Today we will know how to

**become a data scientist,**Data science is an emerging field and if you have decided to build your career in this field then I must say it’s a great choice because many companies are showing their interest in data science but you should know about the requirements to build a career in this field.To help you out here are the basic requirements to build your career in data science:

**Good Mathematics skills:**

In this field, you have to deal with data and you should have good mathematical skills because you will be using different types of statistical techniques to get important insights from the data.

**Learn Programming Languages:**

The programming languages that you will use the most in this field are:

- Python
- R

Java is also another programming language for data science but most of the data science enthusiast uses “Python and R” because Python is easy to learn and understandable programming language and R is very good when you have to develop some statistical projects.

Here are the links from where you can learn basic Python and R for free:

**Learn SQL and Databases for become a data scientist:**

As I already said that you are going to work with data and data can be in any form, it can be from an email, a picture, a video, an excel or CSV file, etc. So, it will be a plus point if you learn SQL, SQL and NoSQL databases like oracle database and MongoDB.

Here is the link from where you can start with SQL for free:

**Learn Data analysis:**

After learning python and r you should know how to use these languages to analyze the data. Learning data analysis will help you to get useful insights from the data such as the correlation of different variables (columns) in the data, mean of the data and many more things.

Here are the links from where you can learn data analysis for free:

- Data Analysis with python: https://cognitiveclass.ai/courses/data-analysis-python

- Data Analysis with R (coming soon): https://cognitiveclass.ai/courses/data-analysis-r

**Learn Data visualization:**

You should learn how to build good visuals of your data because after analyzing your data you should represent it using some good visuals like barplot, scatterplot, histogram, box and whisker plot, etc. The main objective of data visualization is to present your findings (which you have found after analyzing the data) to your team members or your manager or other employees.

Here are the links from where you can learn data visualization for free:

- Data visualization using python: https://cognitiveclass.ai/courses/data-visualization-with-python

- Data visualization using R: https://cognitiveclass.ai/courses/data-visualization-with-r

**Learn Machine learning:**

After you have learned data visualization the next step is to learn machine learning. If you honestly followed the above steps then trust me you will find machine-learning is an easy field but if you don’t follow the path which I am telling and you directly jump to machine learning after learning python then you will find it so much difficult to understand it. So, it’s my request, please don’t skip any step if you want to build your career in data science.

Here are the links from where you can learn machine learning for free:

- Machine learning with python: https://cognitiveclass.ai/courses/machine-learning-with-python

- Machine learning with r: https://cognitiveclass.ai/courses/machine-learning-r

**Keep practicing it helps you to become a Data Scientist: **

At this stage you know that you have learned machine learning so know it’s your turn to show the skills that you have gained till now. You should practice your skills by building some good projects for the open-source community or it can be your personal project also.

**The closing point:**

Now I have told you about all the stages now next are to check your own curiosity towards data science. So, now you will build your own path towards data science by learning deep learning (you can explore the same website for the free course) and keep practicing your skills by developing good projects.

**Also read:-**

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Thanku You are explaining in a very easy way