Math, probability and statistical modeling Exploring probability and inferential statistics, quantifying correlation, Reducing Data Dimensionality with linear algebra, Modeling Decisions with Multi Criteria Decision making, Regression Methods, Detecting outliers, Time-series analysis. (Chapter - 1) 2. Using Clustering to subdivide data Introducing clustering basics, identifying clusters, Categorizing data with Random forest algorithm. (Chapter - 2) 3. Modeling instances Recognizing the Difference between Clustering and Classification, Making sense of data with nearest neighbor analysis, classifying data with average nearest neighbor algorithms, classifying data with K- nearest neighbor algorithms, Solving Real-world problems. (Chapter - 3) 4. Principles of Data Visualization Design Data visualization : The big three, Designing to meet the needs, Picking the most appropriate Design style, Choosing how to add context, Selecting the appropriate Data Graphic Type, Choosing a Data Graphic, Using D3.js for Data Visualization. (Chapter - 4) 5. Web based applications for visualization design, Exploring best practices in Dashboard Design, Making maps from spatial data. (Chapter - 5) 6. Data science for driving growth in E-commerce. (Chapter - 6)
