Syllabus Exploratory Data Analysis - [CCS346] UNIT I EXPLORATORY DATA ANALYSIS EDA fundamentals - Understanding data science - Significance of EDA - Making sense of data - Comparing EDA with classical and Bayesian analysis - Software tools for EDA - Visual Aids for EDA- Data transformation techniques-merging database, reshaping and pivoting, Transformation techniques. (Chapter - 1) UNIT II EDA USING PYTHON Data Manipulation using Pandas - Pandas Objects - Data Indexing and Selection - Operating on Data - Handling Missing Data - Hierarchical Indexing - Combining datasets - Concat, Append, Merge and Join - Aggregation and grouping - Pivot Tables - Vectorized String Operations. (Chapter - 2) UNIT III UNIVARIATE ANALYSIS Introduction to Single variable : Distribution Variables - Numerical Summaries of Level and Spread - Scaling and Standardizing - Inequality. (Chapter - 3) UNIT IV BIVARIATE ANALYSIS Relationships between Two Variables - Percentage Tables - Analysing Contingency Tables - Handling Several Batches - Scatterplots and Resistant Lines. (Chapter - 4) UNIT V MULTIVARIATE AND TIME SERIES ANALYSIS Introducing a Third Variable - Causal Explanations - Three-Variable Contingency Tables and Beyond - Fundamentals of TSA - Characteristics of time series data - Data Cleaning - Time-based indexing - Visualizing - Grouping - Resampling. (Chapter - 5)
