Free Data Science Foundations Course

This section gives you various examples to help you understand Data Science. It explains how you decide on a place for the vacation, how the weather is predicted, and sales during a particular time in a year using data science. 

Data Science life cycle revolves around data acquisition, preprocessing, ML algorithms, pattern evaluation, knowledge representation, and analytical strategies to predict and proffer insights, which we shall learn in this module, into the procedures to yield the best results.

Data mining tasks include classification, prediction, association, clustering, and summarization. This module explains anomaly detection, continues with matching data points and explains the concept with a real-life example.

Machine learning is a learning method to process raw data based on the previously trained model for similar input data. This section explains how machines understand the patterns and the features through which it tags every data.

Most commonly used programming languages used in Data Science are R (for statistical computation) and Python (including MATLAB library), which we will know why in this section. We will also look into the famous libraries in each of these languages for Data Science.