In this course, the student will learn basic concepts and techniques in data collection and some first exploration and analysis of data. It includes some essential techniques and tools for data exploration, data pre-processing and cleasing activities such as data grouping, smoothing, and subsetting. It teaches students to handle problems such as missing data, dimentionality reduction, and feature engineering. The course also trains the students with fundamental concepts and techniques of exploratory data analysis (EDA) helping them in summarizing, preliminarily visualizing, understanding data, and detecting outliers in large data sets. Using EDA tools, students will learn how to look for relationships between data attributes and how to choos approriate data modeling appoaches (taught later in the training program).