Post Graduate Certificate in Data Science & Machine Learning

This Certificate builds a solid foundation in Data Science & Analytics by covering industry standard tools and techniques through a practical, industry-oriented curriculum. You’ll learn competencies in the core focus areas of Data Science, Machine Learning, Mathematics, and Data Visualisation. This program assumes no prior knowledge of coding in Python or R and begins with basic principles.

By the end of the 6-month program, you will have a solid understanding of techniques critical in performing Data Analytics and will be able to create analytical models using real-life data that delivers invaluable insights for your business and career.

Course 1: Programming for AI & Data Analytics
- In this course, you will learn and develop the necessary knowledge and skills for the most prevalent programming languages in the Data Science domain: Python and R. You will learn Python and R basics, data structures, programming constructs, and how to work with data using key Python packages Pandas, Matplotlib, Seaborn, and R.

Course 2: Linear Algebra Basics
- This course provides all conceptual knowledge from Linear Algebra required in the domain of Data Science and Machine Learning. First, you will be introduced to real vector spaces and then to the linear transformations and their representations in terms of matrices. You will learn the importance of eigenpairs in machine learning and various concepts such as orthogonality and projection.

Course 3: Foundations of Data Analytics
- This course is divided into two parts. In the first part, you will be introduced to the concepts from probability theory and statistics with strong relevance in machine learning and data science. In the second part, you’ll explore gradient calculus and numerical optimization algorithms like gradient descent, stochastic gradient descent, etc.

Course 4: Machine Learning Techniques for AI & Data Analytics
- In this course, you will learn Machine Learning (ML) concepts and algorithms. This course will cover machine learning concepts and popular supervised and unsupervised learning techniques.

Course 5: Selected projects from Kaggle
- As a data scientist or ML engineer you are supposed to implement the selected machine learning project and compete with your peer-group. Through this industry style project, you will learn to:- - Adjust to team work environments and gain industry-like experience in a machine learning project - Familiarize oneself with Kaggle platform and understand the objective of the analytics problem in the project - Download data and produce first prototype of the ML model - Experiment with their ML model to improve its performance - Submit the project and share the learnings as a team