Roadmap to becoming a Data Scientist

 “Data Science” - the buzzword of the 21st century introduced not very long ago is showing nothing but immense potential to change the way we work with data and computers itself. I’m sure everyone has heard about it but did you know it holds a place in the top 10 career options in the field of technology? It has one of the highest demands in the industry. But what does it take to become a data scientist? Let’s discuss.

Learning a Programming Language

First, you must get started with a programming language like Python or R. There are other programming languages as well but it’s better to use the ones which are relevant in the industry. There is an added benefit of huge libraries when it comes to Python or R. Here is how to get started with any of these two programming languages,

• Basics like variables, functions, loops, etc.

• OOPs concepts.

• Libraries. For example, in Python we have libraries like Numpy, Pandas, Matplotlib, Seaborn, etc.

Getting familiar with Mathematics

Mathematics plays as a foundation in dealing with data and analysing it. So it is important to have a basic understanding of the following concepts. These are in the order of priority,

• Statistics - mean, median, mode, covariance, etc.

• Linear Algebra - vectors, matrix operations, eigenvalues and eigenvectors, etc.

• Calculus - derivatives, meaning of first and second variable, etc

• Probability - conditional probability, random experiment, bayes theorem, distributions, etc.

Data Visualisation Tools

These tools help us in visualising and analysing large amounts of data. Although it is not necessary when starting off early, it is very useful when going for internships. MS Excel is a great way of starting with a small amount of data and you can gradually move your way to handling large amounts of data with something like Google Data Studio.

Machine Learning Algorithms

This is a concept that will take your maximum amount of time but it is also one of the most important ones. These algorithms are divided into four categories. They are,

• Supervised

• Semi-Supervised

• Unsupervised

• Reinforcement Learning 

Under each of these there are several algorithms like logistic regression, linear regression, decision tree and many more. You must also know the math behind these algorithms which are concepts mentioned earlier. If you are familiar with those it will be a piece of cake. Python libraries like scikit-learn can be used to deploy these algorithms.

Deep Learning

Under Deep Learning, you have to focus on algorithms like artificial neural networks, convolutional neural networks, recurrent neural networks, etc. To deploy these libraries like tensorflow can be used. I know these terms are new but once you dive deep you will understand everything.

Apart from these you must also have some knowledge about Databases to structurally store your data and Git to showcase all of the projects you have built.

I hope you found this helpful. All the best on your Data Science journey.