# How to Become a Data Scientist for Free: Learning Resources

Data scientists are eternal for the success of all organizations in today’s data-driven world, where there is still a colossal amount of data to analyze. Today, every business strives to acquire professionals who are proficient in data science. However, becoming a data scientist comes with its own set of challenges.

### Difficulties on the way to becoming a data scientist

First, very few colleges offer data science courses, so there is a huge gap between demand and supply in today’s market. Second, the course fees are too high, making them unaffordable for many aspirants.

Data science aspirants opt for paid online courses to streamline their learning to improve their skills. However, if you want to learn the skills of a data scientist, you can do that for free as well. The only compromise is that you will not be certified by any institute.

### Skills to learn

To become a data scientist, you have to become familiar with a wide range of subjects and tools while avoiding burn-outs. It often takes at least 6 months to acquire and acquire the skills required to become a data scientist. This can be intimidating for many, so it is essential to design a roadmap to minimize burnouts.

Here are the skills most in demand for a data scientist:

- Programming
- SQL
- Python
- R

- Mathematics
- Linear algebra
- Calculation

- Probability and statistics
- Basic and advanced probability
- Descriptive and inferential statistics
- A / B tests

- Data analysis skills
- Data dispute
- Visualization
- Technical writing

- Machine learning
- Supervised teaching
- Unsupervised learning
- Reinforcement learning

- Other data science tools

### Links to free data science courses

To lay the cornerstone of your data science career, you must master programming to query data from databases and other sources such as web pages and documents, and then perform analysis to uncover information. on the data.

To get started, you can start by taking these free courses:

- Introduction to SQL for Data Science
- Learn Python for scratch
- R programming
- Introduction to data analysis

It is not mandatory to learn both Python and R at the same time, so you can choose one of them and later learn the other.

A technically trained aspirant will already be familiar with linear algebra and calculus, so a refresh of his academic books would be enough to move forward with probability and statistics.

While people typically learn probability and descriptive statistics in school, they will need to focus on inferential statistics to get a hunch from the data and infer from a plethora of data. Plus, learning A / B Testing will help you make decisions by choosing between different approaches.

Binge learn with these statistics courses:

- Introduction to descriptive statistics
- Deductive statistics
- A / B tests

All of the above courses will lay the foundation for getting your hands-on analysis now. This is where you have to bring together all the know-how to analyze big data.

- Data science: quarrels
- Data visualization with Python
- Free training videos – Tableau

These courses will help implement programming skills to collect, cleanse, and visualize data. While this can help you get started, it is up to you to practice and become an expert as the data differs dramatically. Therefore, learning different methodologies for collecting and cleaning data will help you stand out.

If you get this far, you can call yourself a data analyst. Congratulate yourself and move on to acquiring machine learning skills.

Machine learning is a huge subject to learn over a short period of time, but the courses below will equip you with the basic skills that can be used over a period of time to master the skill. Effective methods of training models and predicting future events should be regularly practiced while trying to balance the bias and variance of the models.

- Introduction to machine learning
- AWS DeepRacer Reinforcement Learning (To start)

Are you done with all of the above? If so, you’ve taken the plunge and become a machine learning engineer. However, there is one last step to be taken in order to become a data scientist.

There are many tools that make it easier for data scientists to tell stories with insights. Learning the tools below will allow you to learn advanced visualization techniques and effectively manage Big Data.

Other tools course

- Spark
- Data visualization and D3.js

### Outlook

While we’ve covered the most important data science tools, others are generally adopted in a few organizations. Therefore, learning them will be a plus as it will differentiate you from others. Learning the aforementioned technologies is just a drop in the ocean. One needs to practice and develop one’s data intuition skills by practicing and competing on multiple data science hosting platforms such as Kaggle, DrivenData, and others.