In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.

In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.

In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.

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