In this article, I’m going to give you a complete roadmap on how to become a Data Scientist with only free courses and resources.

So you’re probably thinking that you need a Masters or Phd from a top university to become a Data Scientist. In a way, it is true that having a master’s or a doctorate in this context from a very large university will open doors for us in the field of data science and machine learning.

But you really don’t need any degree to become a Data Scientist, let me explain how to do it.

## How to become a Data Scientist

There are 5 steps to becoming a Data Scientist, they are:

- Learn Python.
- Learn math (linear algebra and statistics)
- Learn Data Science Libraries of Python.
- Learn SQL.
- Build a Project

## 1 – Learn Python

Python is a great language for data science, now there will be people who say, to learn **R** or **Matlab** instead of python. Don’t listen to them. Yet one day you may need to learn R and Matlab.

But for starters, Python is the best choice for me, because external library support for data science and machine learning is better and easier in python.

And luckily, in the following link, you will be still able to find all the materials.

This is a collection of free Python courses that should give you enough knowledge to start learning data science tools.

## 2 – Learn math (linear algebra and statistics)

Data science is the application of mathematics to data. If you don’t know math, you’ll struggle with data science. You can start learning math for data science using **Numerical Python** and **Statistics** .

This is a course provides an introduction to using Python to learn linear algebra. It is designed for people who have no (or little) previous exposure to Python or to linear algebra.

Also, with this free EDX course you can learn :

- Data collection, analysis and inference
- Data classification to identify key traits and customers
- Conditional Probability-How to judge the probability of an event, based on certain conditions
- How to use Bayesian modeling and inference for forecasting and studying public opinion
- Basics of Linear Regression
- Data Visualization: How to create use data to create compelling graphics

## 3 – Learn Python Libraries

The Python libraries are absolutely fantastic. If you don’t know what a library is, it’s basically something you can add to python, which gives python a lot more functionality.

There are great libraries for data science you should learn:

**Numpy**(for linear algebra)**Pandas**(for statistics and data manipulation)**Matplotlib**(for data visualization)**Learn all data science topics**

After learning all these libraries, you can start working on your own projects. You can search the internet to download the datasets for your practice.

You can download the datasets from https://catalog.data.gov/dataset.

And, this is a collection of free Python Libraries courses that should give you enough knowledge to start.

Also, check these two free course from **kaggle** :

## 4 – Learn SQL

Data is everywhere. In fact, it is at the heart of newer disciplines such as Data Science and Data Analysis.

It’s essential learn how to manipulate relations using relational algebra operators, and apply these theoretical concepts to a widely used language: **SQL** , allowing you to interact with relational databases!

Here is a special collection of courses that teaches you enough SQL to start as a data scientist.

Also, I suggest these two free course from **kaggle** :

## 5 – What is next ?

**Build a Project** :

I feel the best thing after learning any course actually in the programming space is to build a project.

I find that I learn better through **project-based learning. **Learning to program can be stressful sometimes, but having a project to develop as you program could really help re-enforce what you have learned.

**Register on Github** :

Here you can share your projects with everyone in the world.

The idea is to create a good portfolio of your projects at Github, so people know what you do and how good you are at programming.

Try to upload at least one project each month to Github, so that when employers ask for your contribution to a project, you can impress them with your skills.

After taking the courses above and build some projects, you will be ready to apply for the jobs. You will find many opportunities where you will be a good fit.

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