TOOLS YOU WILL LEARN
Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R.
Once you have the core skill of programming covered – dip your feet in the nitty-gritties of working with data by learning how to wrangle and visualize them.
It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.
Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.
Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib and Tableau.
Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.
Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.
The course culminates in an enterprise-level project for a fictitious client that will expose you to every stage of the data science process – from data acquisition and preparation to evaluation, interpretation, deployment, operations, and optimization. Every student is guided by industry experts as they bring their personal projects to life. Alternately, students may choose to work on a live project from their organization. Our students generally come from varying backgrounds. We encourage all our students to pursue projects that are best suited for their careers and domains. The project is an opportunity for you test your skills and demonstrate your ability to invent solutions for real world problems.