Okay great so I think we can start with the announcements and today's session so the first thing as you know is Buongiorno! Buongiorno a tutti! So and I heard actually that yesterday you also learned come e state come state come and the answer to that which is the new Italian beat for today is Bene! Bene! Okay let me tell you something about this Bene so in Italian if you say Bene it actually means you're not feeling super good so let's try that again so you really need to feel if you're really feeling good then you really feel the energy going up and then you see you automatically see that your hands have to move remember it's Italian and it really goes up to the smile and and it's Bene! Bene! Okay so you have to these two choices depending on how you feel that that's good just be aware of them so come e state okay so tomorrow Alexander will check so please make me proud let's go back to the announcement serious announcements so ladies and gentlemen no food and drinks in the rooms except for your amazing gadgets for European bottles double check the schedule because there might have been some change so remember our great app that you are hopefully using that you can download from for free prepare for the trainings so in case you're attending the trainings remember to download install and see the training pages for today there's two Euro Python sessions so remember that at two you can join us and if you're willing to help us to build a Euro Python 2018 and that's going to be followed by Euro Python Society General Assembly at 235 so the the boss here told me that the second part is like the boring part and the first part is the woo part so just remember if you're willing to help us just join that's open foot for everyone and there is probably going to be also a further open discussion session that is going to be announced in the board in the open space behind the reception and it's going to be somewhere at some point in the in the afternoon so for all people who would like to discuss about how to organize and improve Euro Python for future years next point tonight there is the social event at coconuts so first of all the event is sold out so unfortunately no tickets anymore but the good thing is that there's you can if you haven't if you didn't get your ticket yet you can still obviously pick it up and please do it at the reception before 3 today preferably so that we avoid queues at the end there is information on the website in the events page on how it's going to happen and basically it's going to start at 7 from 7 to 10 there's going to be a sort of aperitivo standing buffet and then there is the party afterwards until early in the morning so there is a part which is a private reserved for us for the whole night but after 10 remember that people from outside the external people can also join the rest of the bar okay so this is where the party is going to happen we are in the bottom thing this is not working and in the top you should see the coconuts bar by the beach next announcement feedback of the conference so remember that at any time after the talks you can rate the talks and hopefully write fruitful comments for us and for the speakers and in addition to that after the conference so next week you will receive a Google form in which we ask you for some questions and we really invite you and encourage you to answer and take part of the survey because it's really helpful for us to build up and improve for the next edition final thing today at 5 there is going to be a sprint orientation so you might be aware that in the weekend on Saturday and Sunday there's going to be sprints or projects in which you can group work together in a group and today you can join there's going to be some explanation on how this is going to work and also gathering ideas of people who would like to propose projects for the spring okay so that's it for the announcements for today so enjoy the conference okay so we move to okay so we welcome our keynote speaker for today Tracy Osburn that just joined us from Canada and so she's a designer producer entrepreneur and she is going to tell us more about the idea of a Python engineer so let's see if that works perfect so happy to be here ciao I'm Tracy a while ago I made the vast mistake of making my username on the internet lime daring it's my github profile it's my website it's my Twitter handle it's my Instagram and is constantly confused as lime darling even by me which is annoying because I can't go back and change it at this point so FYI if you want to stalk me send me questions on Twitter send feedback on this talk FYI it's lime daring not lime darling so this is these are my books hello web app they have a few here if you want to see them in person or by Mofmi they're my my book series introducing Django and teaching people how to make web apps so I am like I mentioned I'm from I live in Canada I'm originally from California I'm more I think well-known in North America so a little bit of an ego check here how many people in this audience have heard my name see my books no of me of some sort all right cool relatively unknown here awesome so I should check out hello web app these are my books I designed them I wrote them and I did Kickstarter for them raised a lot of money if anyone here is a Kickstarter backer thank you so much so I wrote the books I have done a lot of conference speaking you might have seen me at Django con Europe in Florence a few months ago I have spoken at Django cons of pike cons I was in Berlin for view source last year I believe so I do a lot of conference speaking I am an author of a book on Django sometimes I feel kind of badass but then comes times and I'm sorry you're a python thank you so much for inviting me to do a keynote keynote talk here but I'm gonna start off my presentation by doing a little bit of light criticism sometimes I see things like this which is rate your Python power rate the amount of Python you know and I just go into this swirl despair I mean again I wrote a book on you know on Django I'm not gonna say on Python I wrote a book on Django sometimes I feel really awesome but then I see these questions and I'm like oh no where do I fit like I really really don't like being pushed into just kind of this linear I know what's sorry one two three four or five although I did laugh I saw this question so I was like oh my gosh that's exactly why I want to talk about at this keynote so thanks you're a python sorry for criticizing but this is exactly what I want to talk about is this idea that there is this linear path in programming there's this linear path in Python where you go from say beginner to intermediate to advanced and how this path can actually exclude a lot of people how this path can make people feel and welcome like I put three stars for myself at a pride because I didn't want to be like oh I'm a keynote speaker at Euro Python and I'm they have two stars but really that's actually where I am probably two stars the thing is is that confession I'm Tracy and I have absolutely no intention of becoming a software developer like as a job I've chosen to teach myself programming I work with Python I write to help teach other people Python and Django but the idea of say a five star Python programmer is not a place where I'll ever ever go it's not for me you know so I'm still you know I'm still a software engineer I'm just not a software like a software developer I'm not I look at the curriculum here at Euro Python I feel this this dissonance again being an author of books and yet still going to beginner sessions so I do like I said I'll do a lot of conference talks and I have some very strong ideas about talks versus keynotes when it comes to talks I like to have concrete information and like solid takeaways like you know a full like something that people can walk away from something they can write down whereas keynotes I think are a place where we can explore a problem or we can ask questions can tell stories we can start a discussion without really having a conclusion so just heads up I just want to talk about a little bit of my story and how I've gotten here and some of the issues that I've seen in programming I think are affecting our industry that I don't really see talked about as much or least in the context of what I'm doing and I want to start this discussion so there's no gonna be like this the stars in the badges I'm not gonna say that is not the right thing to do because you know that's just my opinion it doesn't work for me I'm not gonna have solid conclusions like that I'm just here to start a discussion so to really get my full background you have to get my full background so I'm gonna start out telling my personal story about learning to program Rocky Road to programming which features the Rocky Road that I grew up on I grew up in Northern California a very very rural area my town was about 3,000 people I lived about half an hour away from it I was lucky to have a couple family members that were were involved in computers in the 80s my grandfather my uncle so I was lucky you know I was born in 1984 I was lucky to always have a computer like incredibly lucky I'm that's part of the reason why I'm here as I was introduced to computers so early like those giant floppies and everything and when it came to all right should I go outside and run around in the trees and mountains and be outside or be on the computer tack tacking playing games I chose to be on a computer and then then in the 90s and basic HTML and I was like boom this is awesome I can make web pages I can host them on like angel fire and this is around say 1996 I taught myself basic HTML I discovered that teachers at my high school if they asked you to write so-and-so page report I just ignored their instructions and I made a website and then I showed them the website on the internet they're like whoa instant a and that is how I got through high school which is by building websites and like my websites I mean things like this this is actually the website taught myself HTML from from way back machine brings me a lot of joy I had friends who program and calculators I was friends with a nerdy group in high school and I was never really interested in programming calculators like that was I thought I was programming I know HTML is not by the time I did and I was much more interested in doing websites and things that were visual and things that people can see interact with and that was kind of a hint of what was come what was to come for me once I was in way back machine I started pulling up all my old websites I'm not going to show you like these are still live I'm not going to give you the addresses but this is my website from high school which was a Terry Brooks fan page it was actually in the running the author fantasy author Terry Brooks it was in the running to be the official fan page when I was 14 check out that frame look at those like generated buttons oh my gosh it's somewhere on there is like some really bad poetry for me in high school I found another website I made this is my golf face everything's really black so I had what I thought was the perfect background for a computer science degree I loved computers I love being on computers I wanted to know everything about them I liked doing the visual stuff again I didn't program calculators like my friends but I thought I would love I wouldn't come computer scientists I'm gonna learn to program and do all these awesome computer things that you know I see my family doing so this is where I went to university it's Cal Poly San Luis Obispo it's about Central Coast of California it's a really great computer science school and literally day one hour one I got a wake-up call I sat down this is like day one hour one of university not just at this class this is the very first class I took of my in university and the professor just started speaking and I don't really say but this is my face throughout the entire first session of computer science I forget I guess I don't remember what he was saying but I did not get it I was like what like was there a prerequisite that I missed was there some like piece of knowledge that all these other people like all these are dudes in the class be like yeah I'm like what I understand the thing here this is awful again day one hour one beginner computer science class and I chased the professor down after class and I'm like what did I miss like how did I miss how everyone else is understanding what you're talking about he's like it's okay it's okay chill out it'll get better really didn't get better didn't help that these classes were teaching Java and like I love Python now and Java was just for me learning was not good nothing against the language it's just not for me so I passed that one one class Cal Poly that we were in the quarter system instead of semesters so there's three quarters per year so a winter quarter took one or two which we dealt with GUIs and I was like whoa actually I'm starting to get this like you press a button and then there's code in the background that does something and then you see your result it made so much more sense to me than working in the command line and I the one of two was the class I started doing better and I was like oh maybe I can make it then spring quarter was computer science 103 fundamentals of your science three and unlike the first two classes of the year one which introduced basic programming concepts and logic one that start talking about GUIs this class large like 99% dealt with theory and abstract concepts and this was awful for me again I'm a visual person like when I see something and click a button it does something like oh I get it but when you're one of our projects was to take sorting methods and we had to reverse engineer them and then create these graphs to show why bubble sort is better than other sort and oh my god this class was not good for me so one project had a rubric and the rubric you know 50% for this 20% for this and if I'm a struggling student I'm going to mentor mentorship sessions I have a tutor I'm trying so hard to pass this class so of course when you get project like that you spend the most amount of time on the thing that's worth 50% of course naturally and when the professor gave me back my project he I had a lesson I had a lower grade because he changed the rubric some things were worth more on the second rubric than before so again as any student would do I went back to him and I said hey you know this was worth so and so can I have the original rubric back because I would get a better grade naturally right like no one should be surprised by this of course you would do this and the professor you know people who quit computer science you know maybe maybe or not they can say the moment that they quit computer science and this was my moment professor emailed back saying okay fine I'll give you the old rubric and then for at least three paragraphs went on to this rant about how lazy I was and what a bad student I was and how I needed to stop trying to skate my way through college those are his words stuck with me how I needed to actually study and actually try and oh my god this hurt so much because I was a struggling computer science student I was going to these mentoring sessions I was going I was getting tutored and these abstract concepts just did not work with my brain I was trying so hard to have this professor just tell me how lazy he thought I was that was it I was done I I started researching that night on how to to leave computer science and I went complete opposite direction I got art degree I was like screw computers I hate computers now never gonna do computers again I'm going to paint and then eventually start doing graphic design and my job I was going to do was gonna be package design and I was yet not going to do programming ever again I hate programming like I thought that it wasn't for me and I started falling back more and more into computers because in right before I graduated university I started working for a startup literally in a garage which is kind of cool and they hired me to become the web designer that's me and my goatee like this photo I mean I was doing what the front end web development for them and you know front end web development usually is HTML CSS and JavaScript but I was so traumatized by Java and I know Java and JavaScript are not the same but the mere fact that Java was in the name and that JavaScript has these really scary curly brackets I refused to do any JavaScript at this company I was a front end developer ignoring JavaScript entirely out of PTSD so I think this story has happened to a lot of other people in you know similar ways and I could have been one of the many who wanted to learn computers wanted to jump into the tech industry and then got completely turned off by by a class and a way of thinking that didn't work for them and I think we all know that programming there's so many different aspects to programming and there's so many different ways of learning and ways of programming of using Python but this university education was not for me and that could have been the end my story so in the bigger picture I'm gonna pause this story kind of talk a little more about stats of the industry and I'm gonna you know the stats here I'm going to there are more about women in tech this story I know happens to both men and women people of every gender but these stats are particularly relevant for women so going to focus on that so this is awesome this is awesome the demand of software engineers software developers is expected to grow by 17% by 2024 like we have so many jobs and opportunities for software developers and that's just going to keep going up which is awesome however back in like 1984 1985 the women accounted for nearly 37% of students in university for computer science but that number has dropped in 2010 2011 women just made up 17.6% for the more the percentage of women in computer science related professions have dropped from 35 to 25% in the last 15 years which really when you look at these software jobs and how it is just growing and we have so much growth here and yet women are taking a smaller smaller piece of the pie it's really scary and I think a lot of my experience in university of having these having this class these abstract concepts in this way of thinking that just didn't work for me this one path didn't work for me I think maybe it doesn't work for other people we can still use that I just want to work on ways on how to bring more people into these other paths so speaking of paths this is a fascinating anecdote University of California Berkeley experienced a revolution in their introductory computer science classes after changing the way that they marked marketed the course so what used to be known as introduction to symbolic programming is now known as the beauty and joy of computing and for the first time in 2014 a number of women out never men fascinating right and don't just jump to the conclusion just because the word beauty is the name that means there's more women right as not it when you look at computer science education typically as a single path like you are becoming a software developer and you're going to become a backend engineer and this is you're going to go from a to be and there's only one straight path that's what the symbolic programming really implies like symbolic programming that's kind of intimidating that's kind of super technical sounding but when you have a title like the beauty and joy of computing you've gone from something that looks narrow to something that looks wide more possibilities more things you can do like larger range of topics that's why I think that that change happened I want to talk a little bit about the whole one true programmer myth which is based on the whole no true scotsman myth where you know true programmers do this and true programmers do that you know there's a good blog post called a new the no true programmer fallacy saying if we look at programming as people who can do it or people who can't just a to b binary binary thing then quitting computer science in university makes me someone who can't write like I you know I couldn't make it in university so I shouldn't try which is obviously not true because look where I am right now I'll get into a little bit more of my achievements in a second while there's many ways about many reasons why women jump out drop out of computer science from sexism to being exposed to computers later than men again I'm so lucky to have had access to computers from such an early on search such an early part of my life my own particular problem was the way that computer science was taught to me then it wasn't the best way for me to learn and I wouldn't realize this had the following events not occurred so back to my story rainbows fireworks so much better than Java for me oh my gosh again not necessarily better than Java I don't do that but for me so much better being in California in Silicon Valley everyone and their mother has a startup and so I got the idea I want to start up too because it looks so easy right and because I hate programming I was going to become the non technical co-founder trying to find my technical co-founder as you do go find someone to just code it for you because I was like I hate programming never gonna do that so I have to find someone to do it for me ridiculous so I didn't go well that's the whole post so when I did this co-founder search and things didn't go well I then looked at my past and how what an awful experience I had a university how much I thought I hated programming like I just don't have the mind for programming but I looked at my potential future and how much I wanted this idea how much I wanted to build this idea and someone introduced me to Django which is like a lot of people complain about Django as you know as a framework because it glosses over all the details and it hides everything it's going on in the background abstracts everything away you know what that is what I needed that is exactly my need I did not need to know the details of what was going on I didn't know need to know really what a database was just kind of scandalous I used Django to build the first version of my website this is actually a second version couldn't find a graphic for what it looked like I built this website taught myself a little bit of Python a little bit of Django I basically taught myself how to program a building this website called I love and I wrote this blog post kind of interesting I have blog posts for like every step of my tech journey because I just have been writing for so long which is kind of awesome second look how adorable I was I'm a designer who learned Django launched your first web app in six weeks note that I didn't mention I went through computer science did I on purpose sounded like a better story to say I'm a designer who learned Django you know not I'm a person who failed out of computer science jumped in the art became a web designer then learn Django so smoother story kind of hid the whole university fact for so long mainly because I was I was embarrassed so this experience of teaching myself just enough Django in order to launch this website and then using that to teach myself Python and get more and more into programming started getting the wheels turning my head thinking you know hey this is the way this works for me why can't this work for other people and thus hello web app started so I wrote hello web app it's essentially the process I use to build wedding invite love again the book just like Django abstracts everything away I don't talk history I don't talk theory I don't say you learn how to program I say you learn how to build your web app with hello web app I've gotten so much good feedback from this is one of the best things I've ever done in my life is writing this book and teaching other people how to build web apps and how to use programming like I did because there's a lot of people out there I think that struggle with abstract concepts and theory but still have desire to build something that's why I wrote my book which was kickstarted to both there's hello web app and then there's hello web app intermediate concepts they have both of me again if you want to see them but they're kickstarted and I've done workshops all over the world it's been amazing teaching programming you know I say teaching teaching how to build a web app as a three-star Python power person on my on my little Euro Python badge so that's me in a nutshell that kind of explains a little bit why I feel so passionate about this idea of different paths in programming and sometimes I want to take my books and go to my professor and just wave it in his face be like ha I almost quit tech entirely because of you and now I have books and you'll probably be like who are you don't remember you like don't you remember me you wrote that email so again in the bigger picture degrees boot camps frameworks oh my I want to be doing what I'm doing personally without a framework without being introduced to programming through this like hand-holding Django way I program I don't feel like a programmer at two several conferences last year it was funny they're back to back and I was not meaning to eavesdrop but there was people near me and they both said something to the something like man frickin hate frameworks they're teaching people to be bad programmers they should not be taught they should just go away like these people are mean and I was like excuse me I didn't actually say that in my head I'm not that brave I wish I was be like excuse me they're saying about programmer they probably like yeah and I'd be like oh maybe I am you know I'm not gonna say that they're gonna make you frameworks are gonna make you an awesome programmer again hello web is I'm not is not teaching you to code is teaching you how to build web apps I personally would never be hired at Google or Facebook or anything or something a couple years ago I applied for recurrence center in New York City it's just like summer camp for programmers and you just kind of work on programming concepts join the best programming community in the world and they say we're just here to make you become better programmers and I was thinking hey this is my opportunity to finally become a programmer a programmer and I applied I got through the app the initial application and then they wrote back saying I need to submit a program like a program that I've written not using framework like I this is wedding lovely ridiculous amount I'm not again I'm not proud of number of lines code here but there's a lot of code in wedding lovely and I've written all of it and this is I at this point had never written something without Django so I've written all this code and yet I'm like oh how do even real programs work just kind of embarrassing like wedding lovely has like over 8,000 businesses on it over a million page views and yet I still don't know how to I didn't know how to write a Python program so I'm not going to say they're wrong Django did not make me a great programmer but it let me let the spark and I actually had a lot of fun with this I had to go in and be like wait I don't understand why do I need this whole like if main stuff at the bottom like what does it make any sense to me how to run this program but I built this tic-tac-toe program and yeah so I wrote I made this little tic-tac-toe and it you can play against the computer and I wrote that this is literally my first program ever it's very one-on-one type stuff but had so much fun with it I'm more fun than I had ever had when I was in computer science so Django and framework and having this original hand-holding lit the spark that allowed me to have more fun with you know say traditional programming later and then bigger picture I was I was sent this paper by a friend Greg Wilson in Canada does a lot of work in this area this paper is the baby the barrier is faced by coding bootcamp students so I'm I didn't go through coding bootcamp but I taught myself how to code and I know a lot of other people out there have taught themselves to code and there's a lot of problems face of people who don't have degrees for example people in bootcamps are faced with stereotypes of what a real programmer is or isn't and that's the thing we talked about before the whole new no like no true programmer does this or only true programmers do that so it's faced by people going through bootcamps bootcamp certificates are not perceived as high as values as as university degrees so they're going out trying to find jobs even though they can have really really awesome skills using Python or doing web app stuff the employers would say hey we're looking for someone with a degree instead kind of sucks for people like me university education doesn't really fit and contracting our freelance work was not seen as valuable as a full-time job again for me I've been working for myself largely for the last eight years I'm not really looking for a job but I think obviously that contracting freelancing work is a great way to make a living I love my life and it's like said these coding bootcamp students were looked down on by choosing to work for themselves then find a job there was a lot of quotes in this paper from people said that even after six months of programming they just still didn't feel like an established programmer which does says a lot about their industry that you know they could be programming but still not feel like your part of the larger group now sometimes I'm reminded people come to I think I've largely skipped I haven't had a lot of harassment I know a lot of their women in this industry has have I've largely missed out on that which is awesome people have been very nice to me but I do get judged every now and then for my lack of engineering this my lack of true programmer skills now again even though I have my books and I'm teaching people I did this survey and this survey was for people who know hello web app we're on the newsletter who follow me on Twitter so largely people who are on my side and I wrote I wanted to see if people wanted like I have an online course for hello web app I have my books I sell on Amazon through myself I do workshops I don't have like an online way of helping people go through the book so I wanted to see if that was something that people be willing to use and again going to people this only went to newsletter or people people who largely should be nice to me but someone someone wrote this and it just stuck with me ever since they took the time out of their day to respond to my survey to tell me that I really needed to level my skills up more before acting like I'm an authority which it sucks to have someone out there I can tell there's someone who's looking at me who's just like she's not a programmer and here she is giving keynotes at Euro Python here she is writing a book on Django without remembering how to run it like write a program and run it from a fan line this sucks and then they like stabbed me and twisted the knife by telling me my book was ugly too so thanks what I'm trying to say is that an example of a programmer a person who uses Python has taken a different path and I shouldn't be judged and no one else should be judged for not following the traditional path not becoming a true programmer not becoming a full-on back-end software engineer like I can and I continue will be just watch me I can be an authority of the skills that I have now our industry should accept that not all programmers can have to look the same not all programmers have to think the same not all programmers have to do the same thing or have the same coding ability we're all equal and that's really key to what I want to talk about now that we need to be more accepting of different paths people can take you know someone who learns programming and comes a documentarian someone who learns programming and does it become the scrum master or QA you know if they don't fit into that traditional path becoming a back-end software engineer you know it's about our ecosystem as a whole you know in frameworks boot camps teaching yourself programming I was going to help push people into different paths and we should be accepting of the different ways and places people can go in different ways that people can learn I really like this you know I found this the stat that two or three developers are self-taught which is awesome it says a lot for how much how much programming and coding resources are on the web you know hey I'm self-taught yay and I found this quote from hacker news that you know instead of just two out of three developers it's really three of three developers are self-taught but only one third of them also have a degree in computer science this is great this is awesome you know the internet has allowed us to build tutorials and resources and you know different styles and different subjects and allowed more and more people to learn coding even if you know say for me this whole like super theory super abstract thinking for them my goal with hello web app the reason why I wrote the book is I wanted to encourage more people who have this like visual way of thinking people who learn better when they can see what they're building like hello about I try to avoid the command line as much as possible you're building a website first like you know you deal with models and views and everything the first thing you do is set up static files and get CSS working get JavaScript working and you have like a website so you can see what you built you're like oh I built this and then you start adding things in yeah I wrote this book for people who have this visual way of thinking and that's one of the reasons why I think my book has been so successful and it's made me excited to see more resources aimed at niches it's not just like here's how you learn programming here's best practices it's more like here's how you learn in this area I mean did you know that Python is heavily used in typography I didn't that's super cool Python for typographic designers you do Python scripting in font lab to help people build typefaces you know obviously we use Python in science frameworks like Django and bootcamps have increased the amount of people who could just jump in and start using Python to use things you know focusing on on different subjects and different niches and different ways of using Python increases our pool of beginners as a whole and increases my people that go into different places not just software engineering but you know all the different other paths would you take you know those guys that I I met at the previous previous conferences who are like I hate frameworks they make bad programmers kind of looking at it you know a world like this like there's a pool of beginners and there's like this percentage that are bad programmers and now that our pool of beginners is much larger that percentage is way is like you know same percentage but there's a lot more people there it's increased a lot of bad programmers we have and that's just like a really crappy way of looking at things I like to look at it as before we're smaller now we're larger and each piece of the pie you know we have a lot of people going software development we also have people going to have a typography we're going to startups like me documentation science you know the larger our pool of beginners is the more people go into these individual areas and our industry is going to grow and it's going to diversify there's going to be different thoughts and different like ways of thinking bring it stronger and better as a whole like one of the criticisms of hello web app is that hello web app does not make you an engineer just like dingo you know it does not teach you to get a job at Google you know and hello web app again for me is that it can teach you just enough skills that opens up all these doors to different things you can do like you could discover oh wait I actually do like programming and then jump in and work on software development or go into back end but you can also discover that yeah maybe it's not for you or maybe you want to start a startup like I did or maybe you want to you know start working in open source opens up these doors friend of mine before picon came up to me this is a good friend and he should know better but he came up to me and he's like hey can you help me my presentation it's for you because my presentation is for beginners I took a look at his presentation and it was not for me like yes it was for beginners but it was a subject that I was totally not interested in he was looking for beginners who are looking to go deeper into a certain skill but I want to go broader like I have no interest in like deep diving into one subject personally and yeah I got his permission to put this up here cuz I was like come on dude not all the beginners are the same not all the beginners have you know the same route that they're going on and I'm on a different path so what can we do better just end on some some actionable notes you know try as best you can reject this whole one true programmer thing you know again all programmers look the same do the same thing think the same learn the same like can do one thing or not one thing you know if you feel like you're the superior race because you're really awesome and like deep python or not the superior race stop now we also need people who don't do documentation we also need people who do technical writing the people who are scrum masters or designers or friend developers and we can all use programming and be equal together this one like you really should go find this presentation by Jacob Kaplan Moss at Picon 2015 embrace the mediocre programmer because just like not all of us looking the same we're having you know same learning process or the same ways of learning we also aren't all becoming rock stars it's impossible we're never like no one is going to not everyone here is going to become a rock star we're all more likely to become a mediocre programmer and we should celebrate that and his whole presentation is fantastic celebrate people who are mediocre because we all are now I've touched on this a few times in this presentation really like to see more specificity at conferences events and courses when it comes to complexity of material by that I mean you know when you narrow things into just a beginner intermediate or advanced stop that that's way too narrow you know and not a big fan of the badges I love your Python but not a fan the whole Python power badge thing you know what's going to happen when you narrow people down into four star three star two star I want to see I hope to see something like this where you can say you know I'm a beginner in this skill I'm intermediate here and advanced here like a lot of people to say what they're good at because not every beginner wants to come intermediate in something not every intermediate person wants to come advanced we can say okay I'm I'm lesser in this skill I really want to learn more in here and that's going to make us more inclusive and make you know people who are like me were really awesome their skills are still essentially a beginner feel more welcome mentorship this is huge for anyone who's jumping into tech gets overwhelmed and then doesn't realize all the different paths they're available to them so this is where it mentioned mentorship can really help out because you can tell you know someone where they can go if something in particular is not working for them I really sketched this out you know little things that I'm good and things that I'm bad at like I'm good I'm like a manager which kind of you know I can't kind of insulting to say to myself but I like to delegate I like to find a 20% that does 80% of the value I like I have ambition I'm really good at breaking things down but I am really really bad at details like design developments like that fine like those little touches terrible at them terrible a beautiful code my code is ugly I cannot remember anything without looking it up I would be terrible at whiteboarding so writing this down or helping people write these things down figuring out what little things are good at what little things are bad at will help them determine the path they should go on as you can see for me I'm not I'm not really interested nor would I ever be really good at becoming a backend engineer and I want to see more tutorials and guides are aimed at things that are like that niches little small things I want to see guide you know programming for engineers but also programming for artists programming for writers programming for people who want to build a startup and of course I feel very strongly about this you know if you're saying I can't write something I can't write a guide I can't teach someone because I myself and beginner hi I'm Tracy I'm a beginner and I wrote a book myself published it and I'm selling it on Amazon people large by and large have given me really awesome reviews and you too can write and please write if you were a beginner because you have the mindset that is needed to teach other people you know you are still like I think people who are really awesome programmers can often make really terrible teachers because they forget how hard it can be or they can forget things I don't really work in a beginner's mindset so in conclusion power of a community lives in its diversity not just things I think people can look different but also think different can learn different that we can choose different paths that we can do different things with our tech and our programming skills the beginners we teach today people we jump into tech are going to be our future programmers but also our future scientists our future artists our future managers future program product designers I hope I raised some good questions I hope I told some fun stories I hope that I've started a discussion I hope we can continue it after this session so thank you so much for having me last but not least a little bit of marketing for myself if you want hello web app hello web app calm it's almost done and I'm also working on a third book teaching design to programmers called hello web design because I'm awesome at titles so that's hello web design look calm and I have five hello web apps and four hello web app intermediate concept books here like paperback books they're really pretty come find me if you want one thank you


Joker is different because he's working backwards. He's one of the rare superstars who gets everybody involved first and then he dominates. Oh, brother, I won 15 assists, 15 euros. Oh, look up, I have 30 points. So it's like he's dominating a whole different way. Joker is really a point guard. And you can mix bird, magic, dirt, and a little bit of Tim Duncan and you make it to Joker.


Hey, this is Brett, and this 2022 Ram 2500 Crew Cab Short Box Big Horn Level C Night Edition is stock number 13519Z. I am here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck and Ram headquarters. This 2022 Ram 2500 has the Cummins 5.9 liter diesel. It's paired up with the six-speed 68 RFE automatic transmission. This one can tow up to 17,790 pounds and has a max payload of 2,069 pounds. Got those numbers off Ram's tow guide website, I'll put those in the description below. This truck has been fully-safetyed and inspected by Ulrich Service Shop. It has a fresh oil and filter change, all the fluid tab and check-in topped off, and it is 100% ready to go. I'm going to go all the way around in this video inside underneath. Start it up and take a look under the hood to give you the most accurate representation of the vehicle possible. Granite crystal metallic is the color, paint codes PAU. I shoot all my videos in 4K, so if you have HD capabilities, turn them on now because it's your best way to check out the quality, condition options, and cleanliness of the vehicle before seeing it in person. As you go down this side of the truck, you can see that the box is in excellent shape, cab and doors, no major dents or dings on there. The paint is in very nice condition, as is the front fender. If you like the video, you can subscribe to my YouTube channel, which is youtube.com slash Summit Auto. Click the bell notifications and you'll get updates on the videos I do each and every day here at Summit Automotive. This one comes with the 20-inch painted alloy wheels, has Firestone TransForce AT tires, these are LT28560R20s, and they have just about all the tread left, I'd say 80 to 90 percent of the tread left on those tires. Frame and underbody is in very nice condition. This is a clean title history, clean car facts vehicle. You get the blacked out headlight bezels, as well as the painted bumpers with that night edition. No major dents or dings or scuffs or scrapes or rock chips on there. Comes with the front bumper parking sensors and the factory fog lights. You get the gloss black blacked out grille, part of the night package, and the hood is in fantastic condition, no dents or dings on there. You get the cab lights up top, passenger side front fender, no major dents or dings on there, and the passenger side front rim, no scuffs or scrapes. Granite crystal, one of my favorite colors on the Rams. Has a lot of metal flake to it, you can see just how nice that paint is as well. Also note you get the blacked out 2500 Cummins and Ram logo part of that night edition as well, and the black step bars. As you go down this side of this 2022 Ram 2500, take note of how clean that body is, how reflective and mirror like that paint is. I take these HD videos, so if you are far away or even if you're close by and you just cannot make the trip down, but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here, there's absolutely no surprises and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that buying decision, let your salesman know that you saw the video, that it was helpful, that Brett sent you the back wheel. Excellent condition to apologize, they're a little fogged up from the cold weather. Back tires have just as much tread as the front tires, and the frame and underbody is in excellent shape. Do note that you get the coil springs on the Ram 2500s, gives you a lot smoother ride when it's not loaded up, has all the remaining factory exhaust, so it hasn't been altered in any way, and the frame and underbody is very, very nice. Get those nice black painted step bars, and that box, once again, excellent condition, no dents or dings. You get the LED tail lamps, the painted rear bumper, backup parking sensors, no dents or dings on that bumper, full towing package, which includes the receiver hitch, four pin and seven pin wiring, and the tailgate is in excellent condition as well. Once again, 4x4 Ram and Bighorn logo all blacked out, part of that night edition. Comes with a very nice spray and bed liner, you get the fifth wheel and gooseneck prep package, seven pin wiring, four pin wiring, you also have a 115 volt 400 watt plug-in right there, there's four pin wiring, and then LED bed lights, you have a button back here to turn those on and off, and that spray and bed liner goes onto the tailgate, and that bed is in excellent condition. Tailgate shuts nice and solidly, has the shock down assist tailgate, so it's never going to slam on you, and for full disclosure, this back wheel is in really nice shape as well. Down the rest of this side of the truck, once again, doors all look really good, has the heated fold-up tow mirrors, built-in directional signals, they fold up like so, they fold in like that. Inside the Bighorn Level C package gives you the black and dark charcoal cloth interior. No rips or tears on the seats, smells very clean inside this truck, I don't think it's ever been smoked in. Power driver seat with lumbar, factory floor mats, these front seats are heated, auto headlamps, power pedals, power windows, locks and mirrors.   You do get storage underneath, receiver hitch reducer, and some extra wiring hookups. You get in-floor storage with removable bins for using cleaning, you can put ice and drinks in there, it's like you have too many coolers wherever you go, or if you're not from Wisconsin, just extra space. Two USBs, two USB C's, and a 115 volt 400 watt plug-in back here.  Inside the doors are in very nice condition. We'll hop inside, check out the miles, the radio, and everything that this truck has to offer on the interior. You can see this one only has 4,972 miles. You do get the seven-inch LCD display, digital speedometer, compass display, and you can change those corners to read whatever you want. Instrument cluster is very nice and clean. Comes with the heated leather-wrapped steering wheel, cruise controls on the right, gear selector on the right as well, Bluetooth and information center controls on the left. Audio controls on the back of the steering wheel, there's your shifter for that six-speed 68 RFE automatic transmission. Comes with the 8.4 Uconnect 5 radio. You get AM, FM, and see here, Serious Satellite Radio capabilities and Bluetooth capabilities. You have all your different apps, including Android Auto, and then this is where your backup camera shows up as well. And you can see that is very nice and clear. They've made these cameras HD on the RAM, so crystal clear, and you can zoom in on your receiver hitch to get hooked up to your boat, camper, or trailer the first time, every time.  Down here is your 115 volt, 400 watt plug-in, and you get the sliding cup holders and coin tray right there. Passengers side dual glove boxes, seat and floor mat are in excellent shape. Once again, I don't think this truck's ever been smoked in, smells very clean inside, headliners absolutely perfect, you do get map lights, assist in SOS buttons, and auto dimmer. Also, home link buttons for your garage door security systems and lighting systems. We'll start it up and take a look under the hood. Actually, I'm going to show you the tire and loading information sticker real quick, as well as the VIN sticker. Always forget to do this, feel free to pause it. All right, let's start it up and take a look under that hood. Starts right up, no check engine lights or anything like that. I would personally like to thank you for checking out the video today, and hopefully from this HD video, you'll have been able to verify the quality condition options and of course, cleanliness of this truck all the way around inside and out. I think it's in fantastic shape, only 5,000 miles, so it should be, but you always want to make sure that you know that it's perfect. Under the hood, you get the 6.7 liter Cummins diesel engine base, very clean, runs very smooth. Once again, this truck has been fully safe and inspected by our service shop as a fresh oil and filter change. All the fluids have been checked and topped off, and this truck is 100% ready to go. And your mission sticker is right there. And I would highly recommend this truck from a quality and condition standpoint. I'd ship it to California, Texas, Florida, New York, wherever whoever is going to get this truck is going to be very happy with it and didn't see really anything wrong with it. And to see more pictures of this truck or one of our other 550 new and used cars, trucks, SUVs, minivans, Wranglers, halftones, three quarter tons, one tons. You name it, we got to go to that website SummitAuto.com. Full pictures and descriptions of every single vehicle from two locations, all at SummitAuto.com. And if you'd like to check out more HD videos, you can go to YouTube.com slash SummitAuto. Remember to like, subscribe, and share on this video and all the videos that you see there. Click the bell notifications to get updates on the videos I do each and every day here at Summit Automotive. In fact, in a second, we'll see a link to subscribe to my YouTube channel on the upper left. Link to more Ram 2500 truck videos like this one on the upper right. A link to this vehicle on our website on the lower left. And a link to one of our latest YouTube videos in the lower right. Click those, check this out. We're super excited to help you with this Ultra Clean 2022 Ram 2500 Crew Cab Short Box, Big Horn Level C Night Edition, and Granite Crystal Metallic. Thanks again for checking out the video. Remember to like, subscribe, and share on the YouTube channel. I really appreciate it. Thanks again.


Today, Professor Santosh Narona from IT Bombay will continue his lecture about considerations of data analysis especially for the omics data sets. Today's lecture is going to be about why basic understanding of data analysis is required. For example, 0.5 percent accepted error rate in significance used without basic understanding of data may result in false interpretations. Professor Narona will also talk about the importance of replicates and how one should choose controls which are usually one of the very important samples for the big data or the omics based experiments. So, again thinking about a good experimental design, what should be replicates, what should be your strategy for data analysis actually determine the meaningful sense of your experiments despite all the advancements in these technologies and the pace at which we could generate the data, but still getting meaningful data is not a straightforward, it is not easy. So, I hope today's lecture and based on the previous lectures, these two lectures will eliminate your knowledge and give you the concepts about good experimental designing and what should be the considerations to look for to get the meaningful insights from your data. So, let us welcome Professor Santosh Narona. He systematically tested so many possible candidates for significance and if there was a 5 percent error rate in your analysis, you would have randomly found a candidate and called it significant and we end up fixating all our energies on these one or two candidates that we get when it is sure randomness that has caused these two turnips. So, unless you have an independent way of carrying out an analysis with these candidates and validating that the important to you, it is kind of pointless proceeding further. Now, at this point if you are in the publishing game, it is very important that you notice that publications do not allow you to publish negative results. So, all these other things you cannot publish here. So, the only thing you can publish is this particular result. So, there is pressure on you to find that needle in a haystack as a positive result and publish it and that is the nature of this confirmation bias and p-hacking which pushes you into now focusing entirely your research on this particular candidate, the green candidate as if it were the only relevant candidate. So, what is the way around this? So, again something that we typically do not do is an adjustment to this. So, the only way to if there is a 5 percent error rate in analysis and 5 percent error rate is a dangerous thing if you are doing 10,000 studies, I mean I want you to appreciate the 5 percent at a different level. You take any 100 papers published which are scientific hypothesis being tested and I can tell you without even reading those papers that 5 of them have got to be false because all of them have used a 95 percent confidence level for executing this analysis and if you are saying there is a 5 percent sheer bad luck error rate, then several of those researchers have suffered unfortunately with randomness in the data they collected which means their results are going to be false. It is not that they have set out to cheat, it is unfortunate given that they are unable to reproduce their own data and they are trying they are in a rush to publish. So, the trick the way to control for this in some ways, so how do I reduce my error rate? So, if my error rate is this red portion the 5 percent, if I want to reduce my error rate I have to move my goalpost further out and that is the only solution of course now it gets hard the number of candidates you will get which pass this goalpost further out which are so extreme what you are saying is your results must be so extreme that they are well outside this wider goalpost range. So, what do you say if you are going to do 10,000 tests? Each test should not have been done at a 5 percent level instead each test should have been done at a 5 divided by 1000 or 10,000 if I am doing 10,000 tests. So, that 5 percent error rate should be spread across the 10,000 tests that you are doing and if you were to attempt that this area now is 5 over 10,000, so it has become a tiny area, but I have effectively pushed my goalposts out. So, the odds of now passing my test are much much lower and the odds of randomly passing my test have gone down and that is a core trick to the statistical analysis it is called a Bonferroni adjustment and good packages software packages for omics testing will have this as a setting where you can correct for the number of tests that you are doing and try to refine this and it is a critical thing. So, in other words one of the things you ought not to be doing in an omics framework where statistical tools are being provided to you by the manufacturer is use a default setting in a workflow you got to ask the question what are the settings which control for statistical significance and do these need to be tweaked to correct for the number of studies you propose to do on that software. There is this aspect of power of a test and what I want you to appreciate is while all the emphasis on asking is a genetic candidate is a gene candidate significant or not all of this involves in only looking at this particular curve. So, if you look at this particular curve forget the other curve for a moment if you are looking at this particular curve then your 95 percent confidence leaves out this blue area on either side that is at 5 percent the blue area would have been 5 percent, but for the sake of argument I am going to pretend as if the reality was some of the hypothesis under which this would have been a mean value and this would have been a range of outcomes I would have seen if some of the hypothesis is better and that is just for the sake of argument because now you will see something problematic happen if this hypothesis were true then this blue area corresponds to that percentage of time you are going to get your hypothesis outcome wrong. So, that 5 percent of the time we are getting a hypothesis outcome wrong under the null hypothesis what do these thresholds mean for you within these thresholds you say this hypothesis is ok I mean agreement with that hypothesis outside that those thresholds you end up saying I do not believe in this hypothesis I will go with the other hypothesis in this case h 1 if you now look at h 1 h 1 is allowed to be true only from this coordinate to the right beyond that region you believe in h 1 to the left you are you have already argued you prefer to go with h naught as a hypothesis, but do you now see under h 1 this area in purple corresponds to an error where h 1 could have been the true hypothesis you have gone with h naught therefore, as a technicality you are committing a mistake by saying h naught is true when h 1 should have been true. So, there is a mistake there is a mistake it is just like false positives and false negatives in fact it is related to the concept of false positives and false negatives you will make one mistake or the other if you were to create a diagnostic kit and you are going to change the threshold for detection of a particular measurement in trying to cut down the false positives just think about this if I take this threshold and I move it to the right if I take this threshold and I move it to the right under the h naught curve what will happen to the blue area the blue area goes down I commit less of a mistake with respect to my original hypothesis, but if I move this coordinate to the right what happens to the purple area the purple area grows if you are trying to minimize false positives in your analysis you run the risk of increasing false negatives and vice versa. So, that is a key issue. So, the headache comes about because if you look at what we have done in the previous thing we only paid attention to one curve we did not ask the question what might the other hypothesis behave like which is the case here. So, if you start paying attention to alternate hypothesis you suddenly realize that yes I might have a diagnostic kit for example, which is accurate 95 percent of the time that is what the confidence interval tells you, but what it and therefore, a 5 percent of the time I am making a mistake of a certain kind let us say I am falsely calling somebody positive. So, false positives, but what is not giving me information at all is what is my false negative rate and for the false negative rate you have to be looking at other curve and this beta. So, in other words you want beta to be small you want alpha to be small you want beta to be small 1 minus beta is called the power of a test and it is a good practice to ask whenever you claim that something was a significant candidate a significant target do not just tell me how significant was that result tell me how powerful that test was. In other words tell me what is this value beta that I might therefore actually have a lousy test. So, this power of a test is a concept which most again it is there it is buried somewhere in software typically as an option for you to report, but it is not something researchers are in the habit of reporting. So, when somebody tells me I have found a candidate and in fact, I found a short list of candidates which are all significant what they are not telling me is how powerful was the analysis what they are not telling me therefore is what was the probability that they got the analysis wrong the other way around while they are telling me that they are confident to within 95 percent what they are not telling me is whether this was so bad that this was 20 percent or 30 percent or 40 percent the purple area. If one of these values is greater than 10 percent your study your analysis is already in trouble. So, both alpha and beta both these shaded areas cannot be large because they are both errors in your interpretation and here are cartoons which quickly improve the point. So, if you are trying to distinguish two hypothesis and your two hypothesis are so similar to each other therefore the effect size is small you will have such an overlap between the two predictions coming out of the two hypothesis that you are unable to discriminate and say which hypothesis is true you will not be able to do that.  So, the spread in here is thin if your curves are thinner the overlap is reduced compared to here. So, you in a nutshell you want more replicates of any analysis that you do otherwise your errors are going to be large. If you are going to make your confidence into a large if somebody says that really ought not to be talking about 95 percent confidence you need 99 percent confidence. Then the immediate outcome to you is the moment you move your error sorry your thresholds further out this purple area which was small here has become large here. So, that that worsens the case. So, there is no clean solution here the moment you try to improve one sense situation something else in your analysis is going to worsen somewhere else and my point is to prove that everything is interlinked and you therefore ought to be talking about significance of a result as well as whether this is a powerful test being done. This is a famous study and it is really worth looking this up on your own later on. Amgen is one of the two top biotech companies in the world. They make that there is a dominant manufacturer biopharmaceuticals protein drugs for the most part and while initially they worked on things which are already discovered in research labs increasingly they have been doing their own research trying to find out what is the next generation of pharmaceuticals that have to be manufactured. They obviously keep track of literature. So, one of the things they did was they took 53 landmark papers published in the top journals in oncology and hematology. These are publications coming out of MIT, Caltech, Stanford, Berkeley the top labs the top universities in the world 53 and their logic was these are all published in the top journals. Let us repeat these results in-house and if as is published these candidates are good candidates let us get into the business of manufacturing these candidates that is where they were going. So, out of 53 they could reproduce only six papers and this is MIT, Caltech, Stanford we are not talking of some small tiny college somewhere. So, what is going on? It is not that people at MIT and Berkeley and Caltech were cheating. It is not that they were deliberately cheating, but there is a situation where the results coming out of even these top labs cannot be reproduced. So, why do you think they cannot be reproduced? In the six cases where the results could be reproduced when you look at carefully what happened, attention had been paid to doing the right controls in the experiment. You need the right controls. You do not make claims about results based on only a test case. You do the controls the reagents were reproducible and this you will realize most of you are doing experiments by lab experiments reagents especially in the immunology space are had to obtain in a reproducible fashion antibodies in particular batch to batch variation exists and you are unable to reproduce results. So, in the six cases there was the ability to manufacture these reagents reproducibly and that made a huge difference. The investigators were not biased. They did not they were not trying to push for a particular insight or an outcome and importantly they were honest about reporting all their data. So, you remember that straight line plot where I deleted the mid-section of the data and then you claim a better result than it actually is. They were honest enough to claim all or at least report all their data which meant that when somebody tried to reproduce it they also saw some bad data equally back to what these guys had found. It is a surprising result. It tells you to what extent there is pressure on people to publish positive insights even at the top labs and the moment this study came out and when this pharma company published this insight many companies started paying suit. So, Bayer did a similar study. They looked at 67 targets published in the literature Bayer is another big pharma major and out of 67 they could reproduce 14 results which tells you this is a serious problem. Now, this problem goes beyond just statistics alone. So, you can argue that a lot of that is bad luck with data not being reproducible because of the one time you do this experiment with that one material and inherently it is not a reproducible experiment. But it also reflects other aspects of poor design. So, here is one experiment of poor design where you are screening for certain drugs to do with epigenetic control in a glabablastoma system and the screen which was done was an in vitro screen. So, there is two ways finally, this got done and in vitro screen you are using basically relying on an RNA interference kind of protocol to try to identify targets. And the in vivo screen where this is the in vivo screen where you are directly loading these cells onto the brain and then looking for changes in function. The in vivo screen and the in vitro screen have practically no overlap in terms of what's up regulated and what's down regulated. Which means if you had just done the in vitro experiment and you had generated a bunch of targets and you had then proposed to now design drug candidates against these targets, you have a huge amount of money. There have been several such things you start going through literature you will see these things you know it is not something conventionally that journals published. So, this is published elsewhere some of these ironically are published in blogs they are not published in a cell in journals. They have for example, been studies on looking at gene signatures predicting the response of breast cancer to chemotherapy. And in this case the problems are even more ridiculous. In this case and this is another strategic problem with handling large data sets. One of the things that happened here is when the researcher and they finally traced it to a student. This was at Duke University. When this data set omics data set was finally taken into a spreadsheet and subsequent analysis was done and this data set was sorted many columns got sorted one column did not get sorted. And now every gene is being assigned or all these numbers are being assigned to the wrong gene labels gene IDs. This was one mistake which happened very early on in a rush to carry out this analysis. Nobody followed that up and it went through an entire analytics pipeline by informatics. Drug candidates were created, probe sets were created. Three clinical trials were started on human patients on this basis and a huge amount of money was spent by NIH and running clinical trials you will appreciate a billion dollar experiments sometimes. And millions of dollars later in this case because this was an early clinical trial early stage clinical trial. Millions of dollars later when the Duke researchers went back to NIH and said our results are not reproducible. These candidates despite the bioinformatics don't seem to work in reality. And the hard question got asked why show your lab notebooks. You go all the way back and you look at the printouts of these spreadsheets and suddenly realize one column has not been shuffled and sorted and comes down to a simple IT mistake which has wasted a lot of time and money. These are clinical trials. It could have been worse if people had died as a consequence or been seriously hurt as a consequence of the trial because you're actually playing around with therapies or proposed therapies. It could have been much, much worse in terms of how this would have hurt the university and the researchers. So I'm giving you a bunch of links here really what I wanted to appreciate is leaving data analysis and statistics as an afterthought to a bioinformatics pipeline is a dangerous business. Remember that phrase I came up with. Hypothesis after results are known. There is this philosophy that getting the data is a hard thing. Therefore all the effort goes into getting the data and once you get the data you say you'll actually get down to doing the science. But really it should have been the other way around. They should have been a robust experimental method and a computational method identified before the experiment was even done. And then you report whatever results you get as per that methodology. In fact, the whole publishing paradigm in the omics space is going to change now. And for example, journals like Nature are already starting to follow an altered publication protocol where they're so concerned about the fact that data was generated and hypothesis are created that they're saying look, the entire review process must now happen in two stages. So in stage number one, before you even do your experiments, you actually try to publish a paper and this is a weird thing. You try to publish a paper and you submit to the editor a protocol that you wish to follow. So you say that look, I wish to work in such and such a system. These are the experimental methods I'm going to use. And this is a statistical analysis that I propose to do once I get this data. And you want a reviewer system, a bunch of reviewers to look at this protocol and tell you in advance whether this is correct or not. Okay? Why? Why would you do this? Because remember, we are under pressure to publish positive results and not negative results. So how do you take away that pressure? So one way to take away this pressure is to say let your methodology be accepted by the peer group, the editors and reviewers. And at this point, regardless of whether your results are good or not, they publish the paper. So you get guaranteed publication of this paper after a protocol is approved. Okay? So therefore before you even publish, you talk of how you're going to assess your data sets. Okay? What is the hypothesis in other words? What's the protocol that you're going to follow, both experimental and computational? And what's your detailed analysis plan of how you're going to interpret which gene sets are important to you and which are not? Okay? And at this point, if they are of acceptable standards, you're guaranteed publication. And afterwards, you publish the data that you actually generated, both good data and bad data, because now there's no penalty if you publish bad data. One argument against this has been that if you're going to allow people to just publish your protocol, in fact have to announce my analysis protocol in advance, does that allow you to do more creative analysis later on? Because you're forced, you're locked into some kind of analysis already, because that's what got approved. But the reality is, okay, as long as you label this extra analysis that you do, in fact it's called a post hoc analysis, as long as you flag it in your publication that this was done afterwards, it's still acceptable to the peer review community. So this is a game changer in the way the omics, okay, industry is going to potentially function down the road. So at the moment it's small, it's probably like 30 journals which are signed on to this kind of a paradigm for publication, but it's a community which is so concerned of that what they are doing is not a participle, that they're willing to collectively go by this protocol of publication.  So if you're not, if it's not a good idea to analyze one gene at a time and ask what's important or what's not important as a candidate, what else can you do? One of the things that's lost to you when you analyze one gene at a time, basically in any system when you analyze one component at a time, what you lose sight of is what are the linkages between the components. It's kind of like saying in a car, I've got each component and I'll try to separately study each component and if you were to study each component, yes you know precisely how a brake works, how an accelerator works, but what you don't know is how the car works given a brake and accelerator. You don't understand how the system works. And clearly there's some interaction between the brake and the accelerator which finally governs how the system works and these interactions are lost to you if you study things in isolation. So the only solution therefore in a computational manner is if you take things into a multivariate mode, don't study things one at a time, study the whole data set at one shot, not one variable at a time. It turns out there are many ways in which you can do this. I'm just throwing a few buzzwords out there. Some of you will be familiar if you've done courses in bioinformatics, you'll be familiar with things like clustering. Hierarchical clustering is something that for example phylogenetics or multiple sequence alignment methods would require. For example if you're building some kind of a tree or species or how genes have evolved over time. So these are all approaches where whole data sets get interpreted at one shot.  Some of these may get built into some omics tools but they are more likely to be present in some statistics toolbox in which case you have to make the effort to go to the toolbox and try to figure out what's going on. Now trying to find patterns in multivariate data sets can be problematic for many reasons. For example go back to this omics question of you have carried out an experiment control versus test case and you're looking for fold change. You will ask the question what's been, you would normally have asked the question to what extent does something mean upregulated or downregulated. If something's upregulated on a log scale beyond let's say two fold that's got to be significant for you. So you're going to make that kind of an argument. Now one of the problems is why is two fold an important cutoff and not some other lower cutoff. And I can give you for example I can give you a simple kinetic argument for why a value of two is arbitrary. Think of two branched pathways. A is going to be going to C, A going to B going to C and A is going to P going to Q. So two branched pathways. A, B, C, A, P, Q. These let's say are metabolic pathways. There's some metabolism going on. There's branched metabolism at A. Something's going down one pathway to C. Something's going down to Q. If you were to look at fold changes so if I upregulate something at A that upregulation of an activity at A cascades into some change for B and some change into C. It starts impacting changes for B and C and similarly for P and Q. Which guys would you expect to be the most upregulated as a function of fold change at A? If I have a fold change of A as two fold what can I expect at B and P? B and P can go up five fold. Because I have typically a transcriptional regulator being toggled a little bit. That effect starts impacting some effect genes a bit more and that goes further down so very quickly. This is what I was saying. So if I've gone up two fold here and you're trying to say this is significant then it's typically the case in a metabolic pathway that this goes up something like five fold and this goes up something like 50 fold. Because then products start accumulating even more. So and why is nature being this way? Because it doesn't make sense to directly push this up 50 fold. Because then you lose control over. You lose fine-tune control over how things propagate down different pathways and you want to control the expression levels of each intermediate to various pathways. If I asked you to find out those those species which are most upregulated you'd have told me C and Q are most upregulated because they have the highest fold changes. Therefore if you were to cluster them if you didn't know better if I didn't draw this pathway structure and you simply told me this went up 50 fold from a spreadsheet and this went up 50 fold from a spreadsheet one in one one temptation at this point is to assume this is a relationship between C and Q. That C and Q are both path part of let's say some operand and that's why the whole operand has gone up 50 50 fold. When there is no connection between C and Q but the connections are via this. So if you wanted a cluster if the question was being asked what's a cluster of effected genes which are going up as a response to whatever intervention you did. The cluster was not C and Q as one cluster and B and P as another cluster because they would be clustered on the basis of fold changes remember that's not a cluster what should have been a cluster this should have been a cluster and this should have been a cluster because there's a more obvious biological explanation as to how there is a cascade effect in terms of upper down regulation as you go down pathways and you can immediately see therefore that any clustering approach which now clusters on under an assumption of fold change alone is problematic. If you're going to start grouping together candidates or targets on the basis of expression levels alone that's a problem. So you've got to be looking for relationships. So what's a relationship? What you want to start looking for is if I move this up is something else going up is something else going up is something coming down is something else going up and what you want to see is in every patient across every patient across every disease condition if these things are going up and down in coordinated fashion then there's something going on between this bunch and that bunch deserves to be clustered. This is some genotypic relationship that you're now seeing across these species because they're ultimately related by one physical process. Now that's a subtlety because I'm now saying I'm not so interested in the raw magnitudes of these up and down fold changes that's not important to me. What is more important to me is whether the level of this goes up and this goes up whether this goes up to fold or whether it goes up one point five fold does this go up across all patients and when this goes up does this go up and those kinds of pairwise relationships is what I start looking for but what is what what do we call those space wise if I were plotting a line between x and y you'd call that pairwise relationship a correlation coefficient that r square value that I showed you a while back. So here's suddenly an insight instead of simply saying let me look at fold changes and ask is a fold change important and then trying to identify targets on that basis. Sometimes it's more intriguing to ask the question are correlations between pairs of candidates important and is that telling you something and now the reason I bring that up is if I were to somehow plot this data okay if you look at the previous slide these are things where clusters are based on magnitude. So the fifty fold change guys are all together the five fold change guys are all together and so on but if I wanted to look at correlations that's a different model okay. So correlation structures are usually way more important in biology than simple fold changes because that fold change could have occurred with sheer bad luck that fifty fold for example could remember all a discussion of randomness fifty fold could have been because of bad luck. So instead you need correlation based analysis if you're talking about hierarchy is a other call a species correlating amongst themselves in a hierarchical analysis. So choose something based on correlation analysis okay. There are methods out there for example on gene set enrichment we say that we build clusters based on which genes are together. One statistical tool which is what I'll end up with is something called correspondence analysis where you've looked at how things cluster together. It's something you've done okay for a series of data sets in this case for a medulloblastoma analysis across different types so what you're seeing are different patient types and you're looking different patients and samples and you're looking for what's the relationship between them. These are all exploratory methods where somebody's saying we have got so many tissue samples across so many patients. Can you find out how many subsets of medulloblastomas you might find and where this is going is nobody knows the cause okay what how many subgroups might exist with this particular disease condition and then later on you ask the question what what could be causing or what what is the signature for that subgroup with which genes are signatures for each subgroup but the question as to how many subgroups exist in the first place is itself an open question okay so if I were to do hierarchical clustering I'll find this kind of analysis if I were to use something called correspondence analysis that same data is plotted it's literally the same spreadsheet but it's being plotted different ways and I wanted to appreciate that there is no one perfect way to do this which is why a better analysis try different ways now in this case an interpretation is slightly different so here most of you are familiar with how to interpret this two nodes in here are very closely related related to something else over here but as here okay you're essentially asking whether things are far away from the center at the center you've got an average condition for a tissue sample as you move further away these are all patients these are all patients as you move further away you're deviating from the normal okay so distance from the origin matters and if you're moving on a diagonal away from the origin all these guys on a diagonal are related so my notion of a cluster is no longer one nice cloud so spherical cloud so this group of patients is a cluster out here is some other cluster and there's another group of patients behaving differently which is not which kind of shows up yet there's one cluster here is one cluster here there's another cluster here of patients okay so different ways of interpreting this throw different insights out what was very useful about this method was a fact that it allowed allows one to not just plot patients but you can also on the same coordinate system plot genes so remember your data set you've got different patients or different sample conditions and for each sample condition based on your omics okay for throughput you have so many gene expression levels or protein expression levels same logic and now I'm plotting just the genes and asking these are all the normal genes housekeeping genes probably okay marginally changing the expression levels and ask a question which genes are sitting out of the extremes with genes radially a furthest away from the origin those genes are probably doing something interesting in terms of having their expressions always go up or down based on okay a correlation with other patients what's being plotted is not raw magnitude but correlation coefficients okay so these genetic candidates are all related to each other somehow and one insight by the way is that when one goes looking in these gene candidates are all related to one particular signaling pathway and no surprise that they are all nicely correlated with each other one guy went up so many other genes responded to that signal and went up and down so they all show up as a cluster on this axis another bunch of genes are clustered around here and so on and what's very powerful about this analytical procedure is you can then superpose this on top of this and you then ask remember the clusters of patients we had there's a cluster of patients here and other cluster of patients now what are genetic signatures so these genes over here are signatures okay specific to this cluster of patients okay now what has happened here is rather than test one gene at a time and we know the problems now of testing one gene at a time by sheer bad luck five percent of the time we get things wrong that can mount as an error rate if I'm doing ten thousand analysis instead the entire cloud of data the entire metrics of data is being analyzed when you think about is these are columns my patients are columns my genes are rows in a data set so I'm looking at columns of patients I'm looking at rows of genes and I'm looking at the two things super imposed and I'm looking at all my data somehow projected at one shot and what I learn from this methodology is that a subset of genes here is associated with these patients a different subset of genes is associated with a different set of patients and so on and I already found my clusters and my markers for the subtypes okay so it turns out that in the statistics world at least in the multivariate statistics world the appropriate methodology for statistical analysis of this data set existed just was a case of being a little adventurous and going out there and trying to find out could was there a method which would more accurately ask answer this question of what were relevant targets and not simply trust the least complicated statistical procedure and the least complicated statistical procedure was just one gene at a time and that procedure is prone to a large number of mistakes okay whereas a more robust approach which looks at all the data at one shot in a multivariate mode captured relationships very fast we go looking it's turned out there are nice insights about why these genetic why these genes were part of a signaling pathway and how defects in one particular gene could escalate into this condition that has led to better science the same thing has been done later on okay again for proteomics data for different for classifying different types of infections from blood so if you're looking so I'm not sure you can make this out other than the colors here but these are healthy patients blood from healthy patients and they're a nice cluster on your own okay you're looking at falsiparam malaria you're looking at vivax malaria and you can clearly see there's a differentiation between vivax and falsiparam that shows out when I just cluster this data at one shot so we're able to therefore fundamentally differentiate falsiparam from vivax as a other malaria type and in fact we've gone further beyond this to ask is there a differentiation for example from leptospirosis which are all conditions that you would normally see as blood infections causing a high fever and so if somebody wants a rapid diagnosis here's an approach which does this and not showing all the data here but you're seeing a subset of your gene candidates and clearly these gene candidates are capable of differentiating multiple clusters so multivariate analysis it's not a question of one of these genes being analyzed at a time in fact you go the other way around you analyze all the data at one shot on one plot ask which gene subsets are important and then go and ask for each individual gene why did it turn out to be important you don't flip it the other way around and ask each gene are you important or not and then try to make a story out of it instead the whole data set gets analyzed at one shot a subset is chosen and each one is reconfirmed as being important one at a time okay so I'm not expecting you guys to turn statisticians overnight but this is more in terms of being aware that there are methods out there and the several other methods out there which improve the quality of your analysis so in a nutshell there are several approaches and it's a democratic philosophy which is don't trust one method don't trust one voter you trust many people to vote for a given candidate and if there are independent statistical methods which are all seemingly voting for the same target then you've probably found a target if one method alone is talking about a target then it's probably bad luck and surely not a significant target okay so that's another insight to take from this so okay I stopped there so today's lecture I hope you have learnt about the errors created due to the lack of knowledge and understanding about the p-values you also studied how the bone pharaonic corrections can help in reduction of false positive and false negative candidates from the data sets you also heard the role of false positive and false negatives in search of potential biomarkers with increased sensitivity and its specificity I hope it also reminded you Dr. Josh Lebed one of the previous lectures about a good biomarker and considerations for biomarker discovery programs so again you can see that you know different experts have same opinion about the experimental design how to really find the right candidates the right targets could be potential biomarker or the discovery targets especially sorting out based on the false positives and false negatives so I hope these two lectures have made you much more aware about the need for the experimental design and various crucial considerations in data analysis but before I close let me give you the overall summary of all the lectures which we have covered in this course so we started this course from the basic microarray technologies especially the nucleic acid programmable protein array and the leading experts in the area Professor Joshua Laber gave you some very interesting lectures about the basics of these technology as well as different applications with more focus on biomarker discovery program in various diseases we then learnt about how to use NAPA technology for a screening of various auto-antibodies in different disease conditions or use the same technology platform for drug discovery screening we also learnt about how to use these technology platforms for protein interactions and looking at various type of protein modifications so various these examples these applications have brought in your horizon that these technologies could be used for identification of biomarkers the therapeutic targets and for the functional proteomics based screening we also got a chance to look into applications of other type of array based platforms especially the reverse phase protein arrays and also the considerations of making good arrays and making good slides by doing good type of printing then different type of applications of purified protein arrays using few projects were shown to you directly with the demonstration sessions from my research scholars in the laboratory where you learnt about some examples of malaria and the cancer research how it could be beneficial by employing the protein microarray based technologies next we learnt about very briefly immunoprecipitation and the use of the advanced mass spectrometry based technologies of course we did not talk too much about mass spectrometry in this course because that was not the scope of this course but this is one of the very promising technology which is helping now entire field of interactomics or entire field of proteomics to say for various applications so of course you should try to get more advanced training in this area but at this one of the application we try to give you emphasis that IP followed by MS is a strong platform to identify the potential interactors during these lectures we also try to give you the idea that different type of label free biosensors are very important while label based technologies may have some bias for what the signal looks like is that a real signal is that an artifact you have to negate many of the false positives many of these false fluorescence signal those possibilities in these experiments but the label free sensors label free technologies have tried to overcome that and look for just the biomolecular interactions in its original state so trying to avoid many of the confounding factors which one may observe in routine microarray based technologies so I hope technologies like biolayer interferometry PLI surface plasma resonance based technology like SPR and micro scale thermophoresis technologies have really given you the broad idea that many of the label free biosensors could also be used for biomolecular interaction studies along with these technologies of microarrays and label free biosensors one of the latest advancement in the entire biomedical field is about next-generation sequencing technologies and these sequencing technologies have immense applications for the entire genome sequencing to RNA sequencing to variety of applications and we try to give you at least some idea for what can be done using NGS platforms with two of the leading industry key players and their application scientist from Illumina and Thermo Fisher to talk to you about the latest advancement in this area as well as the possible applications which could be used on these technology platforms then we also had interaction with one of the leading scientists and a clinician Dr. Sanjay Navani who talked to you about another mega project of human protein atlas and the very important role of India in doing the pathology atlas project and the associated challenges of this journey and the major outcomes of this project so all of these rapidly evolving technology platforms have immense applications in life sciences and transitional biology they also provide a much comprehensive picture for better understanding of the crucial physiological processes in systems approach so I hope these lectures various discussion points have really made you aware the pros and cons of designing these experiments and using the technologies choosing the right technology for your given experiment I hope these weekly assignments and live interactive sessions were helpful and you enjoyed attending this course as much as we made efforts to teach you this course and these advanced technologies thank you very much