Hi Richard! I'm in the first year of a CS degree and I'm looking forward every field to see who I can become and what I could do. I've stumbled upon your channel and I did some other research on how data science is. Then this evening I've seen this article. What do you think about it? veekaybee.github.io/2019/02/13/data-science-is-different/ Thanks!
@@lorebirb This is really interesting, thanks for sharing! I have a lot of thoughts - probably so many that I could fill a whole video with them. The author is right to point out the distinction between full-stack, full-fledged data scientists, and people with some subset of data science skills. Apples and oranges. And also to point out that the vision people are sold of data science in school is complete mythology that's not true in the real world. There is an obscene over-emphasis on AI, ML, and deep learning, and not nearly enough emphasis on adding value to business, communicating, and domain. That is part of the problem. We are definitely in a stage in the hype cycle as well where some companies are beginning to state they are not getting what they wanted out of their data department/scientists. I am not sure if that's a talent mismatch, or poor expectations and organization from the company. I actually suspect it's more of the latter though. The importance of the cloud will be true in this decade, though it is difficult to speculate exactly on what that will look like. I'm highly skeptical of this growing notion that the field should be closer to data engineering, though the author here almost points out a complete different tracks. Expecting people to be unicorns, including with SWE expertise, is a recipe for disaster; the segmentation approach into creating ML engineers is certainly happening though and strikes me as a much better approach. As a long and short, data science suffers some of these problems from being ambiguously defined. It may look a lot different by end of this decade, but it is absolutely not going anywhere.
1. Statistics 2. SQL 3. R or Python 4. The other of Python or R 5. Linear algebra (No BS guide to linear algebra) 6. UX design and data visualisation principles 7. ML
I really appreciate this video you've explained it really well. I did my Industrial Maths degree 20 years ago (I was the second batch of graduates from my uni). There were not many jobs then in my country so I went into a totally irrelevant field. Fast forward 20yrs, to my surprise, my degree is now so popular! I need to refresh what I've learnt from my uni days. It seems like nothing much has changed, just the new Python programming. I'm just starting to learn Python for beginners from UA-cam. Thank you for naming the books and online resources like Cousera for refreshing my memory.
Overall very good roadmap! I was not lucky to find this video when I started, but my strategy to learn each of these topics was nearly in line with everything you said in this video. The order of my journey is listed below: 1. Statistics 2. SQL (You can't analyze or work with data if you can't even retrieve it :D) 3. Python (Could have been R, I personally chose Python) 4. Tableau (Data Visualization Tool) 5. R (Could be Python if R was choice in #3)
I like your approach, this is basically a good starting path, I am currently changing my career path to DS and your video has been very useful, Thanks a lot
great roadmap! based on practicality! You mentioned that after the first three items, one is already employable? can you tell us what kind of jobs a person can get after knowing the first three items? tia! :)
Your videos are amazing! I just watched several of them now after stumbling upon the R vs Python one and they’re all very instructive. I’m currently an undergraduate majoring in Data Science and I’m wondering what’s your take on necessity of getting a masters degree for industry. Could online courses with certificates (such as Coursera) replace a masters degree given you have the skills necessary? And if one was to go to graduate school for the data science field, how should one approach deciding WHAT to study? There’s many quantitative fields to choose from, such as Statistics, CS, data science, predictive analytics, operations research, etc.? Thanks in advance! Keep up with the great videos!
Thank you so much! That is the primary goal - to provide some helpful food for thought, sprinkled in with maybe a little bit of entertainment now and then. I hate to say that a masters degree is a hard requirement, but it's the fact of the matter now that most people with the title of "data scientist" do have at least a masters degree -- naturally some percentage doesn't, but there's (in my opinion) an overly large number of employers that needlessly rule candidates out on the basis of not having a masters degree. At the end of the day though I think having the necessary skills and knowledge in place should be prioritized over having the degree on paper though, which is where a lot of the value of online courses with certificates come into play. I have a bit of a bias towards statistics myself, having done my masters in it personally -- and because it builds a very nice theoretical foundation -- but there are arguments to virtually any of those quantitative fields. I think it's important to realize that the program is just the beginning of the education process (the learning in DS never ends) -- so honestly, picking a field you're genuinely passionate about learning more about will serve you great.
Good presentation. I'm a researcha and I use R for the statistical analysis. I want to ask you somehing info about package funcy. Do you use it? Thanks
Glad it was helpful! Let me know what other videos that you think would be helpful -- I try and take viewer suggestions into account when I plan out my future content!
THATNK YOU THANK YOU THAN YOU & THANK YOU ....i have been looking for months for a guide on my future path to take in life and you've just shown me the way ..i am truly grateful .....and coming from auditing i really see myself doing this regardless of the hurdles i will face..... thank you for your mentoring ....i would absolutely appreciate more videos like this , in helping guide the way to becoming a Data scientist like yourself ...(new subscriber btw ) ......and if you have a personal mentoring program i'd be happy to join ...do you have one ?
Hah, I really appreciate the kind words! I do not have a personal mentoring program - at this time I really do want to avoid creating any kind of paid course or coaching programs (get back to me in a couple years, I'll see if I've changed my mind on that). Having said that, please let me know if there are specific topics you'd like me to continue covering as far as guiding the way to becoming a data scientist, and I'll try to get those out in future videos!
Hi, great video. I noticed that the statistics recommendations are all R focused. What would you recommend that has a python focus or is it fine to take those courses regardless?
Thank you so much for the great insight . .I have one advice. . sorry I am a beginner . . You said item 7 is Machine learning . .but . . I thought those algorithm are in Statistics already .. .and when we implement using python we say its machine learning algorithm . . so fundamentally . how is stats . .different from machine learning . . .
Probably the most accredited one is the Data Science specialization course on Coursera (and the certification is great), but unfortunately you're not gonna get one single class that fundamentally covers all of data science. It's why you'll get people with M.S's and/or PhD's but the learning just begins then. That being said, that class will certainly get you on the right foot!
Thank you for this excellent overview of the field. Perhaps learning linear algebra and calculus as the first courses, before going on to statistics and probability, would be a solid path. Thats at least how it is often presented in many quantitative college courses. You mentioned SAS stattistics programming in passing. What is your personal valuation of the importance of learning this programming language, compared to R and Python?
Haha, yep. Glad that those were helpful to you too. I also have a video on the math (calculus and linear algebra) required for data science as well. To summarize on this one, SAS has its uses in certain industries, but I probably wouldn't dive into it much before getting good at either R or Python personally.
@@RichardOnData Thanks for your reply. I watched the video on SAS, and I actually agree with you. I live in Denmark, and the situation is quit similar. Economics/ data science job advertisements in the public sector, and especially in health(healthcare is approximately 90% publically funded in the nordic countries, I think), usually ask for SAS skills. I have come across a few requiring or wishing programming skills in R, and rarely python programming skills. That is unless it is a high level research position( typically requiring a PhD). My background is in economics and I have job experience in the public health sector. And approximately 15 years ago I was taught SAS. In my course I was first taught calculus, and in order, linear algebra, probability theory, statistics with SAS, and finally econometrics. In my case it actually makes sense to hone my skills in this programming language up to a certain level, as I am at the moment looking for a job. As you have recommended, I plan on expanding my skills, with either R or Python. And probably also SQL, as this is also mentioned regularly in job ads. Machine learning and AI is likely far down the list. For the majority of public sector job ads I have read in the last 6 months or so, SAS and SQL have been requested, in addition to advanced level Excel user. They dont expect much else if you are mostly dealing with government statistics, where most work is concerned with descriptive statistics. It might be different in certain industries, like finance, but for now this plan makes most sense. The situation might be different in 5-10 years. But as you mentioned, the sunk costs associated with the investment in SAS software(not the price of the licence, but the training invested in learning particular programming skills, and the structuring of databases) makes it unlikely that government/public institutions wil make a sudden shift in the near future. Thanks for your videos. They are overall clearly and logically structured, and without dumbing down the content unnecessarily.
4 роки тому+1
Your videos are super helpful! Keep the coming, please.
It's very difficult and takes some thinking outside of the box (or experience from other industries) but not impossible... somewhere around 10-15% of data scientists report having something less than a bachelors.
Hello Richard, Firstly I would like to tell you that this is a highly informative video has given me a lot to think about. If I had known this I would have had done differently in my path of becoming a data scientist. I just had one query regarding the link you have mentioned about the Johns Hopkins Data Course Specialization, so I just wanted to ask you that does this course teach you the statistical basics and tools and how they work along with R or just how to implement them(making statistics a pre-requisite for this course). I really hope you can spend some time answering this. It would be really helpful to me.
Hello, Sorry to take so long to see your comment here! So I haven't taken these Coursera classes myself; we generally with these things have to rely a little bit on the reviews. I know this one is one of the most popular in the data science specialty. Personally, what had caught my eyes about this particular course is that there is emphasis on practicality; in particular it's good that there is an early section on getting and cleaning data (this is FAR too often overlooked in DS education). It looks like the foundation is R, and the course is run through that framework. Doesn't look to me like statistics is a pre-requisite per se, though naturally it'll be of help.
@@RichardOnData RichardOnData, Thank you for giving time and answering my query I appreciate it a lot, your reply does help me a ton and has taken away a lot of tension I previously had. I aim to complete this specialization and then get my core statistics strengthened too after which I plan to learn SQL. Which is the path you have mentioned. Really thanks a ton and I hope you keep making such informative videos and keep enlightening novices like me.
I've taken some stats like stochastic processes, time series, regression analysis, monte carlo simulations, and some other modeling classes in addition to stats and prob sequence. I found myself finishing college in actuarial department and despite passed exams I don't see an opportunity arising, meanwhile taking your suggestion I've been planning alternative route which involves SQL, and exploring opportunities in data science as goal I'm hard pressed to find employment. What would be your suggestions? I took Python class, it made me wake up at night to implement code that was an obstacle during the day, I never felt so driven afterwards despite using R for all the stats curriculum.
I might have some personal bias here because when I was younger I considered actuarial as a career path but pulled away from it seeing some people I knew going that route and being bored out of their minds. But I'll always advocate doing things you have passion for and enjoy over what will pay more or even what seems to have more employment opportunity in the short term - is it worth working a job you despise even if the cash is decent? Knowing a lot of statistical modeling, SQL, and Python should at least theoretically put you in a nice position - have you found that not to be the case for more than ~3 months or so?
@@RichardOnData I agree with doing what gives meaning to early mornings, and stimulate thoughts on toilet, that's the reason I quit kitchen and went to college. I'll work on developing proficiency in SQL, it made me appreciate clean data sets I've seen in college.
Hi Richard, how do I translate what I know to getting an entry level job? I've used SQL in an internship and I've been learning stats alongside R. I'm comfortable with Tidyverse and ggplot2. After over 100 applications sent out, I've only got 1 interview. I've been looking for business analyst jobs and I've seen your other video about the differences between BA's and data scientists. I would imagine that BA position would be easier to get. Any advice on landing that first job out of college? Thanks!
I have a video I just recently did on how to get a data science job. Some short tips I would provide are the storyboard approach during interviews (find something you've done and tell the setting, problem, solution you provided, and the impact of it), utilize whatever network you have (almost all of my job opportunities came this way, personally), build a portfolio if you have enough content, and just hang in there and keep trying! It's also totally doable to transition from BA to DS in the long run.
Yeah, I think so. You may need to look up some concepts here and there for statistics and linear algebra - and there's value down the road in getting up to speed on it (I have a video on math you should know for data science) - but generally speaking I think you will do just fine.
It's amazing how far AI has come along. This deepfake avatar is amazing and makes me really want to listen. What's my proof ? Oh, right, the avatar never blinks. Duh! 😄
Anything you feel I've left out here? What was YOUR pathway to becoming a data scientist?
Hi Richard! I'm in the first year of a CS degree and I'm looking forward every field to see who I can become and what I could do. I've stumbled upon your channel and I did some other research on how data science is. Then this evening I've seen this article. What do you think about it?
veekaybee.github.io/2019/02/13/data-science-is-different/
Thanks!
@@lorebirb This is really interesting, thanks for sharing! I have a lot of thoughts - probably so many that I could fill a whole video with them.
The author is right to point out the distinction between full-stack, full-fledged data scientists, and people with some subset of data science skills. Apples and oranges. And also to point out that the vision people are sold of data science in school is complete mythology that's not true in the real world. There is an obscene over-emphasis on AI, ML, and deep learning, and not nearly enough emphasis on adding value to business, communicating, and domain. That is part of the problem. We are definitely in a stage in the hype cycle as well where some companies are beginning to state they are not getting what they wanted out of their data department/scientists. I am not sure if that's a talent mismatch, or poor expectations and organization from the company. I actually suspect it's more of the latter though.
The importance of the cloud will be true in this decade, though it is difficult to speculate exactly on what that will look like. I'm highly skeptical of this growing notion that the field should be closer to data engineering, though the author here almost points out a complete different tracks. Expecting people to be unicorns, including with SWE expertise, is a recipe for disaster; the segmentation approach into creating ML engineers is certainly happening though and strikes me as a much better approach.
As a long and short, data science suffers some of these problems from being ambiguously defined. It may look a lot different by end of this decade, but it is absolutely not going anywhere.
This now actually created the inspiration for this video: ua-cam.com/video/VQq8DwEJeoU/v-deo.html
Love how you get to the point. No rambling, tangents, or annoying banter. Very underrated channel.
Thank you so much! It's a work in progress of which I'm excited about the rate of growth!
1. Statistics
2. SQL
3. R or Python
4. The other of Python or R
5. Linear algebra (No BS guide to linear algebra)
6. UX design and data visualisation principles
7. ML
Love that you presented your case in a very practical manner... no fluff, no unwanted inspo stuff... Thanks for keeping it real!
Glad it was helpful!
I really appreciate this video you've explained it really well. I did my Industrial Maths degree 20 years ago (I was the second batch of graduates from my uni). There were not many jobs then in my country so I went into a totally irrelevant field. Fast forward 20yrs, to my surprise, my degree is now so popular! I need to refresh what I've learnt from my uni days. It seems like nothing much has changed, just the new Python programming. I'm just starting to learn Python for beginners from UA-cam. Thank you for naming the books and online resources like Cousera for refreshing my memory.
I signed up or the Statistics with R Specialization course offered by Coursera today because of this video. Thank you for the path forward.
Haha, my pleasure - happy to provide some inspiration, and wishing you the best of luck!
Overall very good roadmap! I was not lucky to find this video when I started, but my strategy to learn each of these topics was nearly in line with everything you said in this video. The order of my journey is listed below:
1. Statistics
2. SQL (You can't analyze or work with data if you can't even retrieve it :D)
3. Python (Could have been R, I personally chose Python)
4. Tableau (Data Visualization Tool)
5. R (Could be Python if R was choice in #3)
One of the best educated presentation on DS
No bro, he made statistics a condition for ds. He is wrong I believe
Damn, I love your videos man. Explanation and everything put together, are amazing. Please upload more about Data Science, I'm your biggest fan.
Hello Richard, kindly do a video on the difference between Data Science and Data Analysis, and how one can get into the Data Analyst field. Thank You
One stop solution for all data science queries. Thanks @Richard
That's the long term plan! Thanks!
I like your approach, this is basically a good starting path, I am currently changing my career path to DS and your video has been very useful, Thanks a lot
Thank you so much!
this is the best data science channel i've found
Thank you so much! That’s very kind!
Thanks for your engagement😊 my university is closed, so I play with SQL& R for this period to become good in those fields
Best of luck! Tough times that we're living in now but possibly times that we can use to our advantage if we're disciplined about it!
with the advent of AutoML, how much does one need to know now, to actually use these tools?
This is indeed a very balanced and good video. Well done.
Thank you kindly!
Thank you for this content richard,its great.
My pleasure!
great roadmap! based on practicality! You mentioned that after the first three items, one is already employable? can you tell us what kind of jobs a person can get after knowing the first three items? tia! :)
Could you please list out the books you mentioned in the video?
Please see the video description. It's a little long, but the books are listed there with names and Amazon links, by topic mentioned in the video.
My take is there is more rational, common sense per square inch here than anywhere else on the web or UA-cam. Thanks.
Your videos are amazing! I just watched several of them now after stumbling upon the R vs Python one and they’re all very instructive. I’m currently an undergraduate majoring in Data Science and I’m wondering what’s your take on necessity of getting a masters degree for industry. Could online courses with certificates (such as Coursera) replace a masters degree given you have the skills necessary? And if one was to go to graduate school for the data science field, how should one approach deciding WHAT to study? There’s many quantitative fields to choose from, such as Statistics, CS, data science, predictive analytics, operations research, etc.? Thanks in advance! Keep up with the great videos!
Thank you so much! That is the primary goal - to provide some helpful food for thought, sprinkled in with maybe a little bit of entertainment now and then.
I hate to say that a masters degree is a hard requirement, but it's the fact of the matter now that most people with the title of "data scientist" do have at least a masters degree -- naturally some percentage doesn't, but there's (in my opinion) an overly large number of employers that needlessly rule candidates out on the basis of not having a masters degree. At the end of the day though I think having the necessary skills and knowledge in place should be prioritized over having the degree on paper though, which is where a lot of the value of online courses with certificates come into play.
I have a bit of a bias towards statistics myself, having done my masters in it personally -- and because it builds a very nice theoretical foundation -- but there are arguments to virtually any of those quantitative fields. I think it's important to realize that the program is just the beginning of the education process (the learning in DS never ends) -- so honestly, picking a field you're genuinely passionate about learning more about will serve you great.
Good presentation. I'm a researcha and I use R for the statistical analysis. I want to ask you somehing info about package funcy. Do you use it? Thanks
Wow! This is fantastic and extremely helpful - thank you! I’m so glad I came across your channel.
Glad it was helpful! Let me know what other videos that you think would be helpful -- I try and take viewer suggestions into account when I plan out my future content!
THATNK YOU THANK YOU THAN YOU & THANK YOU ....i have been looking for months for a guide on my future path to take in life and you've just shown me the way ..i am truly grateful .....and coming from auditing i really see myself doing this regardless of the hurdles i will face..... thank you for your mentoring ....i would absolutely appreciate more videos like this , in helping guide the way to becoming a Data scientist like yourself ...(new subscriber btw ) ......and if you have a personal mentoring program i'd be happy to join ...do you have one ?
Hah, I really appreciate the kind words! I do not have a personal mentoring program - at this time I really do want to avoid creating any kind of paid course or coaching programs (get back to me in a couple years, I'll see if I've changed my mind on that).
Having said that, please let me know if there are specific topics you'd like me to continue covering as far as guiding the way to becoming a data scientist, and I'll try to get those out in future videos!
Hi, great video. I noticed that the statistics recommendations are all R focused. What would you recommend that has a python focus or is it fine to take those courses regardless?
Thank you so much for the great insight . .I have one advice. . sorry I am a beginner . . You said item 7 is Machine learning . .but . . I thought those algorithm are in Statistics already .. .and when we implement using python we say its machine learning algorithm . . so fundamentally . how is stats . .different from machine learning . . .
Thanks .. What is the best full course for data analysis and data science worldwide? practical one? with good credited certification.
Probably the most accredited one is the Data Science specialization course on Coursera (and the certification is great), but unfortunately you're not gonna get one single class that fundamentally covers all of data science. It's why you'll get people with M.S's and/or PhD's but the learning just begins then. That being said, that class will certainly get you on the right foot!
Thank you for this excellent overview of the field. Perhaps learning linear algebra and calculus as the first courses, before going on to statistics and probability, would be a solid path. Thats at least how it is often presented in many quantitative college courses.
You mentioned SAS stattistics programming in passing. What is your personal valuation of the importance of learning this programming language, compared to R and Python?
Oh, never mind my last question. I see that you already have posted a video on the subject of SAS.
Haha, yep. Glad that those were helpful to you too. I also have a video on the math (calculus and linear algebra) required for data science as well. To summarize on this one, SAS has its uses in certain industries, but I probably wouldn't dive into it much before getting good at either R or Python personally.
@@RichardOnData Thanks for your reply. I watched the video on SAS, and I actually agree with you. I live in Denmark, and the situation is quit similar. Economics/ data science job advertisements in the public sector, and especially in health(healthcare is approximately 90% publically funded in the nordic countries, I think), usually ask for SAS skills. I have come across a few requiring or wishing programming skills in R, and rarely python programming skills. That is unless it is a high level research position( typically requiring a PhD).
My background is in economics and I have job experience in the public health sector. And approximately 15 years ago I was taught SAS. In my course I was first taught calculus, and in order, linear algebra, probability theory, statistics with SAS, and finally econometrics. In my case it actually makes sense to hone my skills in this programming language up to a certain level, as I am at the moment looking for a job. As you have recommended, I plan on expanding my skills, with either R or Python. And probably also SQL, as this is also mentioned regularly in job ads.
Machine learning and AI is likely far down the list. For the majority of public sector job ads I have read in the last 6 months or so, SAS and SQL have been requested, in addition to advanced level Excel user. They dont expect much else if you are mostly dealing with government statistics, where most work is concerned with descriptive statistics. It might be different in certain industries, like finance, but for now this plan makes most sense. The situation might be different in 5-10 years. But as you mentioned, the sunk costs associated with the investment in SAS software(not the price of the licence, but the training invested in learning particular programming skills, and the structuring of databases) makes it unlikely that government/public institutions wil make a sudden shift in the near future.
Thanks for your videos. They are overall clearly and logically structured, and without dumbing down the content unnecessarily.
Your videos are super helpful! Keep the coming, please.
My pleasure, will do! Are you a current or aspiring data scientist yourself?
For those who want to learn all by yourself, you can't get away with not having a related degree, really
It's very difficult and takes some thinking outside of the box (or experience from other industries) but not impossible... somewhere around 10-15% of data scientists report having something less than a bachelors.
Hello Richard, Firstly I would like to tell you that this is a highly informative video has given me a lot to think about. If I had known this I would have had done differently in my path of becoming a data scientist. I just had one query regarding the link you have mentioned about the Johns Hopkins Data Course Specialization, so I just wanted to ask you that does this course teach you the statistical basics and tools and how they work along with R or just how to implement them(making statistics a pre-requisite for this course). I really hope you can spend some time answering this. It would be really helpful to me.
Hello,
Sorry to take so long to see your comment here!
So I haven't taken these Coursera classes myself; we generally with these things have to rely a little bit on the reviews. I know this one is one of the most popular in the data science specialty. Personally, what had caught my eyes about this particular course is that there is emphasis on practicality; in particular it's good that there is an early section on getting and cleaning data (this is FAR too often overlooked in DS education). It looks like the foundation is R, and the course is run through that framework. Doesn't look to me like statistics is a pre-requisite per se, though naturally it'll be of help.
@@RichardOnData RichardOnData, Thank you for giving time and answering my query I appreciate it a lot, your reply does help me a ton and has taken away a lot of tension I previously had. I aim to complete this specialization and then get my core statistics strengthened too after which I plan to learn SQL. Which is the path you have mentioned. Really thanks a ton and I hope you keep making such informative videos and keep enlightening novices like me.
Incredibly helpful! Thank you!
I've taken some stats like stochastic processes, time series, regression analysis, monte carlo simulations, and some other modeling classes in addition to stats and prob sequence. I found myself finishing college in actuarial department and despite passed exams I don't see an opportunity arising, meanwhile taking your suggestion I've been planning alternative route which involves SQL, and exploring opportunities in data science as goal I'm hard pressed to find employment. What would be your suggestions? I took Python class, it made me wake up at night to implement code that was an obstacle during the day, I never felt so driven afterwards despite using R for all the stats curriculum.
I might have some personal bias here because when I was younger I considered actuarial as a career path but pulled away from it seeing some people I knew going that route and being bored out of their minds. But I'll always advocate doing things you have passion for and enjoy over what will pay more or even what seems to have more employment opportunity in the short term - is it worth working a job you despise even if the cash is decent? Knowing a lot of statistical modeling, SQL, and Python should at least theoretically put you in a nice position - have you found that not to be the case for more than ~3 months or so?
@@RichardOnData I agree with doing what gives meaning to early mornings, and stimulate thoughts on toilet, that's the reason I quit kitchen and went to college. I'll work on developing proficiency in SQL, it made me appreciate clean data sets I've seen in college.
My Brain hurts lol. I am moving out of Desktop support with NO Programming experience and took College Algebra 12 years ago. But I have to try.
Man. That's seriously inspiring. How is it going??
Hi Richard, how do I translate what I know to getting an entry level job? I've used SQL in an internship and I've been learning stats alongside R. I'm comfortable with Tidyverse and ggplot2. After over 100 applications sent out, I've only got 1 interview. I've been looking for business analyst jobs and I've seen your other video about the differences between BA's and data scientists. I would imagine that BA position would be easier to get. Any advice on landing that first job out of college? Thanks!
I have a video I just recently did on how to get a data science job. Some short tips I would provide are the storyboard approach during interviews (find something you've done and tell the setting, problem, solution you provided, and the impact of it), utilize whatever network you have (almost all of my job opportunities came this way, personally), build a portfolio if you have enough content, and just hang in there and keep trying! It's also totally doable to transition from BA to DS in the long run.
if i were to come from a health sciences background with no training in calculus, would I be ok to jump straight into these 7 steps?
Yeah, I think so. You may need to look up some concepts here and there for statistics and linear algebra - and there's value down the road in getting up to speed on it (I have a video on math you should know for data science) - but generally speaking I think you will do just fine.
Python - it's so hot right now!
#2 on the TIOBE index now and it is knocking at the door of the #1 spot!
Awesome video
Thanks!
thank you so much!!
Awesome video 👍
Thank you!
BLINK DAMNIT!!!!
Great video tho
this guy rich good
Haha, well thank you
Great video ✔️
this is awsome
Thank you!
For everyone watching this video: don’t fully believe what he says. Statistics is not a condition. Please watch other videos as well
It's amazing how far AI has come along. This deepfake avatar is amazing and makes me really want to listen. What's my proof ? Oh, right, the avatar never blinks. Duh! 😄
Hah! This cracked me up
Everyone gets shoots