Data Science Roadmap 2024 | Data Science Weekly Study Plan | Free Resources to Become Data Scientist

today we are discussing data science road map with week by- week study plan checklist and free learning resources to increase the views of my video I'm not going to say anything unrealistic this road map requires 4 hours study every day for 6 months so obviously it Demands a lot of hard work so if you are looking for a shortcut please leave this video right now do not waste your time I myself take data scientist interviews in my company's atck Technologies and code Basics I have worked for Bloomberg USA for more than 12 years which is world's biggest financial data analytics company and I also have data scientist friends working in big tech companies who have help me with this road map so whatever we are discussing today is a real advice based on industry experience we are going to talk about both tool skills and core skills core skills are equally important for a data scientist role so make sure you pay attention to that and after upskilling you need to learn how you can showcase your work to the world so that you can can get interview call and crack the interview so here is a road map PDF which you can download from video description below you need to study for six months where every day you spend three hours in tool skills and 1 hour in core skills now before you start the study plan in week zero you need to do proper research and protect yourself from scams because in the ad tech industry there are so many scams going on in the name of job guarantee programs learn data scientist skills in one month whenever you see any shortcut where they say you can learn this thing in one month or you can get a guaranteed job most likely it's a scam okay they're making false promises to increase their revenues many YouTubers are doing the same thing so please make sure you are doing a thorough research you need to also check the credentials of uh the instructor do they have real industry experience if you are watching any videos on YouTube channel look at their thumbnails if they have clickbait type of thumbnails where they promise big things most likely they are a scam you should not learn from them otherwise you will not get a real industry education so you will spend week zero in doing this research and we have provided these three different links where uh we talked about various scams which are going on the market and these videos and Linkedin post will make you aware or it will help you in identifying those scams now in week one and two you will learn python as a data scientist you must know python it's a big programming language you don't need to master all the concepts you need to learn all of these fundamental concepts and in order to learn that you can use a free uh python playlist on my YouTube channel where you need to watch first 16 videos these videos have exercises as well so make sure you work on those exercises now in terms of your core skills you need to build your LinkedIn profile many times people will spend 6 months one year in learning tool skills and they don't focus on building the LinkedIn profile resume and so on here what we are doing is we are learning tool skills and core skills in parallel so that you get some variety and also you are making a progress on both the fronts now we have provided a nice LinkedIn checklist which you can use to make your profile solid so what you'll do is you will read through each of these points and you will say check check and by the time you have checked all these checkpoints your LinkedIn profile will be solid now for motivation I have given a video link of a person who transitioned from physics background to data science he's sharing a lot of useful tips and watching this video will Mo motivate you it will inspire you so that you can continue your study with lot of enthusiasm now for assignments uh you need to work on these three assignments you need to finish all the exercises which I have posted on my GitHub page you also need to create a professional looking LinkedIn profile now to track the assignments we have given you a notion template notion is a free tool that you can use to track your study progress so here you need to click on duplicate button you need to create a free account on notion and then when you duplicate that page that page was basically a template that we have given to you you need to copy that template in your own workspace okay so let's say I have copied this template here now it will show me a weekly progress so as you finish your weekly assignments let's say if I'm saying done done done it will update the progress here see 33.3% that's my progress for Week 1 and two now here this is actually for data analyst road map but don't worry when you're watching this video we would have prepared this template so when you click on that you will get the actual data scientist notion uh progress tracker okay so please study with your friends and in a group you can meet let's say once in a week Etc and you can check each other's progress data scientist use pandas matplot lib and seone these three python libraries for doing exploratory data anal is data cleaning and data transformation and the base of this libraries especially pandas is numpy so you need to focus in week three on numai library for which we have this free playlist and then for pandas matplot lib and seon you can watch all these free videos in this course so this is my math and statistics course but I have made entire chapter free so you can learn these skills without spending any money once you have got some understanding of these three libr lies you need to move on to your core skills which is following some prominent data science influencers on LinkedIn for example dalana L writes about data science uh tips ongoing Trends she interviews a lot of people data scientists who are working in the industry and by reading her post you will not only get the knowledge on what's happening in the AI industry you will get lot of help in your preparation of your studies and also interview help Agarwal is a head of AI in Google and he also writes lot of useful posts so in order to follow this person you can just click on more and click on follow button and then go to their post and start reading their Old Post also when you are following this person day toay when you are spending your time on LinkedIn which I would recommend you spend at least half an hour you will start seeing their Post in your feed after you have read that post start engaging and start adding comments in their post now when I talk about comments don't make generic comments like true absolutely right it doesn't add any value whenever you are adding a valuable opinion what happens is there will be many people who will like your comment and some of these folks could be data science managers or data scientists working in the industry and when you keep on doing this repetitively those people will have positive impression about you you will build a good Rao with them okay obviously you can connect with them on LinkedIn and whenever they have a requirement for a data science position in that team they can refer you so don't think that getting these engagements on your comments is totally useless actually there is a psychology at play where you are building connection you're spreading your influence or you're spreading your impression in this online world you need to remember one thing folks online presence is a new form of resume it's your live resume you are telling your life story on a day-to-day basis you need to focus on business fundamentals as well because as a data scientist you need to have domain understanding and in order to build domain understanding one of the ways is to follow some case studies on YouTube for example this one where they talk about how amul beat competition during covid times and when you're watching this video you are getting knowledge on data analytics as well as the domain or the business understanding you're making that thing stronger now as you study you will have questions let's say you are running some program in Python and you get an error for that you can use Discord okay so Discord server is sort of like a group chat where see this person has posted this error and there are people who will help you there are 40,000 active community members so they will help you here and when you posting a question there is some etiquette that you need to follow which I have mentioned in this particular link in post I have given this post Link in the PDF so please read it carefully and learn the art of asking questions you also need to take help of your two best friends Mr C and Mr G Mr C is chat GPD folks okay and Mr C is Google so chat GPT can many times answer your questions let's say you are facing an error or you need help with a code block chat GPT Works amazing so you need to use that tool for your productivity and here is the assignment you need to write meaningful comments on at least 10 LinkedIn post during week three and also note down your key learning from three different case studies on think school and share it with your friends we have discussed the group study approach where if you have friends who are also preparing for data science career make a group and meet at least once a week on Zoom or in person and show one another these assignments that you're working on show them the notion uh study progress as well in week 4 to 7 you need to make your fundamental skill stronger and that fundamental skill is nothing but math and statistics for data science many times I see people focusing on fancy Frameworks such as tensor flow Lang chain and so on Frameworks keep on changing what do not change is mathematics and statistics behind data science so this is very important module for you statistics is a vast field you don't need to study all the concepts I have highlighted the fundamental or the most frequently used Concepts here I consulted with my data scientist friends working in the industry and based on even my own knowledge and experience we have created this curated list of topics that you need to focus to learn these topics you can use this excellent course on Khan academ this course has lot of exercises easy to understand explanations and so on the course is big but you need to learn the topics which I have mentioned in my PDF now while learning this course if you have any doubt and if you're not clear you can take help of State Quest YouTube channel this is a very popular statistics YouTube channel it has very easy intuitive explanations so you can use that you can also use math and statistics playlist on codee Basics YouTube channel now Khan Academy course and this particular YouTube channel doesn't have python coding practice okay and these two resources are talking about a generic statistics or a generic math if you want to study statistics with the context of data science how statistics and math is used in data science and if you want to practice those Concepts in Python then I have this math and statistics for data science course on my website code basics. it is very affordable and it includes industry project as well so check it out many of the videos are free for motivation you can watch this video where I interviewed pruma who was a petroleum engineer he became a data scientist in oil and gas company called hel Burton he talks about how you can use your domain knowledge to get a data scientist job see while you're studying you need some motiv and thrill and by watching these videos you're getting that motivation as well as you are learning some important tips from all these folks who have made transition as a data scientist and here is the two assignment for this particular section now in week 8 you will learn exploratory data analysis so far you have learned pandas numai and also math and statistics now you can combine these two category of skills to practice exploratory data analysis whenever data scientist is dealing with any data set whenever they are building let's say machine learning model first they will do exploratory data analysis they will do data cleaning data transformation and so on uh for practicing exploratory data analysis you can go to kel.com which is a free website you can click on data sets and type exploratory data analysis here okay and you will find all the data sets on which people have built exploratory data analysis notebook Jupiter notebooks okay so let's say I am uh checking this particular Netflix data set so here you will have a data set as well as the notebooks that people have written for example let's say if you go here you will find a data set see this is the CSV file so if you click on this you can download that CSV file uh this has let's say 8,000 rows okay this is about movie reviews I think and then if you go to code section you will find the notebooks which other people have written on top of this data set so let's say if you look at this particular notebook it is doing explor data analysis on that particular code so see this person is reading the CSV file then they are doing using info function head then they will be handling missing values and then uh I think there should be some visualiz ation as well see drop an and so on so what you're doing is you are following this notebook and you are also practicing and understanding and once you have practiced at least let's say three notebooks in terms of assignment you need to practice on additional two notebooks where you will get two let's say fresh data sets from kle and you will do explored data analysis on your own in week 9 and 10 we will learn SQL as a data scientist T many times we will be pulling data from relational databases into a jupyter notebook and at that time you'll be writing SQL queries and in order to write those queries you need to have knowledge on basic queries okay then joins then some Advanced queries and so on you don't need to learn advanced concepts such as database creation index trigger Etc because that is more for data engineers and software Engineers data scientists rarely use those now in terms of free learning resources we have this KH Academy course that you can use we also have W3 schs tutorials where you can practice your SQL queries uh I also like this website called SQL bold where they have a very intuitive easy interface here you can practice the queries live see here if I'm saying year 2001 see it is changing directly so it's a very nice intuitive interface once you have some understanding of SQL skills you need to to move to soft skills where you need to improve your presentation skills now you'll say why do I need to care about presentation skills well as a data scientist you will often be presenting your Data Insights Etc to business stakeholders and you will be building PowerPoint presentation at that time and people think PowerPoint presentation is easy actually it is very hard okay to build an effective presentation is an art and very few people know it and if you want to learn that art you can follow this stat talk it's an amazing talk where this person shows how you can build very effective intuitive and Powerful presentations all right so I have given link of all of that here in terms of assignment uh you have two assignments here number one you need to participate in SQL resume project Challenge on codebasics doio so if you go here codebasics doio we run a free resume project Challenge and this one is on SQL so you are given a data set a problem statement Etc and using that you need to generate some insights once you have generated those insights by writing SQL queries you can create a LinkedIn post and you can share your insights as if you're presenting it to the business stakeholders in a company so here is a post by Aran Sharma when we ran that challenge Aran Sharma won that particular challenge you can still practice and here Arian has mentioned what type of uh SQL techniques he used for generating insights then he wrote a nice LinkedIn post now see while writing this post you are practicing your written English skills then you have a presentation here so here you're practicing your presentation skills he also made a video so he's now practicing his verbal communication and due to all of this he got a very good engagement and L later on we also post about the people who have won this project challenges on our LinkedIn handle now what happens is see I posted about all these folks and I have some 150,000 followers out of which some of the people will be data science managers so when I'm posting your name in my LinkedIn feed it will get attention of those folks and you may get an interview call now comes the most important module machine learning where you will spend 5 weeks this model has been divided into two categories pre-processing and model building data scientists spend 70% of their time doing pre-processing where they clean the data they handle an values they treat outliers data normalization they do various kind of data Transformations creating new columns label encoding feature engineering train to spit Etc and then comes model building where you will learn learn about supervised versus unsupervised learning then you will learn about regression classification and so on and we have outlined all these topics here as such machine learning field is very very big but we have outlined the important topics the topics which data scientists use 80% of the time now the good news is that in order to learn this topics you have this free YouTube playlist with more than 2 million views if you read the comments you will get the idea on the quality of this playlist videos here include easy explanations coding exercises and so on we have a separate playlist for feature engineering which you can follow and then after you have learned this machine learning skills in this 5e time duration you need to make yourself familiar about project management techniques and in the agile methodology there are two techniques which are popularly used and those techniques are scrum and kban for scrum this website is excellent it has a free videos where you can get an idea on what exactly scrum is and how people use it to execute the data science projects in terms of motivation I interviewed tul Singh who was a mechanical engineer uh he practiced using Kagel and using his kagle credentials he got a job as a ml engineer he will give you a lot of kagle tips and general career advice so please watch it out and here you see the four exercises that you need to work on as an assignment next 3 weeks you will spend practicing the concepts that you have learned so far and in order to practice you need to work on at least two projects one is regression one is classification for this I have a playlist on YouTube for both the projects where we have covered all the stages of a data science project such as data clean feature engineering outli removal and so on so I have these two projects here while you are practicing these projects you need to build ATS resume ATS stands for application tracking system when you apply as a data scientist job the companies will have this ATS system which will automatically filter the resume you need to make sure your resume is ATS friendly for this we have a YouTube video where we have talked about various guidelines such as how to use star method to mention your projects and and how to mention different uh project experience skills and so on so you can watch this video we have also given a resume checklist where you can go uh Point by point and you can make improvements in your resume so when you have checked all these boxes your resume will be in a very good shape folks in today's time resumés are losing their importance and the live resume which is also known as project portfolio website is taking their place here I'm showing you a project portfolio of Raj gopal where he has mentioned about his background his skills and the projects that he has worked on now when you click on this project let's say I'm looking at his profile as an interviewer I can get an understanding on uh what was the project uh he has given see this video clip so that I can see that in action and I have access to his GitHub as well so I can go and check his code so building project portfolio website is a must modern times for which you can use free tools such as github.io or you can use tools such as uh the portfolio Builder that we have on code basic. it is available as of this recording to only data analyst boot camp students but in the future we are going to make a free version as well and it will be included in data science boot camp as well then comes link Tree on link tree you can link all the different profiles for example naven has this link and if someone wants to know about him he will just send this particular link and they will get access to his LinkedIn Instagram powerb portfolio and so on and here is your assignment you need to use fast API instead of FL in that first project and we have given some customization ideas for regression and classification projects week number 19 to 21 will go in learning deep learning chat GPT and majority of the modern AI applications are built using deep learning therefore as a data scientist it is important that you have fundamentals clear when it comes to deep learning in fundamentals you need to know what is neural network multi-layer perceptron and some spal neural network architecture such as CNM and sequence model RNN lstm Etc now if you have knowledge on RNN and lstm you might argue why not Transformer well obviously applications like Jad GPT are built on Transformer architecture but knowing RNN and lstm will clear your fundamentals because eventually Transformer architecture was derived based on RNN and lstm and in the interviews they will ask questions on these topics we have a complete deep learning playlist you don't need to learn all the topics such as I have some videos on distributed computing GPU optimization bird so on you just need to learn first like 15 or 20 videos which covers the concepts which we have mentioned in this PDF file now the playlist which I showed you is using tensorflow as a library there is another framework called py to and for py to we have this particular playlist which is built by my friend aritra who also helped me in uh building this particular road map so you can go through this as well after learning deep learning you can build end to end project so here this is a end to end deep learning project to identify y the disase in potato plant where we have built a mobile application which takes a picture of a plant and it will use deep learning convolutional neural network to predict if this particular plant has a disease or not and you will see we have covered model deployment mobile app uh data collection model building everything so you can work on this project and here are the assignments for these 3 weeks now in last 3 weeks so we talked about six Monon road map right so 6 month is equal to 24 weeks so in the last 3 months of this 6 Monon road map you need to learn either NLP or computer vision I mean if you have a lot of enthusiasm you can learn both but it's like you have become a doctor so far before week 22 you have become a doctor like a general doctor now you want to become a specialized doctor either lung doctor or heart doctor you don't want to become both similarly you can specialize in NLP or computer vision NLP field is booming especially after CH GPT came and in NLP you need to start with regular expression that is like the fundamental concept and then you can uh cover text presentation using count vectorizer and all these topics which I have mentioned here and we have a NLP playlist on YouTube so you can follow this particular playlist covers uh Theory coding exercises everything it's all free for folks all it requires is a willpower to learn in computer vision these are the topics you need to learn I don't have any recommendation on good courses so far so you can figure things out on your own and here is the assignment now see we have finished six month folks uh and there was lot to learn and especially when there is lot of things to learn you need to learn how to learn basically you know how to effectively learn in a shorter time duration and for that you need to follow this concept of spending less time in consuming and spending more time in digesting implementing and sharing so let's say you're watching 15 minute video tutorial now you spend another 30 minutes in digesting it you take note and pen and you try to organize your thoughts and try to understand it then you implement which means you write code execute and then you share so if you have formed group with your friends you have a meeting at and you try to share your learning nowadays what's happening is people spend more time in consuming they watch one YouTube video then another video comes up they watch that video third video fourth video and they don't practice and people are becoming lazy they are getting distracted so make sure that doesn't happen with you and group learning is obviously uh another concept we have talked about so we have partner and group finder in our Discord Channel where people are saying hey I'm learning this if you're interested let's make a group so you can use this channel everything is free folks and when you form a group it becomes more like if you're going to gym alone you won't have much motivation but if you're going to gym or let's say if you're running a marathon with couple of your friends then you will feel motivated same concept applies in learning data science too in week 25 onwards you will be building more projects you will uh focus on building online credibility through Linkedin by participating in kle competitions helping people on Discord open source contribution and so on and at the end we have some FAQs some people might argue okay why didn't you talk about Amazon Sage maker Etc I have given my thoughts these are based on real industry experience as I said I have talked to a lot of data scientist friends who are in the industry and they say during the junior data science interview they don't talk about Cloud offering so you don't need to learn it I mean if if you have time and if you learn it it's not going to hurt but it is not necessary all right that's it all the resources are available in the video description below folks uh start learning I wish you all the best and if you have any question there is a comment box [Music] below

Channel: codebasics

Views: 153,003

Keywords: data science roadmap, data science course, data science for beginners, how to learn data science, data science roadmap 2023, data science roadmap 2024, learn data science, data science tutorial, data science roadmap 2022, data science career path, learn data science step by step, data science career, what is data science, learn data science roadmap, data science learning path, complete data science roadmap, data scientist roadmap, how to become data scientist

Id: PFPt6PQNslE

Length: 29min 17sec (1757 seconds)

Published: Sat Dec 30 2023