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

Today we are discussing

data

science

roadmap

with week by week

study

plan

checklist and

free

learning

resources

to increase my video views. I'm not going to say anything unrealistic, this

roadmap

requires 4 hours of

study

every day for 6 months, so obviously it requires a lot of hard work, so if you are looking for a shortcut, leave this video right now, don't waste your time . I myself take interviews with

data

scientist

s in my company's atck. Technologies and basic concepts of code. I have worked for Bloomberg USA for over 12 years. years, which is the largest financial data analytics company in the world and I also have data

scientist

friends who work at big tech companies who have helped me with this roadmap, so everything we are discussing today is a Real advice based on industry experience, which we are going to talk about.

Tool Skills and Basic Skills Basic skills are equally important for a data scientist role, so make sure you pay attention to that and after improving your skills, you should learn how you can show your work to the world so that you can receive a interview call and crack the interview, so here is a PDF roadmap that you can download from the video description below. You need to study for six months where every day you spend three hours on tool skills and 1 hour on basic skills now before starting the study

plan

in week zero. You need to do proper research and protect yourself from scams because many scams occur in the ad tech industry in the name of job guarantee programs.

Learn data scientist skills in a month whenever you see some shortcut where they say you can learn this. in a month or you can get a guaranteed job, most likely it is a scam. Okay, they are making false promises to increase their income. Many YouTubers are doing the same thing, so make sure you do thorough research; you must also verify the credentials. from uh, the instructor, do they have any real experience in the industry? If you are watching any video on YouTube channel, look at their thumbnails if they have clickbait type thumbnails where they promise great things, most likely they are a scam.

You shouldn't learn from them, otherwise, you will. You don't get a real education of the industry so you will spend week zero doing this research and we have provided these three different links where we talk about various scams that are on the market and these videos and Linkedin posts will inform you or make you aware of it. help you identify those scams now in week one and two you will learn Python as a data scientist. You must know Python. It is a great programming language. You don't need to master all the concepts. You need to learn all these fundamental concepts.

To know that you can use a

free

uh python playlist on my YouTube channel, where you should watch the first 16 videos. These videos also have exercises, so make sure you work on those exercises now in terms of the basic skills you need to build your LinkedIn Profile. Many times people spend 6 months a year learning tool skills and don't focus on building the resume. of the LinkedIn profile and so on. What we're doing here is learning tool skills and basic skills in parallel so you get some variety plus you're making progress on both fronts. Now we've provided you with a good LinkedIn checklist that you can use to make your profile strong, so what you'll do is read each of these points and say "check." check and when you have checked all these checkpoints, your LinkedIn profile will now be strong as a motivation.

I have provided you with a video link of a person who moved from a physics background to data

science

. He shares many useful tips and check out this video. He will motivate you, inspire you so that you can continue your study with great enthusiasm now for the tasks. You need to work on these three tasks. You must finish all the exercises that I have published on my GitHub page. You also need to create a professional looking LinkedIn Profile now to keep track of tasks, we have provided a Notion template. Notion is a free tool that you can use to track your study progress, so here you need to click on the 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 you, you need to copy that template into your own workspace.

Okay, let's say I have copied this template here and now it will show me a

weekly

progress so that when you finish your

weekly

tasks, let's say if I say done, done, done, it will update the progress here. See 33.3%, that's my progress for week 1 and 2. Now here, this is actually for the data analyst roadmap, but don't worry when you're looking at this. video, we would have prepared this template so that when you click on it you get the real notion of data scientist, uh, progress tracker, okay, so study with your friends and in a group you can meet, let's say once a week , etc., and they can verify each one.

The progress of other data scientists uses pandas matplot lib and selects these three Python libraries to perform exploratory data analysis: data cleaning and transformation, and the base of these libraries, especially pandas, is numerous, so you should focus on week three in the 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 done a free full chapter so you can learn these skills without spending money once you have them. To understand these three library lies, you need to move on to your core skills, which are following some notable data science influencers on LinkedIn, for example, dalana L writes about data science, ongoing tips.

Trends, interviews many people, data scientists working in the industry and by reading his post you will not only get knowledge about what is happening in the AI ​​industry but also get a lot of help in preparing your studies and Also help for interviews. Agarwal is head of AI at Google and also writes a lot. of useful posts so to follow this person you can just click on more and click on follow button and then go to his post and start reading his previous post also when you follow this person today when you spend your time on LinkedIn.

Which I would recommend you spend at least half an hour, you will start seeing his post in your feed after you have read that post, start interacting and start adding comments on his post. Now when I talk about comments, don't make generic comments true at all. true, it doesn't add any value every time you add a valuable opinion. What happens is that there will be a lot of people who will like your comment and some of these people could be data science managers or data scientists who work in the industry and when you continue. By doing this repeatedly, those people will have a positive impression about you, you will build a good Rao with them.

Okay, obviously, you can connect with them on LinkedIn and as long as they have a requirement for a data science position on that team, they can recommend you, so don't do it. Think that getting these interactions in your comments is totally useless. In fact, there is a psychology at play where you are building a connection, you are spreading your influence or you are spreading your impression in this online world. You must remember one thing: people's online presence. a new form of resume is your live resume you are telling your life story on day to day basis you also need to focus on business fundamentals because as a data scientist you need to have domain understanding and to develop 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 the competition during Covid times and when you watch this video you get knowledge about data analytics as well as domain or business. understanding that you are strengthening that now as you study you will have questions, let's say you are running some program in Python and you get an error, you can use Discord, okay, so the Discord server is like a group chat where you see this.

Someone posted this error and there are people who will help you. There are 40,000 active community members, so they will help you here. When you post a question, there is certain etiquette you need to follow which I mentioned in this particular link in the post. I have provided a link to this post in the PDF, so read it carefully and learn the art of asking questions. He also needs the help of his two best friends, Mr. C and Mr. G. Mr. C is chatting with the GPD people and Mr. C is Google. GPT chat can often answer your questions, let's say you are facing a bug or need help with a block of code.

GPT chat works like a charm so you need to use that tool for your productivity and here is the task on which you need to write meaningful comments on at least 10 posts on LinkedIn during the third week and also write down your key learning from three case studies different about 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 a career in data science, make a group and meet at least once a week on Zoom or in person and show each other these tasks that they are working on, show them the notion of progress in the study also in weeks 4 to 7, they need to strengthen their fundamental skill and that fundamental skill is nothing more than mathematics and statistics for data science.

Many times I see people focusing on fancy frameworks like Tensor Flow Lang Chain etc. The frameworks keep changing, what doesn't change is the mathematics and statistics behind data science, so this is a very important module for Your statistics is a vast field, you don't need to study all the concepts. I have highlighted the fundamental or most used concepts here. I consulted with my data scientist friends who work in the industry and based even on my own knowledge and experience, we have created this curated list of topics that you should focus on to learn these topics. You can use this excellent course on Khan Academy.

This course has many exercises, easy to understand explanations, etc. The course is great, but you need to learn the topics that I. I have mentioned in my PDF now while learning this course, if you have any doubts and if you are not clear, you can take help from State Quest YouTube channel. This is a very popular statistics YouTube channel. It has very simple intuitive explanations so you can use it. You can also use the math and statistics playlist on the Codee Basics YouTube channel now in the Khan Academy course and this particular YouTube channel does not have Python coding practice, and these two

resources

talk about generic statistics or mathematics generic if you want to study statistics. with the context of data science, how statistics and mathematics are used in data science and if you want to practice those concepts in Python, I have this math and statistics for data science course on code basics my Web site.

It's very affordable and also includes industrial projects, so check it out. Many of the videos are free to motivate you. You can watch this video where I interviewed Pruma, who was a petroleum engineer turned data scientist at an oil and gas company called Hel Burton. he talks about how you can use your domain knowledge to land a data scientist job. While studying you need some motivation and excitement and by watching these videos you get that motivation and learn some important tips from everyone. these people who have transitioned as data scientists and here are the two tasks for this particular section now in week 8 you will learn exploratory data analysis so far you have learned pandas numai and also mathematics and statistics now you can combine these two categories of Skills to practice exploratory data analysis whenever the data scientist is dealing with any data set when they are building let's say a machine learning model first, they will do exploratory data analysis, clean the data, transform the data etc. , to practice exploratory data analysis, you can go. to kel.com, which is a free website, you can click on data sets and write exploratory data analysis here, okay, and you will find all the data sets where people have created a data analysis notebook exploratory, Jupiter notebooks, okay, so let's say I'm checking this out.Netflix's particular 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;

This is the CSV file, so if you click on it, you can download it. that CSV file, uh, has let's say 8000 rows, okay, I think it's about movie reviews, and then if you go to the code section, you'll find the notebooks that other people have written on top of this data set, like this so let's say if you look at this a particular notebook is doing some drill down data analysis on that particular code, so see this person is reading the CSV file, then he is using the info function header, then he will handle the values missing and then, I think there should also be some visualization. drop a and so on, so what you are doing is following this notebook and also practicing and understanding and once you have practiced at least, let's say three notebooks in terms of homework, you need to practice in two additional notebooks where you will be able to get two, let's say, new kle data sets and will do some data analysis explored on your own in weeks 9 and 10.

We will learn SQL as a data scientist. Many times we will extract data from relational databases to a Jupyter notebook and also time you will write SQL queries and to write those queries you need to have knowledge about basic queries, okay, then you join some advanced queries etc., you don't need to learn advanced concepts like database creation index trigger etc. because that is more for data and software engineers. Data scientists rarely use them now in terms of free learning resources. We have this course from KH Academy that you can use. We also have W3 schs tutorials where you can practice your SQL queries.

I also like this website. called Bold SQL where they have a very easy and intuitive interface here you can practice live queries. See here if I say year 2001, you'll see it's changing directly, so it's a very nice intuitive interface. Once you have some understanding of SQL skills, you will need to move on to the soft skills you need. To improve your presentation skills, now you will tell why I should care about presentation skills. As a data scientist, you will often present your data insights, etc. to business stakeholders and create a PowerPoint presentation at that moment and people think about PowerPoint.

Presentation is easy, actually it is very difficult, okay, creating an effective presentation is an art and very few people know it and if you want to learn that art, you can follow this statistical talk, it is an amazing talk where this person shows how you can build very well. Effective, intuitive and powerful presentations, so I have provided a link to all that here in terms of assignment. You have two tasks here, number one, you need to participate in the SQL resume project. Challenge on codebasics doio, so if you go here, codebasics doio we run it. a free resume project challenge and this one is in SQL, so you are given a set of data, a problem statement etc., and by using it, you need to generate some ideas, once you have generated them by writing SQL queries , you can create a post on LinkedIn and you can share your ideas as if you were presenting them to a company's stakeholders, so here's a post from Aran Sharma when we ran that challenge.

Aran Sharma won that particular challenge that he can still practice and here Arian has mentioned what type of SQL. techniques he used to generate ideas, then he wrote a nice post on LinkedIn. Look, while you are writing this post, you are practicing your written English skills, then you have a presentation here, so here you are practicing your presentation skills. He also made a video, so now he's practicing. verbal communication from him and due to all this he got a very good participation and later we also posted about the people who won the challenges of this project on our LinkedIn profile.

Now what happens is that I posted about all these people and I have some 150,000 followers, of which some people will be data science managers, so when I post their name on my LinkedIn feed, I will get the attention of those people and it's You may receive a call for an interview. Now comes the most important module of machine learning on which you will spend 5 weeks. This model has been divided into two categories. Preprocessing and model construction. Data scientists spend 70% of their time doing preprocessing where they clean the data. They manage values. They deal with outliers. They normalize the data.

They carry out various types of data. Transformations that create new columns, label encoding, engineering function, spit train, etc., and then comes model building, where you will learn about supervised versus unsupervised learning, then you will learn about regression classification, etc., and we have described all these topics here as such field of machine learning. It's very, very big, but we've summarized the important topics, the topics that data scientists use 80% of the time. Now the good news is that to learn these topics you have this free YouTube playlist with more than 2 million views if you read the comments you will get an idea about the quality of this playlist, the videos here include simple explanations, coding exercises, etc.

We have a separate playlist for feature engineering that you can follow and after you have learned these machine learning skills in this 5x duration. You need to familiarize yourself with project management techniques and in agile methodology there are two techniques that are popularly used and those techniques are scrum and kban for scrum. This website is great, it has free videos where you can get an idea of ​​what exactly scrum is. what it is and how people use it to run data science projects in terms of motivation. I interviewed tul singh, who was a mechanical engineer. He practiced using Kagel and used his Kagle credentials.

He got a job as an ml engineer and will give you a lot of information. Kagle tips and general pro tips, so stay tuned and here you will see the four exercises that he needs to work on for homework. The next 3 weeks he will dedicate to practicing the concepts that he has learned so far and, to practice, he must work on at least two projects, one is regression and another is classification for this. I have a playlist on YouTube for both projects where we have covered all the stages of a data science project like debugging data cleansing functions engineering etc. so I have these two projects here , while practicing these projects, you need to create an ATS resume.

ATS means application tracking system when you apply for a data scientist job, companies will have this ATS system that will automatically filter the resume you need to make sure your resume is ATS compliant, for this we have a YouTube video where we have talked about various guidelines, such as how to use the star method to mention your projects and how to mention different project experience skills, etc., so you can watch this video. You've been given a resume checklist where you can go, point by point, and make improvements to your resume, so when you've checked all of these boxes, your resume will be in pretty good shape.

People nowadays resumes are losing their importance and live resume. which is also known as project portfolio website, is taking its place here. I am showing you a project portfolio of Raj gopal where he mentioned his background, his skills and the projects he has worked on now, when he clicks on this project let's say. I'm looking at his profile as an interviewer. I can understand what the project was that he gave me. Check out this video clip so I can see it in action and I also have access to their GitHub so I can go. and check your code so that creating a project portfolio website is a necessity in modern times, for which you can use free tools like github.io or you can use tools like Portfolio Builder that we have in the basic code.

As of this recording, it is available only for students of the Data Analyst Bootcamp, but in the future we will also make a free version and it will also be included in the Data Science Bootcamp. Then comes the link tree in the link tree that you can link. all the different profiles for example naven has this link and if anyone wants to know about him he will just send this particular link and you will get access to his powerb portfolio from linkedin, instagram etc and here is your task you need to use a Fast API. instead of FL in that first project and we have given some customization ideas for regression and classification projects, weeks number 19 to 21 will be dedicated to learning GPT deep learning chat and most of the modern AI applications are built using deep learning, therefore, as a data scientist, it is important that you have your basics clear when it comes to deep learning.

In the basics you need to know what is neural network multilayer perceptron and some spal neural network architecture such as CNM and RNN sequence model lstm etc. now if you have knowledge about RNN and Lstm you could argue why not Transformer, obviously applications like Jad GPT are based on Transformer architecture but knowing RNN and lstm will clear their fundamentals because eventually Transformer architecture was derived based on RNN and lstm and in interviews they will ask questions on these topics. If you have a complete deep learning playlist, you don't need to learn all the topics, like I have some videos on distributed computing GPU optimization, so you only need to learn first, like 15 or 20 videos covering the concepts we have mentioned in this PDF file now the playlist 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 was created by my friend aritra who also helped me build this particular roadmap. so you can also go through this after learning deep learning you can build a project from start to finish so this is a deep learning project from start to finish to identify the disease in potato plant where we have created a mobile application that takes a photo of a plant and will use a deep learning convolutional neural network to predict whether this particular plant has a disease or not and you will see that we have covered the model implementation, mobile application, data collection model , building everything so I can work on this project and here are the tasks for these 3 weeks now in the last 3 weeks so we talked about the six Monon roadmap so 6 months equals 24 weeks so in the last 3 months of this 6 Monon roadmap you should learn NLP or computer vision I mean.

If you are very enthusiastic, you can learn both, but it is as if you have

become

a doctor until now before week 22. You have

become

a doctor as a general practitioner. Now you want to become a specialized doctor, be it a pulmonary doctor or a cardiac doctor. I 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 have to start with regular expressions which are like the fundamental concept and then you can cover text presentation using counting vectorizer. and all these topics that I have mentioned here and we have an NLP playlist on YouTube so you can follow this particular playlist, it covers uh, theoretical coding exercises, everything, it's free for people, all it requires is willpower to learn computer vision, these are the topics. you need to learn.

I don't have any recommendations for good courses so far, so you can figure things out on your own and here's the task. Look, we have finished the six months, friends, uh, and there was a lot to learn, especially when there are a lot of things to learn, you need to learn how to learn, basically, you know how to learn effectively in a shorter period of time and, to So, you should follow this concept of spending less time consuming and more time digesting, implementing and sharing, so let's go. Let's say you are watching a 15 minute video tutorial, now you spend another 30 minutes digesting it, you take notes and write, you try to organize your thoughts and you try to understand it, then you implement it, which means you write the code, you run it and then you implement it. you share, so if you have formed a group with your friends, have a meeting and try to share your learning today.

What's happening is people are spending more time consuming, they watch a YouTube video and then another video comes up, they watch that video, the third video, the fourth video and no. You don't practice and people get lazy, they get distracted, somake sure that doesn't happen to you and group learning is obviously another concept that we've talked about, so we have a partner and group finder in our Discord channel where people say hello. I'm learning this if you're interested let's make a group so you can use this channel everything is free friends and when you form a group it becomes more like if you go to the gym alone you won't have much motivation but If you go to the gym or, let's say, if you're running a marathon with a couple of friends, then you will feel motivated.

The same concept also applies when learning data science. Starting in week 25, you will build more projects that you will focus on. building credibility online through Linkedin by participating in kle contests, helping people on Discord contribute to open source, etc., and at the end we have some FAQs that some people might argue, okay, why not Did you talk about the creator of Amazon Sage? Etc. I have given my thoughts on these. They are based on real industry experience, as I said. I have spoken to many data scientist friends who are in the industry and they told me during the junior data science interview that they don't talk about the cloud offering so there is no need to learn it.

I mean, if you have time and you learn it, it won't hurt, but it's not necessary, it's okay, that's it, all the resources are available in the video description below, friends, start learning. I wish you all the best and if you have it. any questions there is a comment box below