class and ultimately I kind of felt like I was teaching a bit of data art. How to think about what you're looking at not necessarily in a numbers sense, but in an artistic way. What are you trying to tease out of these numbers? And there's kind of an art to that, especially when you're thinking about social science. When underlying everything is the behavior of people, which can really be rather, well, unexpected perhaps. Ronald Lee is a professor emeritus of economics and demography here at Cal and he said something that I thought was really astute several years ago that I heard when I'm paraphrasing me here and he said, sometimes your eye is the best statistical tool and that's absolutely right, except when it isn't. So that is a very good baseline to adhere to, but especially when you're talking about studies in the social science fields where you're thinking about the effect of a certain policy on some outcome. Well, if you've run a randomized experiment, you might know the answer to that, at least in the internal consistency of that particular study and then there's a question about whether there's validity to other situations, but you might not know the answer at all if what you've got is just observational data and that's kind of the point. So in health economics, and this is a real standard example here, how do we know whether a new drug, longiviratrol, lengthened life, longiviratrol, I guess, I just came up with a silly name, of course, and so I can't pronounce it anyway. Well, what would you do if you thought about the x-axis here as being your treatment variable? And it's kind of a zero one where you've got a discrete pile of folks who are not treated, that's the placebo, if you felt you were concerned about the mice, you had to give them a little fake pill so that they don't suspect that they're in the control group, that's of course what we do for humans, mice not so much, you treat some mice with it so they get the longiviratrol, it sort of sounds like a pet food, I guess, doesn't it? And then you compare their outcomes across these two treatment and control groups and let's see if this is visible, well, somewhat.  And part of data science is just thinking about there being some kind of a relationship here. So if you have a treatment that's the longiviratrol, how much does that gain you in terms of length of life? And so part of this is to figure out whether the data as you see them are sufficient evidence for you to conclude that in fact, longiviratrol actually works. So that was discussed I think rather aptly already by John and that's a big part of everything. Well, the extra part of the story that I bring to the game though is the fact that there are many influences on health like behaviors where you can't actually run randomized controlled trials and some mice obviously cannot smoke cigarettes. If you gave them, I suppose a blood injection of nicotine, I suppose you could think about that sort of thing. Broadly speaking, we're interested in a lot of influences on well-being where it would probably be unethical to actually have a treatment group in some cases if the thing is a bad or possibly unethical if there was a control and of course there are gray areas here. There was a study out that several months ago was halted because it revealed significant benefits of blood pressure control. And so the way this works is you have a control group that doesn't get the benefit of the treatment the treatment group did and they really benefited and the study was halted because it's not ethical to kind of continue with this because there's such big effects. So when you're thinking broadly about issues in the social sciences of connecting outcomes and treatments, a lot of times we don't have the ability to give our mice cigarettes and see what happens. And in particular, one example of this that leads to a very pretty picture and why I wanted to show it to you is the following. So what is the effect of the minimum legal drinking age on our health? And we might wonder what that might be. The typical problem with this sort of thing, it depends how you measure it, but how you were to test it. People who like risk might both crash cars and drink. So it's not too obvious that the legal drinking age actually affects whether or not they crash cars and cause harm. So rather than compare drinkers to non-drinkers, which is what you could do if you had data on this kind of thing, let's be a little bit more clever. Let's use art to sort of think about how to do this. The art is trying to think about what is the comparison that will remove this problem? Well, let's look at everybody just under 21 to everyone just over 21. So we're combining risky and not risky people and just mushing them all together. There's no reason to think that there's any difference in these characteristics across age, on average, that, well, of course, some cultures believe that when you're born sort of determines your characteristics, but within a calendar year, those people who were born are gonna be all types. And so the average person is probably not gonna be significantly different. Well, if we do that and let's look at the data, suppose you were to plot then the death rate by motor vehicle accident along the y-axis and the age in years and months along the x-axis, here's a cloud that you would see where we're measuring age in years and months and that's why we get so many of these little dollops all over the place. Looking at this and trying to make sense of it is a core element of DS8. One of the core elements of my connector course is looking at this with some art in mind. Where do we draw the lines? And, well, the I might be drawn to one relationship here if taken as a whole. If I were to say, you know, I think age 21, which is the middle of this graph is rather interesting, I might draw these two lines. So in fact, those are the trend lines if you restrict the samples to under 21 and over 21 and it shows this shockingly large effect of turning 21 on motor vehicle accidents. So some art to the science of looking at the data and seeing where your eye leads you. So don't drink and drive. Don't always believe what your eyes see, except when you should, and do enroll in my course, which is LNS 88, Letters in Science. It will be fun and not this hard. Okay, so thanks, that's all I've got. So I'm just throwing in an extra item because I was asked by one of the organizers of these events to mention another direction that you may want to do. If in fact you are taking the DAS science eight this coming semester, which is that there's a complimentary course that we've been thinking a lot about how to tie the two together, where if you take the two, this coming spring, for example, we'll be definitely looking for students who would like to help develop the connector course version of it for the following years. And this is a course that's one of the big idea courses that's one of these Letters in Science, big idea courses that I've been teaching and I'm Sol Promutter and I'm in the physics department. I'm also the director here at BIDS. And I'm working with professors in the public policy school and the philosophy department and also a cognitive science professor. And it's a big idea course is bringing these all together and asking questions about scientific style, critical thinking. So these are the complimentary questions that go along with the data science questions which play a big role in a lot of what we do in the scientific style, critical thinking course, but it's much more from the point of view of where do you want your decisions to be made in a democratic society and why would you trust the scientist versus when would you want to go to the population to ask a question. And then within that question, we basically come up with a wide variety of ways in which if you understand the number of different concepts, you'll understand what is going on in a scientific discussion about a topic. And typically these are ways in which we've learned over the years as scientists that humans fool themselves. And so mostly what the course consists of is one after another different techniques of catching yourself, fooling yourself in this way or in that way. And the course is taught in very much of a short bits of lecturing but with lots of group activities and group discussions so that you really feel all these different ways in which you might fool yourself and then not fall into that trap in the future. And then when you get together with groups in the future, you perhaps be able to together not fall into this trap. So we think that in the long run, this actually will be something that we want to do very much as a connector course topic with the data science world because we think those would be great example ways to show all the examples rid out with actual data. And we are looking for students who would enjoy trying to figure out how we tie this two together and perhaps helping develop the course for the future. So let me stop there and pass it back. Well, and that course by the way, that course is called Sense and Sensibility and Science. And it's a, Letters and Science 70, I believe. You will have to check the number. No, I'm sorry. I think Letters and Science 22. And but Sense, Sensibility and Science if you want to look for it from the spring. Hi, everyone. Thanks for coming on this rainy day. I believe I am unique among the group of faculty here in that I'm the only person who has taught both the main course as well as a connector.  The connector that I'm teaching is called STAT 88, Probability and Mathematical Statistics for Data Science. And the reason I have been asked to talk about it is that it's a little different from the other. Going deeply into one area of application using the methods that are developed in the main course. The Probability and Mathematical Statistics. The other connectors are all sort of disconnector, has a different purpose. The ideas that you will learn in the foundational course all have a clear mathematical basis. However, the main course has no mathematical prerequisite because those ideas can be talked about without going into all of the details of the mathematics. And so it is intended for people who have come in really with no detailed quantitative training whatsoever. But there are in the class many people who would like to know the mathematical basis for the results as far as it's possible with the mathematics that students have the reasons why the results are the way they are. And because of that, and so the Probability and Mathematical Statistics connector that it has a semester of calculus as a prerequisite. So if you take C8, the main course, plus the two unit stat 88, then the combination of that dominates stat 20 and 21. That is, it contains more than stat 20 and 21. And so it ought to once we are done with this process satisfy requirements for economics and business, Catherine, are you still here? Do we know the result for econ? Main course plus stat 88, are we through with the process for econ? Okay, so I can now announce formally that if you take C8 plus stat 88, then that does fulfill the stat requirement for economics. And by next term, we hope that the process will go through for other majors as well that require stat 20. So what I'll do now is I will stop and I'll take questions, not only about the connector, but about the main course as well. Anybody, anything? Yes. So would it make sense to take C8 stat 88 and also another connector? It would be fantastic. And in fact, people are doing it now. So you get both the mathematical basis as well as an in-depth look at some area of application would be fantastic. Yes, yes. Yeah, that's a great question. So this course is really about data science in the mind. So it's about workings of the mind and we explore that question looking at different kind of cognitive data. So maybe, let me give you one example that I'll be teaching in the course so you get a flavor of the course. So let's say I have a couple of concepts, penguin, sparrow, and let's say Robin, okay? All of these are names of birds, right? But if I ask you, which of them is sort of the worst example of bird? Which would you say? Probably penguin, mostly agree, yeah? So the kind of phenomena that we're interested in is why the mind interprets these objects sort of differently, even though they're in the same kind of category. If you think about sort of giving these concepts to a computer, probably they will return the same kind of answer, unless you encode some kind of model of the brain that the computer will churn out some kind of ratings that are similar to how humans will behave. So this is kind of the things that you wind up doing, sort of modeling the human mind using behavioral data and other kinds of data. Does that address your question? Okay. Right, anybody else? Okay, I will finish off with an answer, an additional answer to a question that was raised some time ago by you, which is are there any follow on courses? And this, what I'd like to say is that there have been follow on courses for quite some time. Data science isn't new. We've been teaching data science, it just hasn't been called data science. So once you take the main foundations course, you should be taking some programming. You should be taking some probability theory, CS70, stat 134, get plenty of tools going. There are machine learning courses in computer science, as well as in statistics, there are courses in the School of Information. There are many. There will also be courses that are now developed directly as follow-ons, but there's plenty for you to do to become a data scientist already. John, last words. I think we're done. Right, no more questions, then we're done. Thank you so much for coming and we look forward to seeing you next time. We will all stick around for a minute.


Hi everyone, Sean with jaspyscasebrakes.com here doing 2021 hip parade baseball limited edition series 15 hobby 10 box case break random team number one where obviously all card ship and it's one of the best free pack products There is so 2021 hip parade baseball limited edition series 15 hobby 10 box case out of 100 Jeter, Degrom, Tatis, etc. 30 spots I'm gonna get to random team in LV and one card per box Every box in here, so there's 10 cases made Each hand numbered and inside those are randomly inserted 10 of these boxes, which are also hand numbered to 100 We have two cases here, so Random team number two is already in the store We're gonna figure out which case we're gonna do for random team one second one whichever one we don't hit with the dice We'll actually end up with random team number two, which is in the store now Go get your spots Here is the list of customers for the first iteration Robert Flores down to Jeremy Port and last spot mojo and Washington Nationals back up to the Arizona Diamondbacks dice world randomized both lists and pair them up three and a four seven times lower will Lower VIP's higher Their their higher end ones are VIP and Sapphire VIP. I think is their highest one Seven times the randomizer seven times in the dice It's still really cool stuff. I mean Hip parade doesn't do bad Seven spots Seven dice That's why Jaspie's loves hit parade one Two and I also got some pretty comfortable sweater from them as well from the Cardinals down to the Reds Seven times Seven times on the dice So Kyle Samuel with the Cardinals Steve Virch with the Red Sox Kurt Brose with the Brewers Jeremy Razor with the Orioles Corey Parsons with the Dodgers Wade Valentine with the Rays Jeremy Port with the A's Kurt Brose with the Cubs Victor Zamora with the White Sox Corey Parsons with the Blue Jays Kurt Brose with the Mariners Howard Tong with the Rangers Robert Flores The Nationals Jeremy Razor with the Diamondbacks Jeff Goldhaber with the Padres Jeremy Razor with the Indians Oliver Soria with the Braves Chris Miller with the Angels Robert Flores with the Phillies John Gotti with the twins Steve Virch with the Royals Robert Flores with the Marlins Corey Parsons the Rockies Robert Flores with the Yankees and the Mets Jeremy Port with the Tigers Kurt Brose with Astros Robert Flores with the Pirates John Gotti with the Giants and Robert Flores with the Reds Trade window is open be careful. There is no type of checklist with a product like this So understand when you are trading it's pretty much blind. There you go. So there is your list We're gonna pop over here real quick while you trade windows open here are the cases We have two cases one is gonna be for this break one's gonna be for number two. This is case number four out of ten and this is six out of ten One two three on the dice for case four on the left four five six in the dice for case six on the right Three so we're gonna go with case four case number six will be for random team number two Which is in the store 30 spots go get them If there's no talks of trades, I guess we'll just go ahead and print and rip Kick back over to the list if anybody wants There's the case obviously Not doing anything with it. No funky business. Oh And if you didn't notice this giant box back here, that's tops transcend it. It's in the store We're about halfway gone on there. We need spots taken straight up and we need to do some fillers Go get your spots guys. Let's do it tonight. So there's already nobody talking about trades So let's go ahead and print and rip everybody's happy with the teams they got Here is case number four. Like I said, they make ten cases total each one hand numbered and Then inside of those they randomly insert ten of their boxes And each one of those is hand numbered as well VIP I think you're right Mike. I think that's exactly how it goes I don't know though sapphire. I sapphire might be higher Mike just because I think with sapphire They only do of course Hank. No problem. Um, I think sapphire only has like three hits in a case, doesn't it? So maybe it's like dollar for dollar. It's higher because you're only getting three hits. I think I don't know But one thing is for sure Doesn't matter if it's limited platinum sapphire or VIP Hit parade does it right cool So first box we've got is 74 out of 100 this tier You'll see these if anybody's never said if you haven't seen any of the hip parade products that we've done before All the packaging is exceptional each one's individually plastic wraps And there's two stickers to seal it one on the front with their label of the series and one on the back with their hip Parade silver label as well then in top inside And the hand numbered box inside they've got this nice little share-your-hit cover with a nice little cushiony Area for your card to be shipped and then you end up with something like this Vlad Guerrero junior rookie autograph from Bowman's best blue Numbered to 150 going to the Toronto Blue Jays and Corey Parsons. That is 2019 Bowman's best baseball Bowman's best Year after year one of my favorite products always Nice one for the blue Jays to start box 99 next up Right Hank Paging sapphire Paging sapphire not one but three Hall of Fame autographs Frank Robinson Jim Palmer Eddie Murray 21 out of 27 tops triple threads Dual jersey relic and a bat relic for the O's and Jeremy Rezzer That's beautiful That is 2009 tops triple threads. That's cool Love it next box is box 35 Padres Justin Padres Yeah, that was Padres Rockies. I did see that and And How about Baltimore Orioles again? That is Bowman platinum 19 out of 25 die-cut autograph of Manny Machata Orioles Jeremy Rezzer Cutting edge stars that is from 2013 Bowman platinum Very cool. Look at the die cut on that very nice Orioles off to an early lead box 28 Yeah, it was bad It was bad He also realized he had messed up as soon as he threw that punch.  That's for the Houston Astros and Kurt Rose That is 2019 Bowman It's a nice look card there. I mean that's not enough. He still needs the face charges to 51 teacher Will I think it's gonna go tonight. I think we're like we're right at the halfway point ten spots We're giving away in fillers So we've sold over 20 spots already I Think I think transcendence gonna go tonight Remember this one if this isn't like the regular transcendent. This is transcendent Hall of Fame. There's only 50 cases total Only 50 this regular transcendent has a hundred cases. This is only 50 so even more limited And we're going rookie auto for the South Siders Eloy Jimenez Chicago White Sox Victor Zamora Very nice That is 2019 tops It's the 1984 Retro, what is this series one series one maybe box 88 Yeah, no, I mean It was pretty dumb too But yeah, the guy knew he messed up as soon as he did it At least he didn't try to run like a little baby. He stood there Let the fans jump him and then Sure, he faced some charges quickly there after as he should I'd now like to see him get into a one-on-one fist fight with Dante Vichette And I would like to see but shit Vichette just bash We're going Spencer Torkelson five out of 19 collegiate national team silhouettes This works just like any of the team any Card where it's a non-pro Jersey where it goes to either the team They're currently on or if they were retired or no longer playing the team that they played for the longest Obviously Spencer Torkelson being the number one overall pick to the Detroit Tigers That's going to Jeremy Port the Tigers in last spot mojo number one overall pick Detroit Tigers Spencer Torkelson box number three is up next remember guys second Second case of this is in the store now Go get your spots this one sold out real quick. I'm sure the next one will as well And we've got a tops inception Alex Verdugo auto 14 out of 50 for the Boston Red Sox and Steve Birch That is 2021 tops inception rookies and emerging stars autograph traded from the Dodgers last year Last spot mojo always works Box 65 up next We've got another patch auto and it's a two-color patch auto Gavin Lux rookie from immaculate baseball 41 out of 99 for the Dodgers and Cory Parsons is he number 41 Jersey number as well anybody know Maybe Jersey number hit. I know he just had his first home run of the of his career in the night. That was nice You go Cory and box number 12 one box left after this guys But we've got a whole another 10 boxes in the store Random team number two and we got Christian Pache Prosper rookie auto actually Don Russ 2021 Atlanta Braves Oliver Soria There you go Oliver and last but not least box 40 Pache's back to the aisle 48 is good luck. It's okay. So not 41 and our final auto is rookie gold Blake's down For from Bowman Chrome that is 19 out of 50 and that is 2016 Bowman Chrome going to the Braves and Wade Valentine Nice gold auto there Blake's now and there you go guys. That was the break That was 2021 hip parade baseball limited edition series 15 hobby 10 box case break random team number one from Jasper's case breaks calm I'm Sean Jasper's case breaks calm. Thanks for hanging out and we'll see you next time


I know it's been short notice, and we appreciate your concern and interest in the budget and what we're doing about the federal budget deficit. I understand you've been briefed in some detail on our fiscal 87 budget proposal. I don't know what you're just doing when I came in. Yes, sir. I hope I can take it. Well, I finished. They heard it after me. I'll try to figure out one thing. He's talking about cutting out the curtains. He's talking about the curtains. He took them home and he still has them up his curtain. But he didn't tell us what he paid for them to serve us. I didn't pay nothing for them to serve us. We will charge people. Well, let me just add a little bit here. I believe April 6th is a year of decision. And it is the year to decide whether we'll have the will to at last bring federal spending under control. And remove what I think is the last remaining obstacle to a future of growth and prosperity. It's the year to decide whether we can make the tough decisions that we agreed to last year when the Grand Brethren College legislation became law. It's clear to the world that our commitment to peace through strength will not be compromised. And a year to maintain our continued opposition to raising taxes which I believe could harm the economy from Americans in order to protect government programs we don't need and can't afford. Today deficit reduction is now the law of the land. Grand Brethren College mandates a declining deficit path leading to a balanced budget in fiscal year 1991. Don't mind sharing with you that my dream is that when that moment comes they put into place a constitutional amendment that says that the budgets will remain balanced and then I'm going to run over to Jefferson in order to see if that statue is smiling. Because he is the first one who ever mentioned that. The time of the ratification of the Constitution Thomas Jefferson said it contains before it does not. It has one glaring omission. It does not have a prohibition against the federal government borrowing. So I think after 200 years without a catch up with Jefferson the deficit will be cut. The big question is how? Grand Brethren College's automatic spending cuts are triggered only if Congress advocates its constitutional responsibility and fails to pass a deficit or a budget that meets the deficit targets. In other words Congress still has a choice. It can be responsible by making those careful and sometimes difficult spending decisions where it can simply drop the ball and the across the board cuts take over. The budget that I will soon be submitting to Congress was carefully prepared by taking into account our defense needs the proper role of government and the Grand Brethren College's deficit targets. And it will meet the fiscal year 1987 deficit target of 144 billion without harming the economy through a tax increase compromising our national security. To preserve the social safety net for those who truly in need and will not penalize older Americans by reducing social security benefits. As I'm sure Cameron Jimmick mentioned our budget will include a modest increase in defense to sustain recent improvements in capability. As some will say that that's too much for defense but the proposed funding levels for national security and our budget are consistent with the real growth back that was agreed due by myself and the Congress last year. Incidentally Bill Casey just the other day had to testify before the Senate and House committees in this manner.  Ignored has been the situation of the great massive buildup of the Soviet Union and we're still playing catch up. I don't know whether I'm repeating something that maybe campus said but in the ten years just between 74 and 84. So that takes in Salamari years. The Soviet Union has procured three times as many intercontinental and submarine launch missiles as we have. Six times as many intermediate range ballistic missiles. Nine times the surface to air missiles. Fifty times the bombers long range of medium that we have procured in the same period. Twice as many fighter planes, twice as many helicopters, twice as many submarines three times as many tanks and ten times as many artillery pieces. So I don't think that we're being extravagant in suggesting that we still have a little job to do in catching up with them. Higher taxes as I said earlier are no cure for the deficit. I think that reducing the deficit by tax hikes would impose substantial new tax burdens on American households and as experiences prove would reduce incentives for Americans to work, save and invest. That's choking off the creation of new jobs. Experiences also shown that higher taxes do not necessarily go to reduce the deficit but more often they're used to justify increased government spending. The current economic expansion is well into its fourth year. Over nine million new jobs have been created since November of 1982. We've made significant progress in reducing inflation but I'm not going to rest until it's down to zero. Nominal interest rates are coming down on a well below levels reached in the 70s and the 80s. Excessive federal spending and a large federal deficit are the last major obstacles blocking our path as I said, the lasting prosperity. The budget we're sending to Congress on February 5th will put us on the right track to a balanced budget by official year 1991 but it will reduce the deficit while promoting economic growth and meaning our national security requirements. We've made the tough decision about its time to see if Congress is up to the challenge of passing a responsible budget. I need your help but I encourage you to make your views known. I know we're in for a tough battle. Together we can reduce the deficit instead of having to have permanent prosperity. But now that's enough for me. I'd like to hear from you. That's on your mind. Questions? Maybe have my answer. It's four years of service as our... That's why I'd like to ask you to come up here so I can give you something special that I've been keeping for him now for about a year to give Don the highest award that the Treasury Department can offer the Alexander Hamilton Award. Don, to one thing I'm absolutely certain, Alexander Hamilton and a dominant New Yorker like yourself and a promoter of the energy that I see out there and people who have worked with me. But let you think that I didn't commentate also. I do want to say that I too imperative that and I too found something like that. We're very involved with Jim Baker, the infusion. You're fair to the problem with energy. Perhaps an interesting act of things of that nature. Here's a position for Jim Baker's position. Issue number two, action on energy from the campaign of Jim Baker for Attorney General. Is ready, willing, eager to follow the Court's Charter. Last night you officially began the second year of your second term. Tomorrow you begin your 75th year. We're delighted that you chose to leave with us today. Halfway between these two important events. This is the United States. You are welcome to once again keep intact your reform on track. As I outlined last night in the State of the Union we turn to the full 2,000-dollar personal exemption for identifiers as well as non-identifiers. At least for those individuals in the lower room who may come back. Just to the increasing share of the tax burden on the shoulders of families we serve. Reminds me. We think it's about putting personal exemption today in purchasing power at one of the 600. The exemption will be 2,700 doctors. So that investment decisions aren't disrupting the ability to be more certain. A minimum tax of success. We should all reflect on the dramatic evolution of the change of tax reform represents. We must work to promote the expansion of world trade and growth of the global economy by strengthening economic policy coordination among our industrial and trade partners. As I mentioned in the State of the Union I have directed for every secretary change making to determine if the nations of the world should convene to distress the global relationship of our currencies. Many developing countries with large debts are in particularly dire straits and we in the industrial world must assist them in dealing with their difficulties except denial of property rights. In particular, we must encourage them to avoid high tax rates that only choke off in savings and silverware. We play with many developing countries that are desperate and call to action in search of them. So let's begin now to spread hope and opportunity across the world by encouraging lower taxes, free from fair trade and a sound monetary system and human traditional and essential enforcement activities. We're going to have a hands full. Working together we're going to make 1996 the year that the tax rate cuts holding wide the doors of opportunity at home and our program for sustaining growth on the way to government's growth and hope. That's the financial, educational, social and safety concerns of poor families. And I want the Council to report to me by December 1st on evaluation strategies for immediate action that we should take to meet these concerns. I believe that every person in our free society should have the opportunity to secure their basic living requirements first through their own efforts and then through the support by their family and neighborhood and their community. I also believe that many federal programs have not been working to make this happen. We have far too many programs that are still dependent upon public assistance and that we spend huge sums of money that should be reducing this dependency. In fact, some federal programs appear to be increasing rather than decreasing the dependency of individuals and families, and I think that is the greatest thing that something is wrong. We should be motivating individuals and families to become more independent and economically secure through work efforts and initiative and not through government programs. So I'm asking the Council to evaluate all those federal programs on this area to look at how these programs relate to one another and to report to media what actions we ought to make to the programs nor may suggest to me later in the year we need to explore any idea that may have merit. It's no easy task, I recognize that and I look forward to a strategy we've been realistically employed to solve the problem we've lived with far too long and that we'll get on in the meeting and make certain changes of technology. The President of the United States, accompanied by Secretary Bowen. Today is special. Newest Cabinet Member, I'm pleased that you, Mr. President, have chosen to honor health and human services employees that are causing us less than a day following your inspiring and challenging State of the Union address. I'm gratified that you have also proclaimed that Medicare catastrophic health coverage is a priority of your admittance. As our first Republican President Abraham Lincoln said, government's role is to do only those things for people that they can't do for themselves. If this is true, then much of the government's domestic responsibilities reside in this department, effectively. And we know that efficient and effective management is not the enemy of caring and compassionate programs, but in fact is necessary for a program's success. It is in this spirit that we welcome you. Ladies and gentlemen, it is a great honor for me to introduce the President of the United States. The last two presidents that visited here in their first term weren't re-elected. We've just entered sixth anniversary of mine. The age is riveted. It was once a very famous baseball. How old would you be if you didn't know how old you were? That's how I came up with 39. Well, how did India have to come here and help us? It won't be an easy job. We've just entered sixth anniversary of mine. The age is riveted. India had to come here and help us. It won't be an easy job, but it's a job worth doing. Now, with all the executive agencies it's possible to have it. We have a lot of major initiatives ahead of us. We're going to move toward a more efficient healthcare system. And we're going to try to see if we can. As you know, I've asked Dr. Bohm to study how the private sector and government can work together on this problem and report to me by the end of the year. We continue to support, emphasize this competition and broaden in the type of healthcare plans that qualify as alternatives to traditional Medicare coverage. We will encourage private healthcare providers to differ. Has that wondered for the way I say we? And so much of it's going to be you? Well, we're going to take the doctors and others have to pay. And we're going to look at the practices that minimize malpractice exposure. These are just a few of our plans, but I want to mention one more. One of our highest public health priority on your plate is pretty full. But I know you're up to the job, you always have been. I want you to know that across town in the White House we're aware of your good work, of how hard you work, and we appreciate it. And I just want you to know that we started a little with Moss Hart, the playwright, who was an inveterate along that line. And so one night at a cocktail party in Hollywood he was introduced to a Dr. Jones. And almost immediately he started talking about I've been having this low back pain and the fella that introduced them said, Moss. I just, well, thanks again. God bless all of you.