This document serves as the introductory chapter for the Statistics for Data Science course at the graduate level. It focuses on the fundamental principles of Exploratory Data Analysis (EDA), shifting the focus from simple computation to critical statistical interpretation . Overview of Topics covered: - Measures of Central Tendency - Measures of Dispersion - Measures of Shape - Data Visualization - Multivariate Descriptive Statistics Applied Learning: This chapter includes comprehensive R Labs and Case Studies designed to simulate real-world data challenges . Students will learn to implement these statistical concepts using modern R programming techniques to derive actionable insights from raw data.

Students are able to 1)perform descriptive statistics 2)apply appropriate non-parametric statistics tests to answer research questions of interest.

Students are able to 1)perform descriptive statistics 2)apply appropriate non-parametric statistics tests to answer reseach questions of interest.

Students are able to use R language to analyse data using multiple linear regression: 1. Perform linear regression analysis 2. Check Normality Assumptions 3. Check Constant Variance Assumptions 4. Check Independence (Autocorrelation) Assumptions 5. Dealing with Invalid Model Assumption

Students are able to use R language to 1. perform descriptive statistics 2. construct scatterplot between two quantitative variables 3. perform correlation analysis 4. perform linear regression analysis and inference on regression parameters 5. interpret the results

Students are able to use R language to analyse data using multiple linear regression: 1. perform descriptive statistsics 2. transform qualitative independent variable into dummy variables 3. select independent variables 4. perform linear regression analysis and inference on regression parameters 5. interpret the results