Data Science Roadmap with Projects [Week-by-Week]

I feel your pain.

Starting to learn data science or machine learning often feels overwhelming, like you’re scattered in every direction, unable to see how the dots connect.

That’s exactly how I began.

STARTING IS HARD because you don’t know what to prioritize or what can wait for later.

I vividly remember my journey.

One day, I was learning Python fundamentals from YouTube. The next day, I was diving into math for data science. And the day after that, I was struggling with algorithms.

It was a recipe for disaster.

After months of this unstructured approach, I realized I hadn’t mastered anything solid.

So, I turned to courses.

They provided proper curriculums and a clear, structured plan.

Over the next six months, I followed a few courses diligently.

And guess what?

Just six months later, I landed my first data science job.

Looking back, I regret wasting so much time jumping aimlessly between YouTube videos.

Don’t get me wrong—YouTube is great, but it doesn’t offer the structure you need to progress effectively.

That’s why I created this roadmap for you, based on thousands of dollars spent on courses and months of trial and error.

This is the ultimate roadmap you’ll ever need.

It includes every topic you should study, week by week.

You can easily look up each topic on YouTube to learn without spending a fortune on courses.

Now, let’s dive into the details of this roadmap.

This week-by-week roadmap is designed to build your skills from the ground up, covering everything from Python fundamentals to advanced machine learning techniques.

With a focus on projects and real-world applications, you'll develop a deep understanding of key concepts and their practical implementation.

This Roadmap Covers:

• Python Mastery: starting with Python programming basics, progressing to advanced data types, functions, and object-oriented programming.
• Mathematics for ML: then we'll cover linear algebra, vectors, tensors, and matrices, essential for understanding machine learning models.
• Machine Learning Algorithms: now comes linear regression, logistic regression, SVMs, decision trees, ensemble methods, and more.
• Model Evaluation & Optimization: then its time for ROC curves, cross-validation, and techniques to improve model performance.
• MLOps Essentials: now get skilled in deploying and maintaining machine learning models in production.
• Capstone Projects: Time to apply your knowledge to industry-relevant projects for a practical learning experience.

This roadmap combines theory, coding, and interview preparation to equip you for a successful machine learning career.

Here's what your purchase will look like:

Google doc version of the same: