how to make money while maintaining the respect your audience has for you - not many can do it on YT, kudos! and you can't imagine how much help this video is. I often feel the bigger hurdles are inner/psychological reasons and it made me think: no that can't be maybe it can
I think transparency is the key! My goal is to only do partnerships that I really believe will help out my subscribers. I am glad the video was helpful to you as well!
I already had a solid Math background when I started learning DS - but my graduation was 25 years ago :) I also had a LOT of programming experience, but not in Python or R. And still, my greatest problem wasn't the Math or the Python, it was *lack of the systematic approach*. When you go for one of the countless "become a data scientist in 10 hours" course, you don't really understand that material. The worst thing is you might THINK you understand it, but that's a false feeling. There is no way to understand it all in 10 hours, be prepared for a long journey or retreat. There are three books that I recommend to everyone: 1). Mathematics for Data Scientists (M.P.Deisenroth and others) - all math you will need in a convenient form. 2). Fundamentals of Machine Learning for Predictive Data Analysis (Johh D. Kelleher and others) - everything about Machine Learning except for the neural networks. 3). Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurelien Geron - a great book about Deep Learning (you may skip the other parts). All three books have a lot of exercises, and you should do ALL of them. After that you will know enough to consider your knowledge solid. I've stopped on the exercises for the very first chapter for now, though I've read the books, of course :)
Thanks Ken for another awesome video. Just a moment ago I was about to find something to watch on RUclips and discovered your video in the recommended videos. Great analogy of mastering the rubiks cube and learning data science, particularly like the advice on breaking the learning to chunks and mastering each chunk. 😃👍👍
Hey K, You are correct sir. My saying for the vast majority of 'life on earth' is "Small Victories". For instance, one rarely has *massive battles*, *huge upsets* and *gigantic victories*, instead we have many *Small Victories* that build to winning the battle.
Ken Jee Sir , Your video is really useful. I know the concept of data science but don't know how to practice programming.please suggest something to how should I practice the python to get better in programming use it for DS and ML .
Hi Ken, It took me 1 year to learn to fully solve a rubix cube. For whole one year I was able to solve the 2 layers only, Finally I decided that I need to finish the cube and learn 3rd layer as well. Within a week I learned to whole cube. My personal best is 2 minutes 34 seconds now. But thats not the point. Point is now I am a hotel management student, with MBA in finance from Third tier institute, It is great deal in learning programming and stats , but that cube give me strength that I can. Sooner or later I will figure out everything and become expert. Thanks Ken Jee for inventing #66daysofdata to keep ourselves stick to the plan and get going.
Thank you. Since learning that this field exists 6 months ago it's all I think about, and all I want to do. That being said, I have 0 technical background and am starting this journey at 40. Much harder for me to learn and retain at 40 than it was at 20. As much as I beat myself up for not getting it fast enough, I also try to celebrate the small wins too. Stumbled onto your channel a few days ago and can't get enough. Thank you for this, and all the other videos. They really help.
Hi James - Thanks for watching my videos! I'm glad you are finding them helpful! If there are any specific topics you would like to see from your unique perspective, please let me know. I don't think it is ever too late to learn data science, and I am glad that you are someone who truly enjoys it!
I hope your Rubik's analogy isn't *too* accurate. I never solved more than one side of that damn thing in the 80s and gave up. I hope that doesn't mean I'm lacking the perseverance/chops for data science 😬
Great vid, thanks for sharing. I'm particularly thankful for believing and expressing that the capacity of humans to learn anything one step at a time with persistence.
this video is a good guide for a beginner like me! "start somewhere and usually doesn't matter where" sounds like gradient descending my ML knowledge loss😂😂
couldn't agree anymore :) I think that kind of mindset must be had for all kind of goals that we want to achieve, because some people usually blame the abstract way instead of choosing one and develop little by little
Hey Ken, I had just finish all the modules in 365 data science except for the new BA module. I am revisiting ML and DL(Tensorflow2) as i guess these are the basics which i have to be solid in to progress. The risk credit is the most difficult and i had yet to fully understand it. Time series and customer analytics are much easier to understand. The final five modules are basically implementation and are mainly ML. How should i advance? like learning more advanced ML and DL? Should i just go kaggle instead? or borrow books to enhance my skills? as i do not know how vast the world of ML and DL is. LOL. Thanks
Hey Ken jee, I hope you'll be able to help me with my thought: does venturing into data science naturally means degrading your eyesight since you'll be staring at the screen? What can be done about it? And especially of the programming and casual gaming aspect, because 1) I hardly come across topics about eyesight when it comes to these 2) Asian parents equate computer screen = bad XD Thanks for taking time off in advance!
I can't say it has been good for my own eyesight. I think that balance is really important. Generally, to mitigate problems like this, you need to spend a good amount of time outdoors. Sorry I couldn't be more helpful here, but thank you for watching!
For so long I anticipated my masters program in health data science with a focus on outcomes research, it's been a month and the python program is a huddle, maybe because I'm impatient, I get super frustrated when I run codes, and they don't go, I am quite smart, but I'm beginning to rethink this decision, I'm so confused, don't know what to do. I literally spent 9 hours on a task, and still didn't meet up with the deadline.
Ken sir you're absolutely correct.But remember when you got out of this hurdle and pass rubix or complete learning data science or anything you will get a great reward for it
There are great rewards, but this is where the rubiks cube analogy to data science breaks down. Unfortunately, I will never learn all of data science. I have to breaking it into chunks and conquering the smaller parts. The field is growing faster than I can learn which makes it very exciting!
Hey been watch your channel for a while, great stuff... Could we get an in-depth video on the differences between a Data Scientist and Data Engineer? It would be really helpful.
@ken good lesson. It's easy to get overwhelmed with the choices we have and stall your progress as a result. One tutorial, one project at a time. Keep up the good work!
you entered the field when data science was something only a handful of people could do. now, you need much greater expertise to enter the field, esp with the economy the way it is.
This is a good analogy, Ken. Luckily I took relevant courses step by step over the years, so I only had to focus on certain things when learning DS. I can only imagine how daunting it would be to jump over a huge hurdle (or to climb up a huge mountain as you mentioned) in front of people who are just starting to learn. I believe this video can guide people who are just about to learn data science in the right direction. It's important to take small steps at a time, instead of trying to do everything at once.
@import data.. Will you please tell your learning process which helped you to learn DataScience.. i. e like step1 you learned python, step2.... anything like that ..
I liked what you said about focusing on the individual steps without looking up too often - I look up to check my progress too frequently and end up feeling overwhelmed about how much is still left to learn. Thanks Ken!
Little steps are key! One thing that I’ve found to be super helpful in learning data science concepts is to read 3 google scholar articles on the topic at hand. I did this for the Dynamic Time Warping algorithm and I was surprised how I could start to understand the math after continued exposure. I still don’t have neural networks or naive Bayes methods down, but I hope I can do the same there.
Hi Ken, as always it's a pleasure to watch your video that distinguishes you from the many yackers around talking about data science. When i was listening to you, it came into my mind Tim Ferris and i thought about "selection", one of the key steps to learning. In my idea, to learn data science the first step involves figuring out what are the most frequently used and important mathematical models and algorithms. Once we have this shortlist, we can select key programming languages - i personally opt for Python and R - and try to work out real-world problems with these 2 languages. How, do we do to know if we are gonna do well? One way is to pick algorithms as performed in any of the courses you proposed (or even in Kaggle, where i have been starting giving a look), implementing to the real-world problems as said above and double-checking the solution by implementing the same algorithms using secondary, high-level programming environments like Matlab or Mathematica. I know that mathematica has lot of functions that help you do what in python requires dozens of lines. Just sharing with you this crazy ideas ;-) Ciao!
Hey! Your videos are always amazing. Can you make a video on the learning path for AI? I am a first year undergraduate in Computer Engineering and want to study AI, but I don't know the right sequence of approaching things. It would be very helpful of you if you could do it Regards🙂
@@KenJee_ds Thank you so much for showing concern. I am gonna stick to your data science learning pathway video until you make a one on AI. I am so elated that you replied🙂
Hey Ken, I recently graduated from university with a major in psychology and a double minor in computer science and mathematics. I struggled a lot with the learning environment of university, and unfortunately wasn't able to pursue a masters degree. Instead, I've taken a real interest in data science, more specifically machine learning, and have starting taking some courses online! I plan on doing a data science bootcamp later, which looks promising from the research I've done. My plan is extremely raw, but I hope to combine machine learning and psychology for the benefit of therapy and other aspects of the human mind! I just wanted your advice and general thoughts on my situation. Thanks so much! Love the channel by the way, so happy I found you.
Hi Saif - It is totally fine if the traditional academic environment didn't work for you! I don't think a masters is necessary to become a data scientist. I think going the online course route and bootcamp could definitely help you on your path. Still, I think it is still more likely you land a job as a data analyst or data engineer and then transition into a data science role. Thanks again for the support of my channel!
Here's my plan I'm good with computers, Python is sorted 1. Applied Data Science with Python on Coursera (got for free using university email) 2. Think Stats 3. Business Essentials on Coursera (again free) 4. Kaggle
Ken, I too was all over the place and felt like I wasn't going anywhere. So, a few months ago, I refocused and decided to learn SQL/NoSQL first. Just 2 weeks away from completing 8 certificates (just to give my learning journey some context), I estimate I'll be at an advanced intermediate level. My next "puzzle" will be math and statistics. Wish me luck going forward.
A video of yours (where you mentioned accountability) popped up in my RUclips feed after I missed a week or 2 of online data science class 😂. This video gave me at least enough confidence to continue.Thanks alot (from the Philippines).
Hi Ken , this video appeared right exactly when I just gave up doing my Coursera course in math for data science, as I couldn't solve function approximation task . Seems like I am too dumb and don't have enough math background to be data scientist . Science I only can code, but when it comes to advanced math related problems, I feel depressed. Did you have the same feelings or it just me ? These advanced math related problems really scare me 😓 didn't expect to face such problems where I don't even understand the task itself 😓
This is a normal feeling. If you watch one of my other videos, I actually failed Calculus the first time I took it. I ended up becoming a data scientist. You can't be scared of it though, it is a challenge, and challenges are fun!
Hi. Thank you for this video. Myself I’m a undergrad majoring in business information systems. I’ve been trying watching videos on data science and etc. I’m still trying to navigate what I want to do in the future with my major. Also I’ve been getting my certifications and areas in data science and cyber security too.
Hi Ken! Thank you for your video! To be honest I have literally watched almost all your videos and get excited when ever you post a video. Can you make another series on Data science projects like you did previously? Thank you!
Thanks for watching my videos! I plan to do more projects towards the end of the year. I have a lot on my plate with work right now, so I have been doing more talking and motivation videos, but the projects will pick up again soon!
This was a really great analogy towards the process of learning data science. This reminds me of my data structures class from freshman year where my professor said that even though that most people will have the same type of code to solve a problem, there are many different ways to approach it. Pretty much just like how there are many different ways to learn something. I'm still on this journey as well and just like the advice given here, I use resources that make the most sense to me when I'm developing projects. I relate to the small wins thing as well whether it would be making tutorials for Data Science concepts for the organization that I'm a part of, the Bit Project, or making projects through the knowledge that I have gained through tutorials. Of course my code wouldn't be as complex as someone else's work, the important thing is that I made something that I'm proud of and that the ability to continuously learn will get you far on this journey. Thank you for the wonderful content as always and I hope that you enjoy the rest of your day 🙂
Ken thank you so much for this video. All of the advice given are so valuable and I really needed to hear them. Thank you for all your amazing content.
hey ken jee, when are algorithms and data structures required in data science life cycle and when should you learn it when starting your learning process
I don't think you necessarily need to learn them when starting, I would try to understand them as they present themselves naturally. If there are some that you haven't had exposure to after doing multiple projects, it would then make sense to go back and learn them
You can also take people live on You tube with you.and sir when you will come live please you should tell us in your youtube community section so that we should be prepared for it
Thanks for watching! Free Kaggle Micro Courses: www.kaggle.com/learn/overview
47% Discount on 365 Data Science (Affiliate Link): 365datascience.pxf.io/Z7gXQ
how to make money while maintaining the respect your audience has for you - not many can do it on YT, kudos!
and you can't imagine how much help this video is. I often feel the bigger hurdles are inner/psychological reasons and it made me think: no that can't be
maybe it can
I think transparency is the key! My goal is to only do partnerships that I really believe will help out my subscribers. I am glad the video was helpful to you as well!
I already had a solid Math background when I started learning DS - but my graduation was 25 years ago :) I also had a LOT of programming experience, but not in Python or R. And still, my greatest problem wasn't the Math or the Python, it was *lack of the systematic approach*.
When you go for one of the countless "become a data scientist in 10 hours" course, you don't really understand that material. The worst thing is you might THINK you understand it, but that's a false feeling. There is no way to understand it all in 10 hours, be prepared for a long journey or retreat. There are three books that I recommend to everyone:
1). Mathematics for Data Scientists (M.P.Deisenroth and others) - all math you will need in a convenient form.
2). Fundamentals of Machine Learning for Predictive Data Analysis (Johh D. Kelleher and others) - everything about Machine Learning except for the neural networks.
3). Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurelien Geron - a great book about Deep Learning (you may skip the other parts).
All three books have a lot of exercises, and you should do ALL of them. After that you will know enough to consider your knowledge solid. I've stopped on the exercises for the very first chapter for now, though I've read the books, of course :)
Thanks for sharing the books Ilya!
Great books.
Other books I recommend :
Doing Math with Python by Amit Saha
Think Stats by Allen Downey
hey thanks for the inputs, what do you think of jake vanderplas' -data science handbook ?
Thanks for sharing . And NO RETREAT , no matter what it takes.
Thanks Ken for another awesome video. Just a moment ago I was about to find something to watch on RUclips and discovered your video in the recommended videos. Great analogy of mastering the rubiks cube and learning data science, particularly like the advice on breaking the learning to chunks and mastering each chunk. 😃👍👍
Thank you! Hopefully the analogy helps people to understand the struggle better.
Hey K, You are correct sir. My saying for the vast majority of 'life on earth' is "Small Victories". For instance, one rarely has *massive battles*, *huge upsets* and *gigantic victories*, instead we have many *Small Victories* that build to winning the battle.
I agree!!
Ken Jee Sir , Your video is really useful. I know the concept of data science but don't know how to practice programming.please suggest something to how should I practice the python to get better in programming use it for DS and ML .
There are python classes in my pinned comment (kaggle & 365 DS). This is also a good resource www.learnpython.org/
Hi Ken, It took me 1 year to learn to fully solve a rubix cube. For whole one year I was able to solve the 2 layers only, Finally I decided that I need to finish the cube and learn 3rd layer as well. Within a week I learned to whole cube. My personal best is 2 minutes 34 seconds now. But thats not the point. Point is now I am a hotel management student, with MBA in finance from Third tier institute, It is great deal in learning programming and stats , but that cube give me strength that I can. Sooner or later I will figure out everything and become expert. Thanks Ken Jee for inventing #66daysofdata to keep ourselves stick to the plan and get going.
Awesome to hear! You can do it Piyush!
Thank you.
Since learning that this field exists 6 months ago it's all I think about, and all I want to do. That being said, I have 0 technical background and am starting this journey at 40. Much harder for me to learn and retain at 40 than it was at 20.
As much as I beat myself up for not getting it fast enough, I also try to celebrate the small wins too.
Stumbled onto your channel a few days ago and can't get enough. Thank you for this, and all the other videos. They really help.
Hi James - Thanks for watching my videos! I'm glad you are finding them helpful! If there are any specific topics you would like to see from your unique perspective, please let me know. I don't think it is ever too late to learn data science, and I am glad that you are someone who truly enjoys it!
A great instructor who is putting out sincere content!
Thank you for the kind words!
I hope your Rubik's analogy isn't *too* accurate. I never solved more than one side of that damn thing in the 80s and gave up. I hope that doesn't mean I'm lacking the perseverance/chops for data science 😬
Haha Anyone can solve it with the power of the internet, just like data science!
Great vid, thanks for sharing. I'm particularly thankful for believing and expressing that the capacity of humans to learn anything one step at a time with persistence.
Thanks for watching!!
this video is a good guide for a beginner like me! "start somewhere and usually doesn't matter where" sounds like gradient descending my ML knowledge loss😂😂
Great analogy!!
Awesome... Simple very effective metaphor
Thanks a lot
Thanks for watching!!!
couldn't agree anymore :)
I think that kind of mindset must be had for all kind of goals that we want to achieve, because some people usually blame the abstract way instead of choosing one and develop little by little
Yes!
Ken Jee, I am always waiting for your content :). I hope to create something as useful!!
Thanks for watching! I think you definitely will create something useful!
Insightful. Thank you very much!
Thanks for watching Abhinav!
Hey Ken, I had just finish all the modules in 365 data science except for the new BA module. I am revisiting ML and DL(Tensorflow2) as i guess these are the basics which i have to be solid in to progress. The risk credit is the most difficult and i had yet to fully understand it. Time series and customer analytics are much easier to understand. The final five modules are basically implementation and are mainly ML. How should i advance? like learning more advanced ML and DL? Should i just go kaggle instead? or borrow books to enhance my skills? as i do not know how vast the world of ML and DL is. LOL. Thanks
I would go to kaggle! I would also experiment with some of the algorithms that 365 DS doesn't cover as much like Random Forests.
@@KenJee_ds Thank you
Awesome video! Which resources did you use for the maths ? :)
Thanks for watching! There is a link in the description of this video: ruclips.net/video/zSwM5uVeylU/видео.html. It has a bunch of free math resources
What r u doing guys?? Just press the like button... He's fucking amazing❤️❤️
Hi Ken! Does learning Computational Geometry help you in the field of Data Science?
I am sure it wouldn't hurt, but it I don't think it would be directly relatable.
Ken Jee Thank you !!
Hey Ken jee, I hope you'll be able to help me with my thought: does venturing into data science naturally means degrading your eyesight since you'll be staring at the screen? What can be done about it? And especially of the programming and casual gaming aspect, because
1) I hardly come across topics about eyesight when it comes to these
2) Asian parents equate computer screen = bad XD
Thanks for taking time off in advance!
I can't say it has been good for my own eyesight. I think that balance is really important. Generally, to mitigate problems like this, you need to spend a good amount of time outdoors. Sorry I couldn't be more helpful here, but thank you for watching!
For so long I anticipated my masters program in health data science with a focus on outcomes research, it's been a month and the python program is a huddle, maybe because I'm impatient, I get super frustrated when I run codes, and they don't go, I am quite smart, but I'm beginning to rethink this decision, I'm so confused, don't know what to do. I literally spent 9 hours on a task, and still didn't meet up with the deadline.
Ken sir you're absolutely correct.But remember when you got out of this hurdle and pass rubix or complete learning data science or anything you will get a great reward for it
There are great rewards, but this is where the rubiks cube analogy to data science breaks down. Unfortunately, I will never learn all of data science. I have to breaking it into chunks and conquering the smaller parts. The field is growing faster than I can learn which makes it very exciting!
@@KenJee_ds yes you are right
Hey been watch your channel for a while, great stuff...
Could we get an in-depth video on the differences between a Data Scientist and Data Engineer?
It would be really helpful.
I can look into it! I go in depth into how the data science roles differ in this video as well: ruclips.net/video/BZFfNwj7JhE/видео.html
Thanks for this video 👍🏻😃
Thanks for watching!
@ken good lesson. It's easy to get overwhelmed with the choices we have and stall your progress as a result. One tutorial, one project at a time. Keep up the good work!
Little by little, you will get there!
5:40 As a decent cuber I gotta ask,, do you know F2L, OLL and PLL ?
I see you too have Homo Deus there, another reason why I'm learning Data Science or technology in general hehe..
I actually haven't read it yet haha. Will be starting it soon!
@@KenJee_ds ain't nothing special. A more advanced Daniken, if you ask me.
Amazing video...I also believe in divide and conquer approach...which always works
Binary Search!
you entered the field when data science was something only a handful of people could do. now, you need much greater expertise to enter the field, esp with the economy the way it is.
That is true! You really need to differentiate yourself with projects!
Iconic Ending Jee, simple but powerful ♥️
Glad you liked it! Took a lot of practice haha
This is a good analogy, Ken. Luckily I took relevant courses step by step over the years, so I only had to focus on certain things when learning DS. I can only imagine how daunting it would be to jump over a huge hurdle (or to climb up a huge mountain as you mentioned) in front of people who are just starting to learn.
I believe this video can guide people who are just about to learn data science in the right direction.
It's important to take small steps at a time, instead of trying to do everything at once.
Thanks for watching! Looking forward to your next video!
@import data.. Will you please tell your learning process which helped you to learn DataScience.. i. e like step1 you learned python, step2.... anything like that
..
@@kirandeepmarala5541 Thank you for the suggestion. Will make a video on my learning process!
such an honest answer
Thanks!
I liked what you said about focusing on the individual steps without looking up too often - I look up to check my progress too frequently and end up feeling overwhelmed about how much is still left to learn. Thanks Ken!
It's hard to get into that habit, but it pays off! Thanks for watching!
As with the previous videos enjoyed the transparency and the analogy itself! Thanks!
Thanks for watching!
sir i learned 2 algorithms in python..is it enough for freshers to get a job.. apart i learned tableau and DBMS IN SQL
You will probably need to learn quite a few more than that! You should try to do 5-6 projects leveraging those algorithms
Little steps are key! One thing that I’ve found to be super helpful in learning data science concepts is to read 3 google scholar articles on the topic at hand. I did this for the Dynamic Time Warping algorithm and I was surprised how I could start to understand the math after continued exposure. I still don’t have neural networks or naive Bayes methods down, but I hope I can do the same there.
That is an awesome idea! I will try to implement that!
Thanks a lot, Ken. Can you please suggest some books to read, for Data Science. Thanks
I am reading this right now and it is pretty good: amzn.to/2UAyDzG
Thanks Ken, I have already started to break bigger problem to smaller manageable ones. I hope will learn the skills soon
I think if you break it down you will learn them in no time!
Hi Ken, as always it's a pleasure to watch your video that distinguishes you from the many yackers around talking about data science.
When i was listening to you, it came into my mind Tim Ferris and i thought about "selection", one of the key steps to learning.
In my idea, to learn data science the first step involves figuring out what are the most frequently used and important mathematical models and algorithms.
Once we have this shortlist, we can select key programming languages - i personally opt for Python and R - and try to work out real-world problems with these 2 languages.
How, do we do to know if we are gonna do well? One way is to pick algorithms as performed in any of the courses you proposed (or even in Kaggle, where i have been starting giving a look), implementing to the real-world problems as said above and double-checking the solution by implementing the same algorithms using secondary, high-level programming environments like Matlab or Mathematica.
I know that mathematica has lot of functions that help you do what in python requires dozens of lines.
Just sharing with you this crazy ideas ;-) Ciao!
Thanks for doing a Webinar with Krish Naik
No problem! We are filming another one this weekend!
@@KenJee_ds Great would be looking forward to that
Hey! Your videos are always amazing.
Can you make a video on the learning path for AI?
I am a first year undergraduate in Computer Engineering and want to study AI, but I don't know the right sequence of approaching things.
It would be very helpful of you if you could do it
Regards🙂
I can, I would say that the path to learning AI is very similar to learning data science!
@@KenJee_ds Thank you so much for showing concern.
I am gonna stick to your data science learning pathway video until you make a one on AI.
I am so elated that you replied🙂
Always giving the useful advice.
Glad you found it useful! Thank you for watching!
Wow I needed this after being overwhelmed yesterday. Thanks, man!
Happy I could help! I feel like you're going to crush it today!
Hey Ken, I recently graduated from university with a major in psychology and a double minor in computer science and mathematics. I struggled a lot with the learning environment of university, and unfortunately wasn't able to pursue a masters degree. Instead, I've taken a real interest in data science, more specifically machine learning, and have starting taking some courses online! I plan on doing a data science bootcamp later, which looks promising from the research I've done. My plan is extremely raw, but I hope to combine machine learning and psychology for the benefit of therapy and other aspects of the human mind! I just wanted your advice and general thoughts on my situation. Thanks so much! Love the channel by the way, so happy I found you.
Hi Saif - It is totally fine if the traditional academic environment didn't work for you! I don't think a masters is necessary to become a data scientist. I think going the online course route and bootcamp could definitely help you on your path. Still, I think it is still more likely you land a job as a data analyst or data engineer and then transition into a data science role. Thanks again for the support of my channel!
Ken Jee Appreciate the response!
Thank you for sharing this is totally resonates to me, thank you :)
Thank you for watching!
Hello sir you're a sports analyst know i have one idea regarding duck worth Lewis method how i can contact you for sharing the idea plz reply sir
My email is in the about section of my page
Sir i am unable to send mail by using the mail id which you provided in your channel
Hola hola
Here's my plan
I'm good with computers, Python is sorted
1. Applied Data Science with Python on Coursera (got for free using university email)
2. Think Stats
3. Business Essentials on Coursera (again free)
4. Kaggle
Sounds like a good plan to me!
Ken, I too was all over the place and felt like I wasn't going anywhere. So, a few months ago, I refocused and decided to learn SQL/NoSQL first. Just 2 weeks away from completing 8 certificates (just to give my learning journey some context), I estimate I'll be at an advanced intermediate level. My next "puzzle" will be math and statistics. Wish me luck going forward.
Awesome stuff! I wish you the best of luck. You've come a long way!
A video of yours (where you mentioned accountability) popped up in my RUclips feed after I missed a week or 2 of online data science class 😂. This video gave me at least enough confidence to continue.Thanks alot (from the Philippines).
Thanks for watching! Happy to hold you accountable haha
Another great video!
Awesome stuff! Glad that this analogy hit home!
Hi Ken , this video appeared right exactly when I just gave up doing my Coursera course in math for data science, as I couldn't solve function approximation task . Seems like I am too dumb and don't have enough math background to be data scientist . Science I only can code, but when it comes to advanced math related problems, I feel depressed. Did you have the same feelings or it just me ? These advanced math related problems really scare me 😓 didn't expect to face such problems where I don't even understand the task itself 😓
This is a normal feeling. If you watch one of my other videos, I actually failed Calculus the first time I took it. I ended up becoming a data scientist. You can't be scared of it though, it is a challenge, and challenges are fun!
Hi. Thank you for this video. Myself I’m a undergrad majoring in business information systems. I’ve been trying watching videos on data science and etc. I’m still trying to navigate what I want to do in the future with my major. Also I’ve been getting my certifications and areas in data science and cyber security too.
Happy the video helped!
great vid man, i also come from business background , and now struggling to learn DS and ML especially the math part. keep up the good job
Thank you! If I could do it, I am sure you can too!
Congratulations on 50k subscription... Eagerly waiting for next videos as well
Thank you! More to come every Monday and Friday!
Hi Ken! Thank you for your video! To be honest I have literally watched almost all your videos and get excited when ever you post a video. Can you make another series on Data science projects like you did previously? Thank you!
Thanks for watching my videos! I plan to do more projects towards the end of the year. I have a lot on my plate with work right now, so I have been doing more talking and motivation videos, but the projects will pick up again soon!
This was a really great analogy towards the process of learning data science. This reminds me of my data structures class from freshman year where my professor said that even though that most people will have the same type of code to solve a problem, there are many different ways to approach it. Pretty much just like how there are many different ways to learn something. I'm still on this journey as well and just like the advice given here, I use resources that make the most sense to me when I'm developing projects. I relate to the small wins thing as well whether it would be making tutorials for Data Science concepts for the organization that I'm a part of, the Bit Project, or making projects through the knowledge that I have gained through tutorials. Of course my code wouldn't be as complex as someone else's work, the important thing is that I made something that I'm proud of and that the ability to continuously learn will get you far on this journey. Thank you for the wonderful content as always and I hope that you enjoy the rest of your day 🙂
Glad you enjoyed it! Cool how it was applicable in your life!
Thank You Ken. Your videos are really helping me to change my perception towards this new challenge. More power to you man!
Glad to hear! Thank you for watching!
Thank you very much.
Thanks for watching!!
Wow Ken, it is amazing to see how much your channel has grown when you had a few hundred subscribers and now 50k! Keep up the good work!
Thank you for watching me during my journey so far!
Ken thank you so much for this video. All of the advice given are so valuable and I really needed to hear them. Thank you for all your amazing content.
Thanks for watching! I am glad you found the advice helpful!
Thank you so much for your content! I feel more confidence, drive, and motivation when I follow your advice.
This makes me so happy to hear! Glad I could help!
Your are seriously underrated
Thank you!
Hey man, knowing that it's not that uncommon to struggle in these topics is quite relieving, thanks for sharing!
I think almost everyone goes through it. I certainly did!
Amazing as always, Ken! :)
Thanks for watching!
This channel is growing so fast, love it. Thank you for all the amazing videos !!
Thank you for watching them!
hey ken jee, when are algorithms and data structures required in data science life cycle and when should you learn it when starting your learning process
I don't think you necessarily need to learn them when starting, I would try to understand them as they present themselves naturally. If there are some that you haven't had exposure to after doing multiple projects, it would then make sense to go back and learn them
Thank you very much for making me realize I'm not the only one facing these hurdles.
you are not alone!
Hey, Ken. This is fantastic! Narrating your experiences in such a simple and easy way makes it quite encouraging for beginners. Thank you!
Thanks for watching! I have really been working on breaking things down as simply as possible, I appreciate that you noticed!
@@KenJee_ds Yes, I did notice and possibly others too. Well done!
100% did not see that Rubix cube coming 😂
Got ya!
Thumbs up to the rubiks cube analogy
Thanks! I had a lot of fun with it!
Following him since 4k subs❤️❤️❤️
Thanks for the support for so long!
100k in the next 2 months Bet it......
That would be awesome haha!
Ken Jee sir i am excited for your Q&A .What about coming live for it on RUclips
That is what I am planning! Will probably happen in a few weeks. I need to test how the live streaming works
You can also take people live on You tube with you.and sir when you will come live please you should tell us in your youtube community section so that we should be prepared for it