What's The Best Path To Becoming A Data Scientist?

Data scientists are big data wranglers. They take a massive mass of muddled data points (unstructured and structured) and use their skills in math, statistics and programming to clean, manage and organize them. Then they implement all their analytic powers –contextual understanding, industry knowledge, and skepticism of existing assumptions – to uncover unseen solutions to business challenges.



Learning Path to become a Data Scientist

Getting Started: The major step of them all is commencing your data science journey. This stage is all about comprehending what data science is and what role a data scientist has to play. Further, it is here you should choose a programming language and tool of your choice. This will allow you to code through what you learn.
Learning Basic Maths and Statistics: What are the core theories a data scientist must completely know? That would be mathematics and statistics. Where learning a tool will aid you to perform fast calculations and produce results, you can’t really become a data scientist unless you have a solid hold on statistical techniques.

Learning Machine Learning theories and implementing them: Once you’re done with the above stages, you will begin learning the fundamentals of machine learning. But it isn’t just limited to theoretical concepts. To truly understand the essence of ML you should start applying what you have learnt.

Some more applications of Machine Learning: Once you have a good hold on these fundamental techniques, you should move to more advanced topics, like ensemble learning, random forest, boosting algorithms, and time series methods. But ML isn’t limited to just the algorithms; you need to know nifty tricks to improve your model, right? That’s where validation strategies and feature engineering will play a role. You should focus on industry applications to become a pro at ML.

Introduction to Deep Learning: Now you know these machine learning concepts, what comes next? Deep learning of course! It’s becoming an essential part of any data scientist’s CV these days. You should start learning about deep learning to get your dream job in the field.

Various deep learning architectures like RNN, CNN: Follow that up with a deep dive into advanced neural network frameworks, namely recurrent neural networks and convolutional neural networks. These are fairly heavy concepts; hence it is recommended spending a few weeks on understanding them from scratch.

Natural Language Processing (NLP): No data scientist learning path is fully complete without first going over NLP. You should focus on learning the basics at the very least, including text preprocessing and text classification. If you’re feeling adventurous, you can explore how deep learning works in NLP but that’s not a mandatory requirement.

Data Science Certification Training from a reputed institution can help you make a successful career in the field of data science. Renowned institutions have state-of-the-art resources and faculty to ensure quality learning for students.