How to Learn Data Science: A Roadmap for Beginners

Data science is a pretty trending field nowadays, and the data scientist profession is the sexiest job of the modern era. But how do you learn data science? In this article, I’ll share a roadmap for all beginners who want to learn data science.

You don’t need to have a Ph.D. in data science. Nowadays, we all have access to the Internet, and plenty of resources are available there. But often beginners who want to learn data science might get overwhelmed by the resources available online.

So, I’ll list down precisely the topics that I think you should learn so that you can become a data scientist and monetize your skills. Let’s get started.

What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data [Source].

Data science helps you to work with a large amount of data and extract analytical insights and valuable information. These insights can be very beneficial for businesses.

Areas in Data Science

How To Learn Data Science?

Learning data science isn’t so hard. If you’re ready to put in some time and effort, you can definitely learn data science. Data science consists of a lot of sub-fields. I’ll try to mention some of the important areas in data science and how to learn them.

1. Learn Python

What should you learn first? Well, there are several starting points in data science, and people do it in different ways. Since I’m a programmer, I would suggest you start with the basics of a programming language.

There are two popular programming languages out there for data science, which are Python and R. Both of these languages are great. You can select any one of these based on your personal choice. Check out this article for a detailed comparison of Python and R.

I would suggest you learn Python. Python is a simple, popular, and powerful programming language. I don’t know much about R, and hence I can’t comment on that. But Python is a great choice. I would highly recommend it.

I’ve written a complete guide on the basics of Python. If you’re interested, you can check out this article. It doesn’t matter if you learn from my article or any other learning platform. Learn the fundamentals of Python and become good at it.

2. Learn Math

Math skills are pretty essential in data science. Especially, linear algebra, calculus, statistics, and probability. These skills are needed to understand what’s happening inside when you work on data science projects.

For example, when we analyze data using Python, the data is transformed into a matrix form. If you know the basics of matrices and their operations, it would be easy to do such tasks. Hence, learning math is critical.

However, you don’t need to be a math expert. You can learn the basics of linear algebra, calculus, statistics, and probability from online platforms. I would suggest you learn from Khan Academy, where you can find top-notch courses for free.

Learn the fundamentals and take notes. You’ll be using this knowledge in several projects.

3. Learn Python Libraries for Data Science

There are several Python libraries for making complicated data science tasks simple. If you’re good with the fundamentals of Python, let’s master some Python libraries.


The first Python library that you need to learn is Numpy. Numpy is the numerical library in Python. This library makes it easy to do numerical operations with Python.

Numpy helps you in working with linear algebra. Linear algebra is a branch of mathematics dealing with matrices. Matrices are also known as arrays or lists in programming terms.

If you want to learn the basics of Numpy, check out the article that I’ve written on Numpy Tutorial for Beginners.


If you’ve learned the basics of Numpy, now learn Pandas. Pandas is a Python library built on top of Numpy for faster data analysis, data cleaning, and data pre-processing. 

Pandas library helps to load datasets, clean the datasets, and make data frames easily. I’ve written a beginner’s guide to the Pandas library to help you. Check out this article and learn about Pandas.

Matplotlib and Seaborn

We need to represent the data that we have in a graphical format using several charts and plots. This process is called data visualization.

For data visualization, Python has two amazing libraries, which are matplotlib and Seaborn.

Matplotlib is a comprehensive Python library that helps to create static, animated, and interactive visualizations. It has several plots and graphs available as in-built methods to make everything easy. If you want to learn the basics of matplotlib, check out the article that I’ve written on Data Visualization using Matplotlib.

Seaborn works similarly to matplotlib but has several other capabilities. It provides a high-level interface for drawing attractive statistical graphics. Seaborn has a lot of preload datasets, which can help us to understand visualization easily.

To learn the basics of seaborn, you can check out the article that I’ve written on Introduction To Seaborn Library.

4. Learn Machine Learning Algorithms

Machine learning algorithms can be really helpful in analyzing datasets and extracting meaningful data. Machine learning is the scientific study of algorithms and statistical models that provides computer systems the ability to learn by themselves from experience without being explicitly programmed.

There are so many machine learning algorithms developed these days. But you don’t need to learn all of them. Some important algorithms you should learn are Linear Regression, Logistic Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Trees, Random Forests, Neural Networks, etc.

I’ve written a helpful guide that explains these algorithms in simple words with graphs. Check out this article to learn about these algorithms.

These algorithms can be easily executed using a library called scikit-learn. This library has several built-in methods that can help implement machine learning algorithms with ease. You can check out this introductory article to scikit-learn if you are interested in learning it.

Python has some other data science libraries, such as keras and tensorflow, for deep learning purposes. Deep learning is a subset of machine learning in which algorithms inspired by the human brain (which are known as artificial neural networks) learn from large amounts of data.

If you want to get into deep learning, learn the basics of keras and tensorflow. If you learn these libraries, you can do various projects like the one I did on image classification using deep learning.

The Next Steps

Data science is a vast topic. There are lots and lots of individual fields inside data science. I just gave you an overview of some of the important topics in data science that I know.

I don’t know everything about data science. So, I’ll put an entire data science course down below, which covers a lot of concepts in data science. You’ll learn a lot more about data science from the following video.

This was just an introductory article for data science. As I’ve said earlier, data science is a very big field, and there are a lot of things to learn. I hope this article would be a starting point for you to learn data science.

You can do your own research on the Internet to learn more about data science. If you want to become a good data scientist, you must become a good researcher.

So do some research and find the data that you want to learn. Just like data scientists do, filter the data, and remove the stuff that you don’t need. Focus on important data that will help you improve faster.

I hope this article was helpful to you. If so, let me know in the comments down below. Also, feel free to let me know your doubts or queries.

I would appreciate it if you would be willing to share this article. It will encourage me to create more helpful articles like this one.

Happy Learning!

Ashwin Joy

I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.

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