Thanks for watching everyone! If you're interested in the projects I mentioned in the video they are linked here: Project From Scratch: czcams.com/play/PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t.html CZcams Leaderboard: czcams.com/play/PL2zq7klxX5AR8gVxZ-4w1vsVNTO0RDO0_.html Ball Classifier: czcams.com/video/vy-R4oUZaC8/video.html&ab_channel=KenJee Kaggle Projects: czcams.com/play/PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO.html
Ken - Great video as always, but I think you uploaded the version with the wrong answer to Mistake 1. The correct answer to where to start is 365 Data Science Courses (using the link in the video description above to access them, of course).
100% agree on getting your hands dirty early with projects! When I started, I tried various courses and MOOC's but nothing ever stuck. The moment I started attempting my own projects I started to see progress in my learning and I finally started to retain the information I was learning :)
HI KEN!! Only two months ago, I started my journey by watching your video(beginner's guide) and today got my first Internship ❤️ thank you for being my ultimate source of motivation!!! Btw Happy Ramadan!! ❤️❤️
Hands-on projects, especially small, focused projects, from the outset will accelerate learning of both the theoretical concepts and the practical skills. For example, learn Python while learning a data acquisition technique provides context and motivation. This approach applies to each stage of the data science workflow and to each of the technologies. Revisit earlier projects and add another stage of the workflow.
It was overwhelmingly scary at the beginning, but now I think I know 'what not to do' if I am to make a path out of my passion for Data. Thank you for coming up with such videos. They are a great help.
Thank you Ken, this video certainly gives me direction and reminder. I love this saying so much, “ Data science isn’t a competition, it’s a long journey of slow and enjoyable progression.” It’s so true!!!
1:53 Wow USB-powered Papayas? Great insights Ken! Yes totally agree with you on the importance of domain knowledge which also helps to differentiate us from others.
is it necessary to double major in whichever domain you want to work with? I was a double Stat and econ major but dropped the econ for financial reasons
Excellent Insights! I totally agree with the point of not applying your knowledge and focusing only on theory as being one of the biggest hurdle for someone new in the field.
I still consider myself a beginner after almost two years of learning. Listen to what he says. He is spot on! I would also add that if TOUS start a MOOC make sure you finish it unless the content seems to advanced for your current level
Learning data science is like an ML model ... The more you train it the better result you get... Thanks for the tips. At what point one can say or should consider that data science is not for them?
thank you 😊 just started this journey. It has been a month, and loving it already. I know it is going to be tough in upcoming days or months but I am up for it!
Mistake nr 8 : data leakage between your training and testing sets. I saw that being made by an experienced data scientist in my first job. It was ignored and a 85% precision/recall score was announced to management.
Hi Ken ! Codebasics channel brought me here. Starting from scratch. 1 month in to learning. Trying to change career. Your channel is amazing. Thanks and please keep posting. Following your projects and works.
I have been working on data analytics projects for 2-3 months now and i have to agree, i am making so many mistakes and so many things are getting lost but the more i work, the more i am learning and the more im practicing and its been a great experience for me honestly. Data analyst/science is probably the most fun job out there and the most challenging one.
I've been on my data science journey for a while now and the mistake of neglecting domain knowledge was a big one for me too. Great to see I wasn't the only one :)
Hey Ken just wanted to take the moment to congratulate you for how far you've come. I remember when you were a bit more underground which is just so crazy to think about. I'm super glad to have experienced your growth as your content continues to help me along my own journey, be it about data science/analytics or your philosophies. Anyways, looking forward to more awesome videos and can't wait to someday make it on your podcast!
Thanks Rumil! It really means a lot to me that you've been with me on this journey for so long! Comments like these make me really excited to produce new content each week!
Thank you for the tips. I have resonated with the tip on getting involved in projects. I started working on projects recently and there's been immense growth in my understanding of concepts.
Hey one thing I missed: the right learning succession. You need to know your basics and that is calc 1-3 or in Europe mostly analysis 1 and 2. Linear algebra. And from there on stats 1 and 2. And than coding, machine learning and what not. Or regression. But the key is to know your basics. I so often encountered a problem where I missed math knowledge so, people. Learn math and Stats.
Thank you Ken ! The fire of obsession and stuck time are strongly correlated negatively for me, but as soon as I take a break and get my ... together I find success in the end. I used to wonder if I really love the field, but after listening to you in this video, I realized that feeling this way should be quite common. I would also add that a big mistake for DS beginners is not watching Ken and Tina's videos :) .. You guys give us all we need to stay on track
Thank you for this video. I'll will revisit this video again and again throughout my DS internship this Summer. I've already fell into some of these traps :O
How I spend my first hour on Saturday mornings. Thanks for the continued motivation and i hope to take your support on here and use it to apply simple ML for my project. 🙂🙏
Thank you for watching!! I would say that almost every data scientist has made at least one of these mistakes. I on the other hand have made almost all of them haha
I’m a subscriber from South Korea. Your videos always help me to make my decisions when I have complicate thoughts. Thanks for your videos, and I’ll keep enjoying your channel!👍
You always hear it from seasoned data scientists; just start. But where - right? My advice 1)do small projects. 2)complete a project. 3)literally, rewrite a project you find an example for on CZcams or a GitHub. Literally, type the solution out word for word. This will allow you to feel the data science flowing through your fingers. Then go back and modify the project you just built. Modify it just enough to change the results. Now you feel the data science even more. Your confidence just went through the roof. Like he said, learn just enough. Cause the reality is that you’ll only use 1/10th of what you learned. At least at the beginning. No one is going to make you a lead out the door. If they do, quit or turn down the job. Also and most important, realize a data scientist in IT is not the same data scientist in Finance. I’m a SQL SME in accounting and finance. but if I hang out with IT SQL SME’s, he’s from mars and I’m from Jupiter. We speak SQL but we use SQL for different goals. Data scientists are the same. Don’t get distracted. You’ll find your place. Just keep DSing.
Thank you for the video, Ken. I'm just starting to learn about this field and my interest is only increasing. My nowaday's goal is to learn to program in Python.
I failed my second course R for the certification of Data Science. Your are right about giving up easily since I am a beginner. I will re-do and study harder. Thank you! Your channel helps me a lot!!
Thanks for this video, Ken! I'm getting into the part where i am "appllying the theory" in projects and i feel like i've learned nothing at all hahaha It's always good to hear some encouragement from others
I appreciate that you made mention of a community. I am always seeking to assure myself that I am not alone. Is Kaggle the only place that you know of where such a community can be found?
Thank you for the motivation Kem. I am not the one who is good at maths and statistics . But I love to solve problems in real world . Also somewhere in my mind says data science is good career path for you . So doing it❤️❤️❤️
I actually didn't complete one course properly and leave in between . Big mistake take a course and religiously complete it because if you change course you will have to start again because of different route for different courses
Fantastically helpful video. Thanks for creating something so uplifting and straightforward! Really excellent reminders and motivation to keep going on my learning path.
I'd personally recommend watching some free videos on a topic on yt before enrolling in a course. This way you can easier choose a good course because you already know what to look for, and since you already know some basics as you watched that free stuff, the material sinks in better because you are repeating it again while watching the course you chose (plus you get a different angle on the topic). As you say and I can confirm (from a PhD perspective) ;) getting more then one point of view is important. People have a bias and some things are obvious for one person while difficult for another, the choice of words is very important too. Mine main problem is I like theory too much plus I tend to start too many topics at once. I get too impatient and can start NLP topics even though I should be doing e.g. clustering, and NLP is not on the plan for the next few months at least. This has some advantages too (not only bad sides!) - when I get to NLP it's not all new and sinks in better, but me doing that makes the whole process chaotic. Well, I try to pat myself on the back that - in the end - if I fill all the knowledge gaps - it's not that important in what order I did it... Still I know I need more order and systematic approach. And more practice. Other issue with learning DS, or every new thing really, is that you don't know how long it will take you (coz it's new to you and you don't know it so how can you plan how long it will take if you don't even know what are all the things you must learn... if it makes any sense). So - yeah, don't get crazy about deadlines, just focus on the progress. Another pro-tip - browse job offers to see what the companies want. I think this may be area and industry specific - in my area they want people good with databases, Hadoop, Spark and other Big data associated things more then they want Deep Learning & AI specialists. Kinda makes sense when you think of it... So for me, on my road to Data Science, I decided to go back to my analytics job that will let me practice python&SQL more, get my bills payed ;) and practice the DS stuff in my free time (coz I can already see with the Big data things and me still needing to work on my DL skills more this can easily take another 6 months...) So yeah, some comments on Data Science learning from someone who has been learning it for about 6 months ;)
@@KenJee_ds What do you think about places like LeetCode? Which ones would you recommend? I am looking for python coding exercises as I want to increase my python proficiency to intermediate. :)
I hope I learned this early on my early years but I'm still 20 so I have more time to start. thanks for your video, it helped me internalize what you said since Im always burn out before and tried so many stuffs on self dev but the truth is "I have to be honest with myself" and have my own system not copying others. I will become data scientist for next 2-3 years :)
That video actually came in a good time cause I'm really burning out im my first solo project. Even though I'm not in such a bad position, made a lot of courses and I wrote my first "import pandas as pd" five weeks ago I always feel like I'm having a slower progress than I should. I'll take it easy with myself and start with smaller jobs
Point #1 I agree 100%. I'm into sports so learnt quickly that even a bad tennis coach is better than no coach for novices! So I just googled data camp and went for it
Excellent suggestions! All of these are very helpful. I do think I am going to go through a project with you so I can start learning. I will let you know which one I start with and how it goes.
It's a cruel experiment, but there was an experiment on rats in water. It showed that a rat will drown in 15 or so minutes if left unattended, but if you save the rat right as it submerges,, and put it back in the water, it'll keep swimming for another 60+ hours without drowning. It applies to your concept of us always having 100% more to give. In this case it was more than 100% more.
Hey Ken, have you ever thought on making a video on what is important when you are in a data science role already. Would be quite interesting for me to hear your opinion. I sometimes do not know if I should dive VERY deep into some specific topics or just use my freetime to learn something somewhat related. For example I love working with REST Apis and even some Vue Frontend stuff which will not make me a better data scientist, but are also fun... Since I am also a content creator I something think the same way about creating videos. What´s your opinion?
Hey! That is a really good idea, I think it may be a super difficult one to put together though. I've found that what you should learn in the role really depends on the company you're working at as well as your own personal goal, so it isn't quite as easy to generalize as what people newer to the field should focus on. It's a bit round about, but I hope that some of my podcast episodes can help point to what some other data scientists are doing in this area.
Thank you for the content! I believe I need to apply the theory to projects more. I haven't yet worked on, let's say, real projects, except for some Kaggle practice and reading. A few days ago, I set out to create a substantial project involving extracting valuable insights from the remote job market, and I'm finding it quite challenging to locate suitable data sources. I mean, I'm investing a lot of time exploring techniques to attempt scraping LinkedIn, for example, but it hasn't been yielding any results so far. I would appreciate any suggestions :)
There may be some data on kaggle available! I also made a glassdoor scraper in one of my older videos. It probably needs some cleaning up to work, but the source code is there
Hi Ken Thanx. me a hard core biologist- of late translational research- fascinated by data science n m encouraged by ur video. look forwrd to train myself. send ya happy thots frm Himalayan foothills India. Stay blessed. 🌸🙏🌸
Rip papaya you have , eat it first you must . hahaha. Nice video. I too believe self evaluation is very important before entering in any field. And the field like DS which requires lots and lots of time and learning efforts its crucial to know before you start. Thanks for making people aware of that Ken.
Mistake I personally made, I didn't learn enough math. And I thought that I could brute force my way through a data science course with some hodgepodge math. I think really getting that foundation is pretty important and it can be done in a singular course, it doesn't have to be a year of university math classes
Hey! Which course was a good generalization? I know I lack the math background but sometimes the material starts off simple but goes from zero to hero in a few classes...
Even I denied math and stats at first then understood how learning this will helps us know the what happens behind the scene helping us choose which model to choose for specific purpose 🙌
@@azwin6506 if you lookup "math for data science" course there are a couple on coursera, one by duke and another by a university who's name escapes me. I would go with the one who's name I can't remember, it seems to be more substantial
Hi Ken...thanks for your content. I do a lot of econometrics..learning modelling data (Logit, Ardl, VARs). I think this is a good thing but which skills do i urgently need to learn to call myself a data scientist in your view? Of course divibg deeper into programming etc?
I think #3 is person dependent. If you become to reliant on a coach/mentor then your problem is your mentor not coaching you to become a more self-sustainable data scientist. I'd have killed for a bit more guidance in my formative career years rather than go and try lots of solutions. Maybe it's person dependent? Either way, great video. Liked and subscribed!
Unrelated question here!, So Im a junior physics student right now looking to become a data scientist in the future, what is the fastest route to become a data scientist ? Land a data scientist internship, get a data engineering/analyst job, software engineering job, etc? Also I live in San Diego and don't plan on moving is this going to cripple my chances to find work?
I think physics is a really strong background for data science. I think you nailed it on the fastest route. There are plenty of jobs in San Diego, so you should be fine on that front.
Very informative video. As a IT student myself, I don't know whether I should choose to study data science or network enginner in my next degree course. As I have no experience, idea, knowledge of data science. Do you think it's a great idea to move to data science career or to other IT career? Would appreciate your thoughts on this. Thanks!
I think either is a really strong option! I would do some projects and experiment a bit more and you should be able to figure out what you have the most interest in
So, I am not making most of the beginner mistakes. But I have one question Ken. I don't know enough. Should i see the projects on CZcams and try to implement them by copying and understanding them? And you told about plagiarism in DS. Will it be like I plagiarized those projects ??
Hi Aamna - It is fine to copy these projects if you are using them for practice and not sharing them as if they were your own. I think copying is perfectly fine for learning, but you should start doing your own original analysis over some time! I hope this helps!
Random Q: isnt there a UI (end user stuff) for getting arranging statistics/data instead of doing the hard coding??? (Im guessing all data is in DB SQL like formats so you cant use end user style tools?).
all is clear thanks but i have one doubt i completed python basics and thinking of data structures (dsa) is dsa needed or required skill for data science or not ? plz reply 🐱
@@KenJee_ds Can we like stop this hate between y'all two. Both are amazing following both and both have good knowledge to share in their own way. Like you said there is no one right way. Please 🙏🏻❤️
@@krishnabarfiwala5766 you are stupid if you think there is hatred between these two. This is friendly battle. Better check Ken's other videos and Alex's comments on each of them.
Hi! I like watching your videos. Love and support from India ♥️ There's some thing in particular that I'd like to ask. 1) It's been some time since I completed MySQL course. However i have gone through the videos a few times but still I am not able to gain the confidence of saying that I know SQL. What do you suggest here? 2) Practical knowledge is very important and I am unable to get that. I have a dataset. I make up problem statements in my mind and try solving it. But that is clearly not helping at all. So can you suggest me some places where I can apply the knowledge of Sql that I gained through the course? Course I took: 365 Data Science.
Hey - For SQL, you can always go on websites like hackerrank to practice the questions there. You could also make your own database and explore it with queries like I did in this video: czcams.com/video/WKXtTo7lqmA/video.html&ab_channel=KenJee For the projects, I would start with kaggle. Instead of starting from scratch, review a bunch of different workbooks on the same dataset. See how others have approached this problem. Then try to expand on someone else's work with new questions you have. Once you get the hang of it you will create your own system for approaching these problems. czcams.com/video/-pdXWmj9xxU/video.html&ab_channel=KenJee
hey ken jee, thanks for the info. i am 30 and a total bum. so, i'd like to quit being a bum. this looks like a great career. what do you advise for someone who would like to get started even in something related and not necessarily in it, where from he/she could continue to learn while earning to then move up and /or into this field?
I think trying to go down the data analyst route would make a lot of sense. Getting familiar with tools like SQL, microsoft excel, and maybe some programming languages. Alex the analyst has a great channel on this too!
I'm starting with Advanced Excel and thinking of learning Data Analysis or Business Analysis before deciding on making the big leap into Data Science. For me personally, I think understanding the business side of the data and how to interpret/present your data are a good place to start. If you're a bum, the easiest point of entry or the "least path of resistance" are usually the best for those who are unmotivated. It's important to know thyself. I know that if I get overwhelmed or bored, I'll quit. So the learning process has to be simple, well-paced, and/or fun. Maybe this is the perspective you should take in your approach as well.
Thanks for watching everyone! If you're interested in the projects I mentioned in the video they are linked here:
Project From Scratch: czcams.com/play/PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t.html
CZcams Leaderboard: czcams.com/play/PL2zq7klxX5AR8gVxZ-4w1vsVNTO0RDO0_.html
Ball Classifier: czcams.com/video/vy-R4oUZaC8/video.html&ab_channel=KenJee
Kaggle Projects: czcams.com/play/PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO.html
You might wanna recheck the data collection part of your end to end project, it doesn't work anymore...the site has changed a ton
Can you send me the self help books please?
Ken - Great video as always, but I think you uploaded the version with the wrong answer to Mistake 1. The correct answer to where to start is 365 Data Science Courses (using the link in the video description above to access them, of course).
Pro tip: watch movies on flixzone. Me and my gf have been using it for watching lots of of movies recently.
@Anders Bodie yup, have been using Flixzone} for years myself =)
I wish I saw this at the beginning of my data science journey - could have saved me a lot of headaches and time! Awesome video Ken!!
Thanks for watching Tina! Same could be said for your videos haha
This is the case with most of the learners😬 but Better late than never ✌️😀
Same, Tina!
Your videos are great as well. I love your embrace of accountability with your "Study with me" streams. Thanks.
Dam I love to see you guys Interac (all the data CZcamsrs team. 😭)
100% agree on getting your hands dirty early with projects!
When I started, I tried various courses and MOOC's but nothing ever stuck. The moment I started attempting my own projects I started to see progress in my learning and I finally started to retain the information I was learning :)
💪
HI KEN!! Only two months ago, I started my journey by watching your video(beginner's guide) and today got my first Internship ❤️ thank you for being my ultimate source of motivation!!! Btw Happy Ramadan!! ❤️❤️
That is incredible!! Congrats Aimen!!
Hands-on projects, especially small, focused projects, from the outset will accelerate learning of both the theoretical concepts and the practical skills. For example, learn Python while learning a data acquisition technique provides context and motivation. This approach applies to each stage of the data science workflow and to each of the technologies. Revisit earlier projects and add another stage of the workflow.
Completely agree!
It was overwhelmingly scary at the beginning, but now I think I know 'what not to do' if I am to make a path out of my passion for Data. Thank you for coming up with such videos. They are a great help.
Thanks for watching Shilpa! Glad to hear it helped!
Thank you Ken, this video certainly gives me direction and reminder. I love this saying so much, “ Data science isn’t a competition, it’s a long journey of slow and enjoyable progression.” It’s so true!!!
Glad to hear Victor! Thank you for watching!!
Yeah, The community helped me to create the learning habit. Thank you so much, Ken.
Really great to hear Thinam! You have had one of the longest streaks ever!! Has been a pleasure having you as part of the community
I really appreciate it, Ken. It has already become part of my daily routine. I will continue it.
I think the habit is the most important thing for us beginners. Best of luck.
1:53 Wow USB-powered Papayas? Great insights Ken! Yes totally agree with you on the importance of domain knowledge which also helps to differentiate us from others.
Other way around, data science powered by papayas 😂
@@KenJee_ds Haha, that's a good one!
is it necessary to double major in whichever domain you want to work with? I was a double Stat and econ major but dropped the econ for financial reasons
Usb powered papayas lol😂😂
Excellent Insights! I totally agree with the point of not applying your knowledge and focusing only on theory as being one of the biggest hurdle for someone new in the field.
Yes! You see some pretty funny insights from people that have no exposure to the field of study haha
I still consider myself a beginner after almost two years of learning. Listen to what he says. He is spot on! I would also add that if TOUS start a MOOC make sure you finish it unless the content seems to advanced for your current level
Great advice Mikey! Thank you for watching. I still have a tremendous amount to learn myself!
As someone who's just starting to learn and explore data science, this is really helpful. Thank you Ken!
Glad to hear this was helpful Ardiansyah!
Learning data science is like an ML model ... The more you train it the better result you get... Thanks for the tips.
At what point one can say or should consider that data science is not for them?
So true!
Yepp
Mistake 2 and 4, hit me right in the feels. Definitely guilty of those when I started. Preach Professor Ken, preaaach!!
Haha I did both of those real bad! We get past them together!
@@KenJee_ds also, color grading on the vid is 🔥
thank you 😊 just started this journey. It has been a month, and loving it already. I know it is going to be tough in upcoming days or months but I am up for it!
Awesome!!
"Data science is a marathon and not a sprint"- Nice line ken! Keep going, very helpful video
Yes! Thank you!
@@KenJee_ds welcome!
Mistake nr 8 : data leakage between your training and testing sets. I saw that being made by an experienced data scientist in my first job. It was ignored and a 85% precision/recall score was announced to management.
True!!
Hi Ken ! Codebasics channel brought me here. Starting from scratch. 1 month in to learning. Trying to change career. Your channel is amazing. Thanks and please keep posting. Following your projects and works.
Codebasics is awesome!! Will definitely keep posting haha. Thank you for watching my videos!
I have been working on data analytics projects for 2-3 months now and i have to agree, i am making so many mistakes and so many things are getting lost but the more i work, the more i am learning and the more im practicing and its been a great experience for me honestly. Data analyst/science is probably the most fun job out there and the most challenging one.
As long as you're learning from them!
I've been on my data science journey for a while now and the mistake of neglecting domain knowledge was a big one for me too. Great to see I wasn't the only one :)
Glad to see that you had a similar experience! Thank you for watching Leon!
Hey Ken just wanted to take the moment to congratulate you for how far you've come. I remember when you were a bit more underground which is just so crazy to think about. I'm super glad to have experienced your growth as your content continues to help me along my own journey, be it about data science/analytics or your philosophies. Anyways, looking forward to more awesome videos and can't wait to someday make it on your podcast!
Thanks Rumil! It really means a lot to me that you've been with me on this journey for so long! Comments like these make me really excited to produce new content each week!
Thank you for the tips. I have resonated with the tip on getting involved in projects. I started working on projects recently and there's been immense growth in my understanding of concepts.
Thanks for watching Natasha!
"Give light and people will find the way" - Ella Baker.
Txs Ken.
Thanks for watching Paulo!
@@KenJee_ds 🙏
Nic quote
Great video, ken.
I found this video very useful as a beginner to data science.
Really glad to hear! Thanks for watching!
Hey one thing I missed: the right learning succession. You need to know your basics and that is calc 1-3 or in Europe mostly analysis 1 and 2. Linear algebra. And from there on stats 1 and 2. And than coding, machine learning and what not. Or regression. But the key is to know your basics. I so often encountered a problem where I missed math knowledge so, people. Learn math and Stats.
I almost cried when the video cut over to your introspection clip😂😂😂. Good stuff Ken jee!
Aww haha
Thank you Ken ! The fire of obsession and stuck time are strongly correlated negatively for me, but as soon as I take a break and get my ... together I find success in the end. I used to wonder if I really love the field, but after listening to you in this video, I realized that feeling this way should be quite common. I would also add that a big mistake for DS beginners is not watching Ken and Tina's videos :) .. You guys give us all we need to stay on track
Yes, it is a very common problem!
Love your videos!! As I’m about to graduate with my Masters in a few months, they give me a confidence boost
Thanks for watching Cynthia! Early congrats on graduating!
Thank you for this video. I'll will revisit this video again and again throughout my DS internship this Summer. I've already fell into some of these traps :O
Excited to hear how your internship goes!
Agreed on knowledge of the topic as it helps you understand the data and you have an idea on what to look out for.
How I spend my first hour on Saturday mornings. Thanks for the continued motivation and i hope to take your support on here and use it to apply simple ML for my project. 🙂🙏
haha you rock Assad! Crush your project!
getting to see this video at the start of my data science journey ... this is certainly making a lot of sense now
Glad to hear!
Thank you Ken for sharing your genuine thoughts on this topic. Not gonna lie, I find myself doing some of them too
Thank you for watching!! I would say that almost every data scientist has made at least one of these mistakes. I on the other hand have made almost all of them haha
I’m a subscriber from South Korea. Your videos always help me to make my decisions when I have complicate thoughts. Thanks for your videos, and I’ll keep enjoying your channel!👍
Glad to hear the videos are heling to simplify things! Thanks for watching my videos!
You always hear it from seasoned data scientists; just start. But where - right? My advice 1)do small projects. 2)complete a project. 3)literally, rewrite a project you find an example for on CZcams or a GitHub. Literally, type the solution out word for word. This will allow you to feel the data science flowing through your fingers. Then go back and modify the project you just built. Modify it just enough to change the results. Now you feel the data science even more. Your confidence just went through the roof.
Like he said, learn just enough. Cause the reality is that you’ll only use 1/10th of what you learned. At least at the beginning. No one is going to make you a lead out the door. If they do, quit or turn down the job.
Also and most important, realize a data scientist in IT is not the same data scientist in Finance. I’m a SQL SME in accounting and finance. but if I hang out with IT SQL SME’s, he’s from mars and I’m from Jupiter. We speak SQL but we use SQL for different goals. Data scientists are the same.
Don’t get distracted. You’ll find your place. Just keep DSing.
Very true! Great advice!
Sit thanks.......really from the bottom of my heart thanks....
Happy I could help! Thank You for watching!
Bravo, Ken. You´ve certainly figured out a lot of this right. Thanks for sharing!
Thanks for watching Jan!
Thank you for the video, Ken. I'm just starting to learn about this field and my interest is only increasing. My nowaday's goal is to learn to program in Python.
Thanks for watching! Sounds like a great goal!
I failed my second course R for the certification of Data Science. Your are right about giving up easily since I am a beginner. I will re-do and study harder. Thank you! Your channel helps me a lot!!
Awesome to hear about your perseverance!
Thanks for this video, Ken! I'm getting into the part where i am "appllying the theory" in projects and i feel like i've learned nothing at all hahaha
It's always good to hear some encouragement from others
It happens, but you get through it!
The most refreshing thing about your videos is the honesty. Almost as much as the advise.
That and seeing your dad make Cameos 🤣
Thanks James! He went back home so no data cameos for another 4 months or so 😢
I appreciate that you made mention of a community. I am always seeking to assure myself that I am not alone. Is Kaggle the only place that you know of where such a community can be found?
Thank you for the motivation Kem. I am not the one who is good at maths and statistics . But I love to solve problems in real world . Also somewhere in my mind says data science is good career path for you . So doing it❤️❤️❤️
Love it!
Thank you very much. I like your honesty.
Thanks for watching!
Thanks for shedding light on this amazing journey.
Thanks for watching Opeyemi!
Those are spot on .
These are few of the mistake I made when I was starting
I actually didn't complete one course properly and leave in between . Big mistake take a course and religiously complete it because if you change course you will have to start again because of different route for different courses
Networking and engaging is best way to connect with various people in this field and get there advice or get mentorship
Thanks for sharing Shruti!!!
@@shrutijain1628 I will make sure I complete one completely thanks for sharing
Helpful as always 🙌
Thank you for always guiding beginners like me 🙇🏻♂️
Glad to hear it was helpful Shubham! Thanks for watching!
You are a great mentor, Ken!
Thanks for sharing this content as well!
Thanks for watching Julio!
Fantastically helpful video. Thanks for creating something so uplifting and straightforward! Really excellent reminders and motivation to keep going on my learning path.
Thanks for watching!!
I'd personally recommend watching some free videos on a topic on yt before enrolling in a course. This way you can easier choose a good course because you already know what to look for, and since you already know some basics as you watched that free stuff, the material sinks in better because you are repeating it again while watching the course you chose (plus you get a different angle on the topic).
As you say and I can confirm (from a PhD perspective) ;) getting more then one point of view is important. People have a bias and some things are obvious for one person while difficult for another, the choice of words is very important too.
Mine main problem is I like theory too much plus I tend to start too many topics at once. I get too impatient and can start NLP topics even though I should be doing e.g. clustering, and NLP is not on the plan for the next few months at least. This has some advantages too (not only bad sides!) - when I get to NLP it's not all new and sinks in better, but me doing that makes the whole process chaotic. Well, I try to pat myself on the back that - in the end - if I fill all the knowledge gaps - it's not that important in what order I did it...
Still I know I need more order and systematic approach. And more practice.
Other issue with learning DS, or every new thing really, is that you don't know how long it will take you (coz it's new to you and you don't know it so how can you plan how long it will take if you don't even know what are all the things you must learn... if it makes any sense). So - yeah, don't get crazy about deadlines, just focus on the progress.
Another pro-tip - browse job offers to see what the companies want. I think this may be area and industry specific - in my area they want people good with databases, Hadoop, Spark and other Big data associated things more then they want Deep Learning & AI specialists.
Kinda makes sense when you think of it... So for me, on my road to Data Science, I decided to go back to my analytics job that will let me practice python&SQL more, get my bills payed ;) and practice the DS stuff in my free time (coz I can already see with the Big data things and me still needing to work on my DL skills more this can easily take another 6 months...)
So yeah, some comments on Data Science learning from someone who has been learning it for about 6 months ;)
I agree!
@@KenJee_ds What do you think about places like LeetCode? Which ones would you recommend? I am looking for python coding exercises as I want to increase my python proficiency to intermediate. :)
I hope I learned this early on my early years but I'm still 20 so I have more time to start. thanks for your video, it helped me internalize what you said since Im always burn out before and tried so many stuffs on self dev but the truth is "I have to be honest with myself" and have my own system not copying others. I will become data scientist for next 2-3 years :)
Great timeline! So important to take a look in the mirror sometimes and take things at your own pace!
That video actually came in a good time cause I'm really burning out im my first solo project. Even though I'm not in such a bad position, made a lot of courses and I wrote my first "import pandas as pd" five weeks ago I always feel like I'm having a slower progress than I should. I'll take it easy with myself and start with smaller jobs
Happy to hear the video helped!
Point #1 I agree 100%. I'm into sports so learnt quickly that even a bad tennis coach is better than no coach for novices! So I just googled data camp and went for it
Good call!
Great video as always, Ken! & definitely the right choice :D @1:28
haha I think so too!
Excellent suggestions! All of these are very helpful. I do think I am going to go through a project with you so I can start learning. I will let you know which one I start with and how it goes.
Yes! Would love to hear how the project goes!
@@KenJee_ds I certainly will! Thank you so much.
Thanks Bro to make me so much clear and i will take care of it.
Awesome!
Thumbs up!!
just now finished all(all!!) leetcode sql questions!!
Looking to finish more questions at another websites
Crushing it! Keep up the good work!
It's a cruel experiment, but there was an experiment on rats in water. It showed that a rat will drown in 15 or so minutes if left unattended, but if you save the rat right as it submerges,, and put it back in the water, it'll keep swimming for another 60+ hours without drowning. It applies to your concept of us always having 100% more to give. In this case it was more than 100% more.
true!
Thank you Ken.
Thanks for watching!
This video is the best way for a well sleeping for me.
Hey Ken, have you ever thought on making a video on what is important when you are in a data science role already. Would be quite interesting for me to hear your opinion. I sometimes do not know if I should dive VERY deep into some specific topics or just use my freetime to learn something somewhat related. For example I love working with REST Apis and even some Vue Frontend stuff which will not make me a better data scientist, but are also fun... Since I am also a content creator I something think the same way about creating videos. What´s your opinion?
Hey! That is a really good idea, I think it may be a super difficult one to put together though. I've found that what you should learn in the role really depends on the company you're working at as well as your own personal goal, so it isn't quite as easy to generalize as what people newer to the field should focus on. It's a bit round about, but I hope that some of my podcast episodes can help point to what some other data scientists are doing in this area.
Thanks, Ken. Great video.
Thanks for watching Robert!
Thank you for the content!
I believe I need to apply the theory to projects more. I haven't yet worked on, let's say, real projects, except for some Kaggle practice and reading.
A few days ago, I set out to create a substantial project involving extracting valuable insights from the remote job market, and I'm finding it quite challenging to locate suitable data sources. I mean, I'm investing a lot of time exploring techniques to attempt scraping LinkedIn, for example, but it hasn't been yielding any results so far.
I would appreciate any suggestions :)
There may be some data on kaggle available! I also made a glassdoor scraper in one of my older videos. It probably needs some cleaning up to work, but the source code is there
... ''sexy computer vision and deep learning projects '' )))) thanks dude
feeling good after watching this, thank you sir.
Great stuff! Thanks for watching!
Thanks for sharing Ken 👍
Thanks for watching!!
Nice! I feel better now knowing that I ONLY committed 6 out of these 7 mistakes when I first started! #winning
And look where you are now! #successstory
Thank you!
Thanks for watching!
Probably most useful and honest video👍
Glad it was helpful!
that 10 minute rule is smart gotta try that
Definitely worth a shot!
Hi Ken Thanx. me a hard core biologist- of late translational research- fascinated by data science n m encouraged by ur video. look forwrd to train myself. send ya happy thots frm Himalayan foothills India. Stay blessed. 🌸🙏🌸
Thanks Asha! Good luck on your learning journey. Excited to hear how it goes!
Rip papaya you have , eat it first you must . hahaha. Nice video. I too believe self evaluation is very important before entering in any field. And the field like DS which requires lots and lots of time and learning efforts its crucial to know before you start. Thanks for making people aware of that Ken.
Lol! I couldn't agree more. Thanks for watching!!
great direction
Thanks for watching Mazen!
Mistake I personally made, I didn't learn enough math. And I thought that I could brute force my way through a data science course with some hodgepodge math. I think really getting that foundation is pretty important and it can be done in a singular course, it doesn't have to be a year of university math classes
Hey! Which course was a good generalization? I know I lack the math background but sometimes the material starts off simple but goes from zero to hero in a few classes...
Even I denied math and stats at first then understood how learning this will helps us know the what happens behind the scene helping us choose which model to choose for specific purpose 🙌
@@azwin6506 if you lookup "math for data science" course there are a couple on coursera, one by duke and another by a university who's name escapes me. I would go with the one who's name I can't remember, it seems to be more substantial
Definitely a major bottleneck!
I will focus on maths thanks for sharing
Hi Ken...thanks for your content. I do a lot of econometrics..learning modelling data (Logit, Ardl, VARs). I think this is a good thing but which skills do i urgently need to learn to call myself a data scientist in your view? Of course divibg deeper into programming etc?
I think programming would be very practical!
I think #3 is person dependent. If you become to reliant on a coach/mentor then your problem is your mentor not coaching you to become a more self-sustainable data scientist. I'd have killed for a bit more guidance in my formative career years rather than go and try lots of solutions.
Maybe it's person dependent? Either way, great video. Liked and subscribed!
Totally fair! Admittedly, I've never personally had a good mentor, so I may be slightly biased
Why does Ken not apply for a Data Scientist job? The job would play much better, wouldn't it ?
Haha I actually do work as a data scientist (Head of Data Science), I just do youtube in my free time for fun / as a side hustle!
Great!
Thanks for watching!
Hey ken please make a video on interview for data science....like what kind of python coding question will be asked, etc...
I have a few of those already, but honestly, Tina Huang and Data Science Jay have more content on their channels regarding that!
@@KenJee_ds thanks ken for reply!😊
Unrelated question here!, So Im a junior physics student right now looking to become a data scientist in the future, what is the fastest route to become a data scientist ? Land a data scientist internship, get a data engineering/analyst job, software engineering job, etc? Also I live in San Diego and don't plan on moving is this going to cripple my chances to find work?
I think physics is a really strong background for data science. I think you nailed it on the fastest route. There are plenty of jobs in San Diego, so you should be fine on that front.
Very informative video. As a IT student myself, I don't know whether I should choose to study data science or network enginner in my next degree course. As I have no experience, idea, knowledge of data science.
Do you think it's a great idea to move to data science career or to other IT career?
Would appreciate your thoughts on this. Thanks!
I think either is a really strong option! I would do some projects and experiment a bit more and you should be able to figure out what you have the most interest in
subscribed! for awesome videoss
Thanks Kong! Glad the videos are helpful!
So, I am not making most of the beginner mistakes.
But I have one question Ken. I don't know enough. Should i see the projects on CZcams and try to implement them by copying and understanding them? And you told about plagiarism in DS. Will it be like I plagiarized those projects ??
Hi Aamna - It is fine to copy these projects if you are using them for practice and not sharing them as if they were your own. I think copying is perfectly fine for learning, but you should start doing your own original analysis over some time! I hope this helps!
@@KenJee_ds okay !! noted
Random Q: isnt there a UI (end user stuff) for getting arranging statistics/data instead of doing the hard coding??? (Im guessing all data is in DB SQL like formats so you cant use end user style tools?).
eh every job is the same basically gets mundane or same same- unless your doing an extreme sport for job...
Should I focus more on oop? To be a data scientist
no, that isn't going to be as relevant as some of the other topics
I will make sure I don't make these mistakes🤞
💪
@@KenJee_ds :)
all is clear thanks but i have one doubt i completed python basics and thinking of data structures (dsa) is dsa needed or required skill for data science or not ? plz reply 🐱
Some understanding is useful, but not a requirement
Apologies for the unrelated comment but what is the brand-model of the chair that you're using at 4:40? Thank you and more power to you.
It is from uplift desk!
Mistake #1: Plugging your USBs into Papayas
How to Solve This: Plug your USBs into Bananas
It's basic stuff, but some people still mess this up.
There is no way you can fit a quality GPU into a banana. EVERYONE, please don't listen to this advice!
@@KenJee_ds Can we like stop this hate between y'all two. Both are amazing following both and both have good knowledge to share in their own way. Like you said there is no one right way. Please 🙏🏻❤️
I always reach out in comments section to see Alex's comments😂
@@523harshpatel3 not funny
Sucks how people enjoy this
@@krishnabarfiwala5766 you are stupid if you think there is hatred between these two. This is friendly battle. Better check Ken's other videos and Alex's comments on each of them.
What's the song playing in background?
I honestly don't remember haha. I am certain it was from youtube's free music studio though
Great content as usual! Ken, we know your secret now: Your source of Data Science are Papayas 1:53 :D :D
I was pretty impressed I could a RTX 3090 in there! 😂
@@KenJee_ds The powerful next-gen bio-degradable Papaya Processing Unit (PPU) is the one-stop solution 😂😂😂😂
love ur content
Thanks Mahmoud!
You focus on about DS only u should say something about the person's environment about DS is also affect more than DS itself
True!
Wait what ? You reply to all of your YT comments ? ? Damnnnn.
I do my best to! 😀
Yeah so nice of him
What's with the papaya??🤔
It is my favorite fruit haha
papa jee!! i hope he had a lovely nap :)
Haha he always does!
Hi! I like watching your videos. Love and support from India ♥️ There's some thing in particular that I'd like to ask.
1) It's been some time since I completed MySQL course. However i have gone through the videos a few times but still I am not able to gain the confidence of saying that I know SQL. What do you suggest here?
2) Practical knowledge is very important and I am unable to get that. I have a dataset. I make up problem statements in my mind and try solving it. But that is clearly not helping at all. So can you suggest me some places where I can apply the knowledge of Sql that I gained through the course?
Course I took: 365 Data Science.
Hey - For SQL, you can always go on websites like hackerrank to practice the questions there. You could also make your own database and explore it with queries like I did in this video: czcams.com/video/WKXtTo7lqmA/video.html&ab_channel=KenJee
For the projects, I would start with kaggle. Instead of starting from scratch, review a bunch of different workbooks on the same dataset. See how others have approached this problem. Then try to expand on someone else's work with new questions you have. Once you get the hang of it you will create your own system for approaching these problems. czcams.com/video/-pdXWmj9xxU/video.html&ab_channel=KenJee
hey ken jee, thanks for the info. i am 30 and a total bum. so, i'd like to quit being a bum. this looks like a great career. what do you advise for someone who would like to get started even in something related and not necessarily in it, where from he/she could continue to learn while earning to then move up and /or into this field?
I think trying to go down the data analyst route would make a lot of sense. Getting familiar with tools like SQL, microsoft excel, and maybe some programming languages. Alex the analyst has a great channel on this too!
I'm starting with Advanced Excel and thinking of learning Data Analysis or Business Analysis before deciding on making the big leap into Data Science. For me personally, I think understanding the business side of the data and how to interpret/present your data are a good place to start. If you're a bum, the easiest point of entry or the "least path of resistance" are usually the best for those who are unmotivated. It's important to know thyself. I know that if I get overwhelmed or bored, I'll quit. So the learning process has to be simple, well-paced, and/or fun. Maybe this is the perspective you should take in your approach as well.
Does 365 Data Science have project?
Not that I know of right now. I generally recommend combining the coursework with kaggle projects to maximize learning
@@KenJee_ds Awesome. Thank you so much..