In the present day, ‘data scientist’ is one of the hot and trending job titles that people want to have. It is a hugely challenging and fun field to be in. So, there are many people aiming to find out how to become a good data scientist.

To become a good data scientist, candidates should go through proper data science training. There are many good and free resources like Massive Open Online Courses (MOOCs) and tutorials that can help those motivated people to become a data scientist, but I am very much skeptical whether these tutorials actually help them or not.

Before talking about data scientists and the processes of becoming one, let me define data science. So, data science is a field that uses scientific processes, systems, methods and algorithms to extract insights or knowledge from both structured and unstructured data in various forms.

To become a data scientist, people must have sound knowledge about programming languages and algorithms. Along with programming languages and algorithms, a data scientist must understand and think about the things like data generating processes and they should also keep in mind that not specifying them properly can largely affect the inference that is drawn from the data analysis.

This takes a lot of training and a huge amount of time for this to come naturally. A data scientist should have a solid foundation in Mathematics. Basic twelfth grade mathematics is good enough to start the journey on the path of learning data science. The focus is mainly on linear algebra and calculus.

I would suggest that candidates should start their studies from MIT open courseware and then move to a refresher course in linear algebra. This is one of the integral parts of data science training for beginners. There are various topics that a fresher needs to brush up, like hyper parameters and cluster analysis. These few topics are important for machine learning. Vector calculus is also one of the important domains that have to be covered for proper understanding of data science concepts. Statistics is also an important domain that covers a major part of data science training.

In data science, there are some important assumptions that, if not specified, can render the results invalid. A proper training in statistics will teach on how to know all these assumptions, help them to understand the consequences if the assumptions are not met, it teaches how to solve the situation or the error. As mentioned earlier, machine learning is also an integral part of data science training.

Machine learning focuses on making exact predictions over unseen data. Beginners can use the help of Coursera for machine learning courses. This is perfect for a fresher to take the first step for machine learning.

As I earlier said, a person should have a sound knowledge about programming language for learning data science. There are many languages to learn. Any language will give enough ideas about programming and coding.

This being said, I would suggest that R and Python are popular languages for learning data science. These two are the most effective languages that are important for data science training and it will offer a good start to any person who intends to learn data science.

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