(Reminder that all resources I mentioned are linked in the description!) Thanks for watching everyone! Let me know what your goals for this year are below! Mine Are: - Do a project with a Generative Adversarial Neural Net - Enter at least 1 Kaggle Competition - Do at least 4 projects and share them on RU-vid
Been waiting for this video!!!! To anyone reading this, if you're looking to break into Data Science in 2022 I 100% recommend subscribing to Ken's channel and following his advice!! Back when COVID-19 started in 2020, I was looking to break into the field and I had no experience, no projects, and I didn't know where to start. 6 MONTHS after subscribing to Ken's channel and soaking in all of his advice through his videos, I got a job as a Data analyst. I'm living proof that this channel is 100% all that you need to get started. Thank you Ken and & I can't thank you enough for the doors you have opened for me.
I used your project-based learning approach from last year's video to learn data science. After 15 months and 700 hours of learning (no online courses, just individual studying), I landed as job as a mid-level data scientist in financial fraud detection. Thank you! This video is spot-on. Especially your emphasis of building a roadmap and systematically updating it as you grow. 2 not-obvious study habits which I found helpful: 1. I kept multiple projects open so that after I went through a topic (e.g. feature selection), I could implement it in multiple projects. 2. For each topic in my roadmap, I kept a date of when I last implemented the technique in a project. This helped me identify important topics which I had covered a while ago and might have partially forgotten, so that I could refresh my knowledge/skills.
@@CabralNick You could learn Excel and Power BI if you want to start there. But Excel has limitations and you'll have to learn Python eventually. So you could just skip Excel entirely and go straight to Python. Find out what works best for you!
So I have decided that 2022 will be the best year of my life. I've started learning Python on 1st day of the year and up till now I have covered most of the basics and made some simple programs as well in last 20 days. So far it's going good. Thank you for this video. I wanted to ask, when should I start practicing on Kaggle now? I Study around 1-2 hours daily after my job and 3-4 hours on weekends...
Awesome! I think it will be a great year for you too! I recommend starting before you feel ready. The micro courses should be digestible for you even now.
This video is extremely accurate. I have had such a hard time learning how to be a data scientist. I am in a MS in Data Analytics program and feel that doesn't really provide any real direction. I don't know what I don't know and that keeps me from learning what I need to know. Seeing the path helps, thank you. I would like to say I have struggled to learn R and I seem to be getting SQL quickly and I think this will help my confidence. Getting one language down can help to learn another, just from a confidence standpoint.
Hi mate, can I just ask, are you learning R by using the Tidy Verse collection of packages or just learning base R. If you aren't taking advantage of the Tidy verse, I highly recommend as compared to base R it is very intuitive.
Great advice on typing out other people's work. Myself as a beginner it's easy to get flustered on projects as some solutions/tools I have not yet been exposed to. By looking up other's solutions/work and typing them out it really helps me to understand the anatomy of the code. Furthermore, sometimes the solutions don't work as I am using python 3.10 and so I have to work out and start fishing around for the latest 'iteration' of the module/code and that actually helps to drive home the knowledge.
This updated video is epic. Your pioneering "If I had to start over" video had inspired so many similarly named videos in the tech youtube space. Certainly agree with you on the importance of coding and how that opened up several doors of opportunities in accessing "power tools" for the data science learning journey. 8:01 Also thanks for the mention 😆
Thank you for sharing your experience! I just got an internship to be trained as a data analyst and learning Data Science basics. I felt so lost as a person in their mid-thirties feeling like my previous Psychology BS degree with English/Math minor was worthless. Your video has given me hope that I can obtain this knowledge with a bit of planning and goals.
Boss Ken.. 6months on the go... I consider I'm a good learning path. I'm starting with statistics (19hrs tutorial video), then I will take up math to Excel/python... Thank you for this
Thanks, Ken. I have started learning myself Data Science, fortunately, I have a programming/math background. and so far I have found the going reasonable all though it is an effort in time. My question is this is there a place for a part-time / freelance Data Scientist? I find the field extremely interesting but I doubt if that is all I want to do. Also, do you know if c++ is still used in Data Science? Kind regards
Yes! Definitely room for freelance in this field. You do have to build up credibility first, so it can be a bit difficult to get your first client. Not much C++. mostly python and R
"When beginning with this projects, they don't have to be original. You can go through the exact same analysis that someone else did, and still learn something. But typical learning session could just be you having someone else's project on half the screen and you typing it line-by-line and running it on other half of the screen. As you do this you can change the parameters, you can experiment with different visuals and see how it works as you go." This one what i've been thinking for. i feel like i do nothing if i see someone's project and copy it to my notebook, but you just said it and i totally agree. we can experiment it with changing the code, maybe we use a different way to clean the data, or just define a function that others use repeated code, (a.k.a use DRY concept), and so on. thanks Ken Jee, it really helps me out.
Hey, I have started my journey on learning data science. And, the math is what I find the most overwhelming, so much that I almost dropped the idea but curiosity gets the better of me and keeps on track. Being from non CS background, I feel really lost and tired when it comes to mathematics involved. Could you please share the maths topics and their sub topics that I should focus on and get as proefficient in it. I will be absolutely grateful if you get a chance to share the same and if any resources that I should refer to.
@@KenJee_ds thank you so much for responding. I already had the article from trithajyoti sarkar bookmarked on my medium. I am glad that I was following the right resources. Hopefully this helps me get through Once again, thanks for the response and all the amazing content. .
I would recommend getting a research paper and finding a friend who is good at reading research in Math to go over the equations in the paper with you. Personally, I really don't think the math in data science is difficult. It's actually mostly basic algebra, statistics and might go into entry level calculus. Bayes might get more complicated, but most deep learning models don't use bayes. You probably know more than you think.
Ken, what I have found is all these online projects / learnings mostly gives 10 variables and ask to predict a 11th one. But in the real world it’s very difficult to goto that level of identifying a problem and later identifying a approach to solver a problem . Until now I have not seen any course teaching that. Your advice on this ?
I strongly recommend Think Big: Take Small Steps and Build The Future You Want by Dr Grace Lordan. A great book to chart your own successful career, be it a data science career or any other career. It gives you a wider perspective on the activities you want to do in your life and career.
This has helped me tremendously! I'm a database coordinator and I feel so clueless at times because I don't know enough about this subject. I will definitely take your advice about setting smaller goals. At first, my goal was to take all of these courses, get certified and that would be it. But, now I know that this is a process and learning data science is continuous. I now have a more clearer picture of what my path should look like. Thank you so much! You just gained a sub!
Hi Ken Jee, Hope you're doing well! Would appreciate your thoughts when you have a free moment. I am in the US, NYC area, looking to dabble in Data Analysis specifically for applying to Financial Planning Specifically for visualizing data and using Data, Factor Analysis similar to PortfolioVisualizer when looking at stock market return data series, with the longer-term goal of eventually simulating systematic stock market factor-investing strategies. I already have a strong understanding of the Finance Theory behind these quant strategies, however, I don't have knowledge of the Ddta side. I'd like to acquire that knowledge in order to apply it in my own projects and in mimicking others'. Can you provide some direction? My current assumption is that R would possibly be more suited and in demand than Python for my specific industry. Unfortunately I'm not sure what specific topics I would want to investigate in order to gradually expand what I can analyze within stock market data. Thank you in advance for your time and assistance!
I generally think python is better for quant finance. I would check out 365 Data science, I am pretty sure they have some coding / finance related content. I also have a discount code for them in the description. Full transparency it is an affiliate link. Other option would be kaggle.com which is free but less specific to finance
I am currently a Firefighter/Paramedic and am completely over the job. It’s time for a switch to better provide for my family and live a better life. Does anyone have an opinion on Practicum’s boot camps?
I think bootcamps can be successful if you currently have the funds and are willing to take an analyst job. I would take some online courses to (1) make sure you enjoy / are suited for the work (2) get ahead because the bootcamps move very fast
As someone with an artistic background and amateur interest in many scientific fields, am I totally off-piste with looking into Data Science if I'm interested in moving towards a path of focusing on the visualization of different data sets? Is what I envision even a thing? lol. Any advice appreciated
my Roadmap: python Kaggle project learning by imitation learning math concepts while doing projects Learning algorithms after learning probablity start doing my own kaggle projects based on random datas i get practice projects untill excellent progression nearing perfection take up either sql or image processing or probably cryptography??
I am just researching this subject and trying to understand.... What I learned is Data Analytics gradually merges into Data Science the more you incorporate probablity/statistics and math algorithms.... Does that sound accurate? So get a solid foundation in Data Analytics first . It will be your strong foundational base.
Thanks for watching Wayne! it is a little more nuanced than that. I think data analytics merges with data science the more you try to use algorithms to predict rather than to describe. I hope this makes sense! Data analytics is definitely a strong foundation first though
Hey Ken. What do you think about the Open Source Society University's self taught data science curriculum? I'm just starting my Junior year in college and wanted to start learning data science.
Thanks Ken, I took off with python basis last two years, within the space of 2 months, I was already in Pandas, and doing the Jupyter thing. Anyways everything poured on my face, and I just gave up. With this video I saw my mistakes. Roadmap. Starting with the basis and mastering them. Then you proceed. Spend as much time as you want to learn the basis. When your is strong, your building will last forever. Thanks again Ken. 🇳🇬
Nigerian here too. I took Python serious at the start of this year and I intend to code everyday for the whole 365 days of this year. If you want a coding buddy, Lemme know. For accountability purposes
Reeatching 5 months later. I failed to learn python/pandas. Can I change for R? I try to start a few days ago, looks good to me. Ken, to be honest, I prefer Java for programming. I am a computer science student. I use Linux and just open source software, I recuse pirate software.
Hey Ken, thanks for this video! I have a question though: how do I learn the math part? Are there any courses for that? I already have a background in coding
Thanks for watching! There are plenty of courses on Udemy, 365 Data Science, and some resources freely available on RU-vid. I highly recommend StatQuest!
If it's related to ML mathematics you can refer Khan Academy Videos | ISLR book | Deep Learning Book by Ian Goodfellow ( only the math part for concepts) Hope this helps you.
Data science is a practice, but there are some concepts you need to learn to just get started. Reminds me of that famous saying " How do you get to Carnegie Hall? Practice practice Practice."
I just finished and submitted my first submission to a Kaggle competition and am learning python on Kaggle. My goals are to create a more optimized algorithm and learn python and basics of data science
Totally new to data science, have not even started learning anything as yet- reason why I watched your video. Ps very enjoyable. Hereafter, I'll remember what you said: write down my goals, but keep evolving them as I learn
hey ken! i'm a freshman double majoring in cs and stats - also pursuing a data science-related career. just thought you should know that i as a college student find your channel extremely insightful and valuable. wishing the best for you and your content coming into 2022 :>
Hi Ken, kudos for you clearness about how to get started with data science! How about the age to start with? Do you think that middle age people (40's) who come frome a very different environment (let's say life sciences for example, with 0 maths or statistics) are capable of begining the journey and landing a data science job without spending many years to be ready to apply for one?
Looked at several analyst roles in business such as those related to sales in Insurance companies and retail, they dont seem to require python at all or that much. Whats used is Excel, Tableau / Power BI and if more advanced SQL. Why the focus on Python and R when these roles are likely to be those used extensively not by analysts but senior level Data Scientists? Is this the standard in Tech or Big Tech FAANG roles? It appears for a lot of companies these higher skill roles are not used in their daily usage of data for analyst positions in their actual work. Maybe its an HR thing requiring higher skill levels on paper like requiring an MS for junior data analyst roles, tsk...
Really good observation here. For data analyst roles, bi tools & sql are the most important things. Programming is a plus and can help you get ahead. This content is a bit more for people looking to go right into data science. Some overlap, but definitely different priority for coding
@@KenJee_ds Thanks, yes kind of a warfare to look better on CV. Not to fault them if there are many applicants for a position they may get the most skilled with or higher qualification even for less senior roles. Nice videos, just getting to watch the channel. Cheers.
my main issue is: I work on making reports on excel. Right now i am embarking on a big one-man project to make more reports on excel. Completely on my own, I am the only report making guy on my team. I keep thinking "I bet I could do this better if i was a programmer", but I just... keep finding solutions to my problems in excel. And just keep default-ing to excel time and time again. My motivation to wanna learn to program is this blurry idea of "I bet a programmer could do this better", but my concrete idas of what i wanna do I always find a way to make it work on excel, I am very create on my problem solving in excel. But I don't wanna stay in excel. I know progress will crush me, I will become obsolete, any minute now. I need to move forward and learn proper data science. I just struggle to get motivated cuz all my projects end up working just in excel.
To be honest, a lot of our work can be done in excel. The problem is when we run into problems where it can't. The easiest one I see is when you start dealing with larger datasets (>1,000,000 rows). There may be some ways around this now, but this is where I see programming languages really shining. There are also bottlenecks with excel when you want to run algorithms outside of regression or making interactive dashboards available online. I think maybe trying a project that would fit into one of those categories could help you stretch beyond your excel skills a bit. I hope this helps!
@@KenJee_ds I work on the HR department that recruits directors of our firm. We handle not more than 500 recruitments a year, so I doubt I will ever see a database of over a million items. The running of algorithms... the thing is, there may be awesome things to do with that, but I don't know what that is. See, that's part of my issue, it's not that there ain't anything worth doing with python on my line of work, is that I don't even know what I could do, like "I don't know what I don't know". BUT, the online dashboard idea... mhmm... yeah, that would be cool, I mean, as I said this is basically a one-man project, I am given a lot of leeway on how to do things, and it gives me plenty of room to "show-off". Doing something like an online dashboard could be really cool. I have no idea how to do that, but hey! you at least gave me an objective now, guess I will research more on that. Very appreciated!
In my course on the ML process there are a few small projects to help get the ideas. The project aren't exactly built into the curriculum though. I still generally think it is worth it
@@KenJee_ds thanks for answering! but I guess with the knowledge acquired I can still make my own projects and grow my portfolio, parallel to using the course
"Stay True to the process" .. You told me this last year which really stuck to my head and helped me get my recent job 🌻 Excited to be doing more projects and learn from/with u
I have serious issues with self-learning new stuff from scratch (org skills, procrastination, concentration, motivation etc.). Would you then rather recommend doing a 2-year long MSc. in Data Science (part-time, with intro semester with all basic stuff, such as Python, statistics, math etc.), or attending a part-time bootcamp for 9-12 months (I have a BSc. in Urban Planning, so not really someone from the field...)? The MSc. would be a couple of K Euros cheaper than the boot camps (I'm in Europe). Thx!!
I'm currently learning Data Analytics to transition into ML engineering within 2 years according to my plan because I heard that ML engineering is not an entry level job and I need to be either in software development, mathematics, or data related fields to get in. I'm wondering if I can accomplish that while having no degrees in CS or Mathematics whatsoever and with just online cources and project creation. Please, give me your insight.
I think it is possible, but would be fairly difficult. I generally recommend getting a job as a swe first for a year or two then transition in. That way you are also making money as you learn rather than just studying hoping to land a job
Thanks for the recommendations Ken! I am always updating my learning path to include new things or based in things I enjoyed the most, for example making a deeper dive in data visualization. What happened with the editing? There is a frame mirrowed in minute 5:52. Your desk is in the other side of the room. Thanks again for the amazing content!
appreciate it :) actually, it mitigate a little bit my anxiety in terms of "OMG I need to learn ML within very close future, but I'm under ground zero level" yeah, so first goal is the ground zero, next 1st flor but in the middle aprox 20 smaller stair steps but most important is to DO it in endless itterations :D
Hey! Thanks for explaining everything so much in detail, just the video I was looking for. How much is leetcode & DSA required for a junior Data Scientist position? Thanks.
If I were relearn data science in 2022, I'd start by subscribing to your channel haha but in practicality it is: 1. Coursework 2. Projects 3. Teach Also great motivational video for simple content creation during break!
Hey Ken, Thanks a lot for the video. Of all the confusion which now seems to be a bit clear of what I need to learn, the challenge now is to manage time along with work & prioritize the learnings as couple of things are needed parallelly. Example: Planned on learning Advanced SQL | Python for DS & if I want to build projects it's a bit of challenge when working a full time job. I kind of know the answer that It's my life & I am the one to sort it but, just sharing as I am going off the track sometime by doing nothing.
@@KenJee_ds Thanks for the support. For sure, trying to prioritize things & will break them in chunks as you suggested. A lot to learn now mostly time management. There was a time when I had plenty of time to do anything. Time does move quickly when we get older :)
Thanks for this video Ken. I decided to go into Tech in 2018 by learning web development. Learnt a bit of HTML, CSS and Javascript. My goals has since evolved through this period as I got interested in python programming and Data science. My current interest now tends towards deep learning and AI Interesting my knowledge of front end web development will be very helpful in web scraping.
Hi Ken, I have been lost not knowing what to do with my life. I have always be leaning towards coding and business but had difficulty combining both. Imagine the relieve I felt when I learnt about data science. I am just beginning my journey and am grand I have somewhere to seek guidance. Thank you
Thank you for all the information that you share. I've spent several months considering the proper career switch after deciding not to return to medical school. At this point, I'm confident that data science is the proper career path. My overarching goal is to use data science in healthcare, but I am open to other fields. I found your channel today, and I want to say I am already grateful for your content and the resources you share. In addition to this video, I watched a video this evening in which you mentioned that Jan 4 is your bday. Happy Belated Birthday. May you continue to experience joy and success.
Hello Ken, thanks for your video! It’s really informative. I have some questions though. Will you consider R as a coding skill? I started learning coding through R, I learnt a bit of Python but I use it when necessary. So R becomes my ‘first language’ when comes to writing scripts. Would this drive me far if I continue doing data science-related work? Or should I start transitioning into using Python?
Depending on what you want to do, R is totally fine! I generally just recommend python because there are slightly more job opportunities that are looking for python related skills. R is a good language with plenty of opportunities though
How or where does a career like this tie into healthcare? Specifically, surgery? Surgery of any specialty? Or the functioning patterns of operating rooms? I have worked in surgery for 26yrs. I want a career change. And I think my past experiences and knowledge can help some where.
Hi Ken! This is AWESOME - I've been a Data Scientist for a few years now and completely agree about constantly needing to learn and refresh the data science toolkit. I didn't start out working on ML models but my work led there over time and had to learn on the job. Just joined the #66DaysOfData challenge and discord for Day 1, really excited to dedicate time to explore the field more. Thanks so much!
The only question I got is do we need a traditional degree to get in that domain? I'm a finance grad. Idk if I'd be eligible for a masters program in data science so how do I do it? Cause I don't think employers recognise anything other than degrees.
You don't need a traditional degree to break into the domain. Quite a few people land data science jobs from finance actually! A masters program is an option (most don't care what your undergrad was), but you could realistically make the transition just by self learning and building out an impressive portfolio.
Thank you for your kind words for how to start on the path of data science. Glad to say I had already a list of short course and webinars I have completed and a list of one's I wish to advance up too. I to completely agree on what I call ' cut and paste and make it my own' version of projects in python. After doing a very poor course in python I stumbled my way through to taught myself by doing just as you said in your video, that opening a screen and just typing it out and running it. I am visual person so makeing it my own by styling was how I learnt the best. Loving the colour or direction of xlable I could control, my understanding and interest peaked. This then grew into wanting to graph better results, more complicated, while, ifs, else all started to become tools. Now seeing the hurdles at the start to the hurdles I over came I'm so proud to complete my course as I know I can build from this, I never know it but I know I can do it... Reminds me of cooking when I first left home, you just keep getting better the more you try and read up on it. If your at the start of your data career good luck.
I always had that one question in my mind, i have so many sources to learn data science from RU-vid, websites , apps further more.... I stuck in this infinity loop .. Do i need to select one particular source ?? Or learn flexibly??
I would settle on one and commit to it for about 3-6 months. If you like it, great, stick with it more. Otherwise you can try others. All the resources out there are really good, just choose one
Enrolled into data science program from recommendation. Trying to understand but impossible to do so. Wish i an go back time and start from data analytic first
Watched just right now, but worth it. I want to entry in data science because I like to solve problems driven by data. I like to see the variables of a problem in the reality and reach a solution; solve real-world problems. This is something that I couldn't do with enginnering; college was a really frustation. But there I heard of 'data science' for the very first time and learn stathistics. I trying to enter in this field in a self paced manner. Already study python documentation, trying pandas documentation. Anyway... I hope the year ahead will be better.
Great Video! I stumbled upon your profile through Stefanovic > Luke Barousse to you. I have done a bachelor's in mining engineering and have been working as project manager for various IT relevant projects of mining provincial sector of Pakistan for more than 7 years now. Now I have picked up DS as my major for masters as I have been linked with IT and data for quite some time now. I have started learning Python and have gotten admitted in a master's program in DS starting Feb 2023. I am planning to follow your 66DaysofData challenge too to start my new journey. DS is quite overwhelming, but your videos make it sound intriguing and adventurous. I am switching my field from mining engineer to project manager to a data scientist now. Please share your insights on switching careers to DS and having any age limitations (if any) when it comes to getting rewarding projects and jobs relevant to DS.
Amazing! Excited to have you as part of the 66daysofdata! No age limitations from what I've seen. I've had a few podcast guests transition in late into their 30s
Hi, I'm a uni student majoring in business analytics. Because my school doesn't have a program that is "data analytic". If I only have two years left of college life. Is it possible to enter the data field? Btw, I like this video.
Thanks for watching! Yes, definitely possible to enter the field. I would try to get an internship if you can. If not, do as many personal projects as you can!
@@KenJee_ds If I'm not in a related program, how can I get a data-related internship? Btw, I've searched many articles online, trying to understand what are the differences between data analysts, business analysts, and data scientists. And, yes! They give me confidence to switch my future career path.
Great video! Seems like a very sensible roadmap for becoming a good data scientist. I'm here to comment because you said 6+ months to learn and I came to this video because I've only got a few days. I've got part-way through a recruitment process to be a data scientist and have a background only in maths and economics. I'm teaching myself python and SQL in about 4 days! Kaggle seems like an excellent resource to use on the fourth day, didn't know about it until watching this video. I hope this comment gives people a small chuckle at the task I've found myself with!
@@KenJee_ds Had it today... I think it went well. They sent me Python and SQL code 24hrs in advance for me to talk through in the interview. The rest of the interview was logic questions, suggesting solutions to extracting the correct information from tables (spoken, not code) and data table comprehension. I'd say the talking about the code was my weakest area, which is not surprising, but I gave it a good shot. A key thing I missed was you could assign values of another column into a sliced dataframe. The slice was checking a logical impossibility that might come up from inconsistent data and replacing it with a correction... which I basically misunderstood and thought was an incorrectly written slice. Hopefully my immediate "Oh you can assign new values into a slice" as soon as they pointed that out was clear enough that it was just I hadn't learned that was possible yet. They knew I'd taught myself everything in a few days, so took that into account. Hoping to hear back from them soon. I'm feeling sheepishly hopeful
@@KenJee_ds Update: I got the job! Turns out I did really well all around and because they knew I only had a few days to learn coding they were very impressed how much I'd managed to pick up.
@@KenJee_ds Absolutely! I am currently going to start by re watching this video like you said, and then create my roadmap. I have already downloaded R and R studio, but I think I will begin with Python. In addition, I am prepping to begin taking a Data Science Certificate course through Outlier, so I am hoping that certificate will assist me with some concepts and knowledge to help me. I told my self 2022 was gonna be the year I vastly improve for Data Science.
Hi! I just got accepted to ucla for data theory major(transfer). I leaned c++ in community college and have little bit of computer science background. Do you think it is better to go to grad school to become data scietist? I’ll self-teach python before I attend ucla.
I think it is better to try to land one first before going the grad school route. At least in the US grad school is very expensive. I'd rather be making money learning skills (even as an analyst) rather than paying to learn the same skills
I would start on kaggle by doing the micro courses. If you like those and think the field is still worth pursuing, I would start doing analysis on kaggle datasets and eventually maybe purchase a course
I want to become a data scientist working in the finance domain (I am not sure which division specifically for now, but I am sure its in the finance space). I know the basics of python for data science (pandas, numpy, etc) and I have done a few projects on kaggle. However, I am going to soon pursue a masters in data science and I am worried about the math. How much math is really required for data science? Intuitively, I understand why statistics plays a big role, but what about linear algebra and calculus? Like, how much of those do you need and what specific topics / learning objectives should you know before you can confidently say they are sufficient? (Sufficient to be a data scientist) Also - I understand that data science is a huge field and different parts of data science requires different skillsets, but I am looking to be right in between data analysis and machine learning. I am very much interested in machine learning, but then again, i don't want to go too deep into it considering my calculus isn't as strong. Any advice is much appreciated! Thank you for your time in advance!
Really depends on the work, but I think basic college level requirements are generally sufficient. That is an understanding of statistics, linear algebra, and calc 1. For some research roles it can be more, but most positions this would be more than enough
I don't mind it, and I know you're not gonna change the whole vid- but theres a small typo @7:16 where you type out "probalitiy theory" and i thought it was funny. Some data might show you get more comment engagement on videos with obvious typos 📊
I started to read the book "Introduction to Data Science", from Spring. And, also, the pandas documentation. Besides, i think pandas documentation a little confuse.
