I think that what worked for me can definitely work for you in this case. You need to be patient and consistent when learning this field. Feel free to comment any questions that you may have below!
Hi, thank you for this. Data science has a very steep learning curve. At some point, it feels very overwhelming, but thank God everything is possible. Like you said "Focus on implementation rather than trying to remember syntax" is key. Thanks again, I sincerely appreciate your content.
The best part of your videos is you are doing good research for various learning methods and sharing those with us which motivate or increase our eagerness to implement it... It is like connecting things to grow more...
Those are definitely the keys to get to the next stage! You are definitely doing the right thing by going through the project from scratch series and continuing on with your own projects!
As always, great video! One idea I haven't heard of before is focusing on one project and learning only the specific skills necessary for that project. Everyone's experienced the overwhelm when it comes to beginning the Data Science / ML / DL journey, and we're usually hit with the, "learn all of statistics, all of programming, and all of math" mentality that is drilled into our heads. This can be enough to scare people away, and your advice really combats this while touching on the idea of running on passion and things you enjoy by picking a personal project or use case. It's really cool to see that you have a specific domain that you stay in for projects (sports and sports betting). My question is: Do you stay within that domain at work? If you do, do you suggest that we each choose a specific domain as well, and cater towards that when building a career and finding a job? If you don't, would you agree that it's still valuable to choose a specific domain? Do you think that your experience and expertise in sports / sports betting transfers over to your current job/position? Thanks for the video Ken!
For data science, I generally recommend going deep rather than broad. If you have borderline expertise in one area that is valued, you will be more in demand and can actually get paid more. For example if you are very experienced with NLP or Computer Vision, you can make significantly more than a "general" data scientist. I think you should still have some foundation in all the types of techniques, but in my mind specialization is very valuable. That can also be transferred to domain. In my work I work almost exclusively with sports data. If you want to work in a specific industry, it definitely doesn't hurt to do most of your projects focused on that domain. On the other hand, I don't recommend this approach as much as I recommend specializing in certain types of analytical tools and techniques. I hope this helps answer your question! Also, will be responding to your email later today!
I’m in the second stage forsure, I hate it, it demotivated me, haven’t been working as hard because every time I get stuck I see the answer that I could never have though of with functions idek how they got, and I’m like I’ve been on this problem for an hour. It’s like macro-patience and micro-results
@@rayojel3918 Basically you put the code that you use frequently into one master document. You can reference it when you need it rather than trying to remember it every time!
I 've been learning a lot thru courses and I've learned cleaning data, python, numpy, pandas, matplotlib, and a few others but the math and algorithms are difficult. ( I haven't done math in school since 2011). I for sure feel like I'm in the seconds phase of learning. Should I stop learning the python programming part and just focus more on the math part?
I actually think that learning the programming will allow you to better understand the math. By implementing the algorithms in python you begin to understand the foundations of the mathematics. I think that you can keep learning both at the same time! I hope this helps and thank you for watching!
Also I have a small reques for you if possible... I can see some books are there on your table... Can you share which books you read other than data science or any book which you would like to suggest to us?
Books on the table there are "the 1% rule", "Why we sleep", "thinking in bets", "the 4 disciplines of execution", "Mathletics", and "Sprawlball". I actually have a video where I outline the top books I read last year, I think you may like it! ruclips.net/video/3TrAYmrmA8o/видео.html&ab_channel=KenJee
SQL is very important on the job, but I think it is a little less actionable for projects. I also think if you get familiar with python and pandas, SQL is something you can pick up very quickly. So I think you should definitely learn it, but it doesn't need to be the first thing you do. I hope this helps Joey!
I started with code academy personally (quite a long time ago). I think that the kaggle micro courses are a good start for python for data science as well. I also have a good relationship with 365 data science and believe they have a pretty good introductory python programming course. I hope this helps!
Ken Jee thanks for response! Yes, I did some micro courses in kaggle . I guess my problem is I know elements of Python separately, but don’t know how to combine them ) Are these courses for free or how much do they cost ? Thanks for your time and effort !
@@KenJee_ds thank you for your answer in your opinion a master degree in artificial intelligence could help to cover also some computer science topics or it is better to choose a master degree in statistics after a bachelor degree in statistics?
@@alessio4265 I think the masters in AI would make the most sense coming from a undergrad in Statistics. I did economics undergrad and did my masters in CS with a concentration in ML and artificial intelligence. I think that this path worked fairly well for me.
i really appreciate You talk and share your experience very smoothly .i am trying House price Advance Regression problem since three months even though i applied feature selection algorithms libraries , sklearn regression models but didn't work .i am stuck. I need help
@@KenJee_ds i did same..but i think i am not good at finding right model for finding the missing data even after trying mean, mode,median and most frequent values of any feature. another way i feel myself lost, outliers :-(
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I think that what worked for me can definitely work for you in this case. You need to be patient and consistent when learning this field. Feel free to comment any questions that you may have below!
Hi, thank you for this. Data science has a very steep learning curve. At some point, it feels very overwhelming, but thank God everything is possible. Like you said "Focus on implementation rather than trying to remember syntax" is key. Thanks again, I sincerely appreciate your content.
a tip: you can watch movies on Flixzone. I've been using it for watching all kinds of movies lately.
@Dario Reid Yea, I've been using Flixzone} for since december myself :)
@Dario Reid Definitely, I've been watching on flixzone} for since december myself =)
This is the best DS channel on YT ❤️❤️❤️
Thank you my friend!
The best part of your videos is you are doing good research for various learning methods and sharing those with us which motivate or increase our eagerness to implement it... It is like connecting things to grow more...
I love hearing this feedback Piyush! THanks for watching as usual :)
@@KenJee_ds My pleasure 😁...
Thank you. Currently fluctuating between stage 2 and 3. It brings so much clarity, watching honest videos like this.
Glad you found it helpful!!
It is a big honor to get such information from you , thank you so much !
Thanks for watching!
Thank you for your continuous videos about progressing for DS journey
Thank you for watching my videos Paul!
This is the far best video that i saw on my journey as data scientist. i really wanna thank you. because this was exactly what i wanted to hear.
Glad to hear!! Thank you for watching!
Yet another gem of a video, Ken. Thanks!
Thanks for watching and for the kind words!
Great, I am at stage 1 , after 20 days of starting this journey.
It's all about progress!
Now I know I am at stage 2. I need to practice and learn simultaneously. Thanks Ken
Those are definitely the keys to get to the next stage! You are definitely doing the right thing by going through the project from scratch series and continuing on with your own projects!
your videos has the best timing, thank you for the useful tips, i`m currently at stage 2 and using your tips to transition forward a big thanks Ken
I am glad it was helpful! I hope some of the tips here can help you hop to stage 3 soon!
As always, great video!
One idea I haven't heard of before is focusing on one project and learning only the specific skills necessary for that project. Everyone's experienced the overwhelm when it comes to beginning the Data Science / ML / DL journey, and we're usually hit with the, "learn all of statistics, all of programming, and all of math" mentality that is drilled into our heads. This can be enough to scare people away, and your advice really combats this while touching on the idea of running on passion and things you enjoy by picking a personal project or use case.
It's really cool to see that you have a specific domain that you stay in for projects (sports and sports betting). My question is:
Do you stay within that domain at work?
If you do, do you suggest that we each choose a specific domain as well, and cater towards that when building a career and finding a job?
If you don't, would you agree that it's still valuable to choose a specific domain? Do you think that your experience and expertise in sports / sports betting transfers over to your current job/position?
Thanks for the video Ken!
For data science, I generally recommend going deep rather than broad. If you have borderline expertise in one area that is valued, you will be more in demand and can actually get paid more. For example if you are very experienced with NLP or Computer Vision, you can make significantly more than a "general" data scientist. I think you should still have some foundation in all the types of techniques, but in my mind specialization is very valuable.
That can also be transferred to domain. In my work I work almost exclusively with sports data. If you want to work in a specific industry, it definitely doesn't hurt to do most of your projects focused on that domain. On the other hand, I don't recommend this approach as much as I recommend specializing in certain types of analytical tools and techniques.
I hope this helps answer your question! Also, will be responding to your email later today!
@@KenJee_ds Thank you!
thank you so much ken! i just begin my journey learning data science with no background and your videos rock
Glad you found them helpful! Thank you for watching!
I’m in the second stage forsure, I hate it, it demotivated me, haven’t been working as hard because every time I get stuck I see the answer that I could never have though of with functions idek how they got, and I’m like I’ve been on this problem for an hour. It’s like macro-patience and micro-results
Also, what’s code snippet library?
You'll get through it, I promise! Just make sure to break it down into small chunks!
@@rayojel3918 Basically you put the code that you use frequently into one master document. You can reference it when you need it rather than trying to remember it every time!
Damn good that You have writable version
Yep!
Great video Ken!
Suggestion for a future video - "Data Science and the Coronavirus"
Thanks! Will look into it!
Ken got that Under Armor sponsorship
Lol, maybe one day! They're kinda floundering these days. Maybe a data scientist could change that 😉
I see you have a copy of Wayne T. Winston.
Well played.
If I could like this video twice i'd do that. Thanks
I am glad you enjoyed it, thanks for watching and for the like!
I 've been learning a lot thru courses and I've learned cleaning data, python, numpy, pandas, matplotlib, and a few others but the math and algorithms are difficult. ( I haven't done math in school since 2011). I for sure feel like I'm in the seconds phase of learning. Should I stop learning the python programming part and just focus more on the math part?
I actually think that learning the programming will allow you to better understand the math. By implementing the algorithms in python you begin to understand the foundations of the mathematics. I think that you can keep learning both at the same time!
I hope this helps and thank you for watching!
Also I have a small reques for you if possible... I can see some books are there on your table... Can you share which books you read other than data science or any book which you would like to suggest to us?
Books on the table there are "the 1% rule", "Why we sleep", "thinking in bets", "the 4 disciplines of execution", "Mathletics", and "Sprawlball". I actually have a video where I outline the top books I read last year, I think you may like it! ruclips.net/video/3TrAYmrmA8o/видео.html&ab_channel=KenJee
@@KenJee_ds yeppp thanks for video link... I will try to read some of them...😀
You have mentioned starting with python or R, but how important is SQL? Do you think learning SQL would be good for someone just learning to code?
SQL is very important on the job, but I think it is a little less actionable for projects. I also think if you get familiar with python and pandas, SQL is something you can pick up very quickly. So I think you should definitely learn it, but it doesn't need to be the first thing you do. I hope this helps Joey!
Can you point to some projects from kaggle that we should do in stage 2 and stage 3 ??
I recommend these projects: ruclips.net/video/8igH8qZafpo/видео.html. I hope this helps!
Entered stage 2 ...
That is a big step, congrats!!
I am on stage 2, let's see how many of us?
I have Masters Degree from Illinois State university in applied statistics but lack in programming). What would you recommend @Ken Jee?
Great Idea!
I started with code academy personally (quite a long time ago). I think that the kaggle micro courses are a good start for python for data science as well. I also have a good relationship with 365 data science and believe they have a pretty good introductory python programming course. I hope this helps!
Ken Jee thanks for response! Yes, I did some micro courses in kaggle . I guess my problem is I know elements of Python separately, but don’t know how to combine them ) Are these courses for free or how much do they cost ? Thanks for your time and effort !
In your opinion is a statistics' degree useful to become a data scientist or is completely useless?
I think a statistics degree is one of the better ones you could have to enter this field. Just make sure you learn the coding skills as well!
@@KenJee_ds thank you for your answer in your opinion a master degree in artificial intelligence could help to cover also some computer science topics or it is better to choose a master degree in statistics after a bachelor degree in statistics?
@@alessio4265 I think the masters in AI would make the most sense coming from a undergrad in Statistics. I did economics undergrad and did my masters in CS with a concentration in ML and artificial intelligence. I think that this path worked fairly well for me.
@@KenJee_ds thank you very much
i really appreciate You talk and share your experience very smoothly .i am trying House price Advance Regression problem since three months even though i applied feature selection algorithms libraries , sklearn regression models but didn't work .i am stuck. I need help
I would check out other peoples code on kaggle for it! That should help you get unstuck!
@@KenJee_ds i did same..but i think i am not good at finding right model for finding the missing data even after trying mean, mode,median and most frequent values of any feature.
another way i feel myself lost, outliers :-(
@@muzamilshah8028 When starting out, you can also remove outliers! Just so you have something that is runable
@@KenJee_ds All i wish to have face to face discussion with you friend but not possible :-(