Roadmap to Becoming a Successful Data Scientist

After reading this, you will know how you have to learn Data Science and which topics you have to learn from where, to become a successful Data Scientist.

Data Science is a technology that is becoming very popular in today’s era of data.
Because of tools like ChatGPT, AI, and especially Agentic AI, the demand for Data Scientists is increasing with time.
And in such a situation, if you want to learn Data Science, then this is the best time to learn Data Science.

We will talk about how to start, what are the fundamental skills that we have to learn, what are the tools that we have to learn, and which AI tools we have to learn.
I am going to tell you about a modern spreadsheet tool that will blow your mind when it comes to analyzing your data using AI.
In this roadmap, I will explain everything step by step.

But let me tell you that all the things that I will teach in this roadmap are packed into one complete course, where I teach Data Science step by step by holding your hand.
If you are interested, then the link is in the description where you will get all the details.

Now this guide is for you because it is very important for you to know how you can learn Data Science by yourself.
IBM’s recent research has shown that over 90% of the data we have today was generated in the last two years, which means that all the data we had before accounts for only 10%.
And one thing is certain from this that the need and demand for Data Scientists will continue to grow.
And when the supply of good Data Scientists is low and demand is high, then we will see very high salaries.

Talking about India, the average salary of a Data Scientist is between 6 to 12 lakh rupees per year.
And if we talk about the US, the same salary ranges between 90,000 dollars to 1,20,000 dollars.
According to Economic Times, in the next five years, Generative AI can increase India’s GDP by around 45 billion dollars.
And if we talk about the world GDP, then this number can increase it by 6 to 10 trillion dollars.

So if you want to learn Data Science, this is the best time.

Step 1: Programming Language and Foundational Skills

• Now, how to start Step one is that you should pick one programming language.
• It is very important to have command over at least one programming language.
• I personally recommend Python, and 90% of successful Data Scientists will recommend Python to you.
• The reason is that the libraries and ready-made solutions in Data Science are mostly available in Python.
• That’s why, if you don’t have any other strong reason, you should start with Python.

• Python is a very simple language with very intuitive syntax.
• If you want to learn Python, I recommend my Python course available on this channel.
• But if you want to check out my latest course, then you can see my Udemy course, where you will get updated projects, and I also keep updating it from time to time.
• Plus, I have also planned some AI projects in it, so if you want to see those, you can definitely check out this affordable course.

Now, one common question comes can you learn Data Science without a degree?
Can you get a job in Data Science without a degree?
The answer is yes.

Can You Learn Data Science Without a Degree?

Now look, a CS degree always helps.
If you have a Computer Science degree, it will definitely help you.
I personally am a graduate from IIT, and I don’t want to lie to you my IIT tag still helps me a lot.
Even today, when I tell someone that I am an IIT Kharagpur pass-out, I get a different level of treatment.

But having said that, even if you don’t have a degree, or a relevant degree from a good college, that is not a problem because in today’s world, skills matter.
If you have taken your skills to such a level that people value your work and you are creating something useful, then degrees don’t matter, and you can definitely get a job in Data Science.

Now another question arises why don’t we use tools that are already made for data analysis?

Tools vs. Programming

Why do we need programming?
Why not just use tools like Power BI or Excel?
The simplest tool is Excel, and many people ask if Excel alone is enough.

In Python, for Data Science, there are libraries like NumPy and Pandas, and you can do data analysis with them.
But these are far superior to Excel they can do things that Excel cannot.
For example, if you want to build a web interface or your own app where users can upload their data, you can create features using NumPy and Pandas.

Now, this doesn’t mean you shouldn’t learn tools like Excel.
Today there are many great tools in the market.
Let me give you an example of one that I personally use and the name of that tool is Quadratic.

It is an AI spreadsheet tool where you can talk in natural language, and it will write code for you and help you with data analysis.
This is a modern AI tool and very powerful for quick data analysis.
I’ll include its link in the description so you can check it out.

Once you learn to use such tools, I recommend you also learn Python libraries like NumPy and Pandas, and for data visualization, libraries like Matplotlib and Seaborn.
With these, create good projects — projects that perform real-world data analysis.

In my Data Science course, I make students build a project called “Quota Soft Daily,” which teaches how real-world data analysis works.
Having such projects in your resume will definitely help you.

Step 2: Libraries, Mathematics, and Leveling Up

After that, you should learn a bit of Mathematics that is needed for Data Science.
I recommend learning Linear Algebra, and along with that, Probability and Statistics.
You must know basic concepts like normal distribution, optimization, Poisson distribution, and what normally distributed data means.

If you want to self-learn Probability and Statistics, I recommend Hans’ book, which is very famous and used in colleges.

After learning Python and building projects with NumPy and Pandas, your next step is to level up your projects.
For that, I recommend the book Python for Data Analysis — you can buy it on Amazon or read its free web version from the author’s website.
It’s a bit long, but an amazing book that teaches the complete Data Science lifecycle — defining the problem, collecting data, cleaning and preprocessing it, evaluating, deploying, and maintaining it.

Once you’ve done all this, an important thing comes — graphs.
Your graph drawing skills should be very strong.
For that, I recommend a book called Play with Graphs by Hemant Agarwal — a very useful book for learning Data Science concepts visually.

Data Structures and Algorithms (DSA)

Another question arises should you learn DSA (Data Structures and Algorithms) for Data Science?
My recommendation is: learn the basics.
At least know about time complexity and space complexity, so you can write efficient programs.
Because time is very important in Data Science models, especially for time-sensitive problems.

Next, let’s talk about Databases.
You should have good knowledge of relational databases and SQL how queries and joins work, etc.
Since we use Python for Data Science, you must know how to connect Python programs with databases like MySQL, PostgreSQL, or MongoDB.

MongoDB is widely used because it’s intuitive, easy to use, and you can learn its basics within a week.
So, along with SQL, also learn NoSQL databases like MongoDB.

Step 3: Machine Learning and Advanced Topics

Now, should you learn Excel?
Yes because some things are quicker and easier in Excel than by writing a program.
And that’s where tools like Excel and Quadratic come into play.
Quadratic is like Excel, but with the power of AI it combines the capabilities of Excel and Python.

Once you’ve done all this, I would recommend you transition toward Machine Learning.
When you enter Machine Learning, it’s a whole different world — you’ll learn how neural networks work and get into deep learning.
For that, I recommend the book Hands-On Machine Learning with Scikit-Learn and TensorFlow — a brilliant book that builds your Machine Learning foundation from zero to advanced.

The only challenge is you’ll need time to read it fully.
But it’s worth it.
And if you find some parts hard to understand, that’s exactly why I created my Data Science course — to simplify these topics.

If you follow both Python for Data Analysis and Hands-On Machine Learning, you’ll learn a lot about Data Science.

After that, practice is the key.
You should also learn how to push your code to Git for that, I have a 10-minute guide that will help.
You should also know how to deploy your apps and how to build web interfaces using frameworks like Flask or FastAPI.
These make it easy to convert your Python programs into web-based applications for users.

So, start with Python first master it well.
Then, I’ll show you an amazing GitHub repository called Pandas Cookbook, which makes learning Pandas very easy.
Once you go through that, you’ll also understand about LLMs (Large Language Models).
Before collecting data for LLMs, you must know how they work, what context windows are, and what challenges data scientists and ML engineers face with them.

When you understand this, you’ll be able to collect better data for LLMs because data is the most important fuel for powering a large language model.

So, I hope now you have a clear idea of how you can learn Data Science from start to end.

Remember, while studying in college or any field, you might feel like you also want to enjoy life and that’s fine.
But if you enjoy and grind together, that satisfaction is unique.
Always remember when you are relaxing or wasting time, someone else is grinding and might go ahead of you.
That doesn’t mean you shouldn’t enjoy life you definitely should but maintain a balance between enjoyment and hard work.

Conclusion and Mindset

If today you only enjoy and tomorrow you don’t have a job, then that enjoyment will not matter. I hope you’ve gotten a good idea of how you can learn Data Science from start to finish. You need to maintain a balance in life. While it’s important to enjoy life, you should also grind with pleasure. Always remember, when you are enjoying yourself or wasting time, someone else is grinding and maybe passing you in the race.

So, balance fun and work enjoy life, but also grind hard so your future can be bright. And if you don’t have a degree don’t worry at all. You can definitely do it, even without a degree. Many people ask me, “Can I do it without a degree?”
The answer is: Yes, you definitely can.