Data Exploration and Visualization for BE Anna University R21CBCS (III- AI & DS- AD3301)

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Syllabus Data Exploration and Visualization - (AD3301) 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 - Grouping Datasets - data aggregation - Pivot tables and cross-tabulations. (Chapter - 1) UNIT II VISUALIZING USING MATPLOTLIB Importing Matplotlib - Simple line plots - Simple scatter plots - visualizing errors - density and contour plots - Histograms - legends - colors - subplots - text and annotation - customization - three dimensional plotting - Geographic Data with Basemap - Visualization with Seaborn. (Chapter - 2) UNIT III UNIVARIATE ANALYSIS Introduction to Single variable : Distributions and Variables - Numerical Summaries of Level and Spread - Scaling and Standardizing - Inequality - Smoothing Time Series. (Chapter - 3) UNIT IV BIVARIATE ANALYSIS Relationships between Two Variables - Percentage Tables - Analyzing Contingency Tables - Handling Several Batches - Scatterplots and Resistant Lines - Transformations. (Chapter - 4) UNIT V MULTIVARIATE AND TIME SERIES ANALYSIS Introducing a Third Variable - Causal Explanations - Three-Variable Contingency Tables and Beyond - Longitudinal Data - Fundamentals of TSA - Characteristics of time series data - Data Cleaning - Time-based indexing - Visualizing - Grouping - Resampling. (Chapter - 5)

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Pages: 236 Edition: 2022 Vendors: Technical Publications