Contents: 1.Data Science: Key Concepts 2.Data Wrangling
3.Spotting Signals: An Overview
4.1. Introduction to R
4.2. Business Storytelling Using R
5.1. Problem based Analysis
5.2. Model
6.1. Bivariate Analysis
6.2. Cross Tabs
7. Correlation Matrix
8.1. Visualization and Visual Constructs
8.2. Advance Visualization
9.1. Machine Learning in Action
9.2. Decision Trees
9.3. Support Vector Machines
9.4. Naive Bayes
9.5. Linear Regression
9.6. Regression
9.7. A/B Testing
9.8. Classification
9.9. Introduction to Gradient Boosting
10.1. Sample Preparation
10.2. Data Train and Test Data
11.1. Multivariate Analysis Topics
11.2. Principal Component Analysis
11.3. Factor Analysis
11.4. ANOVA
12.1. Additional Topics in Analytics
12.2. Exploratory Data Analysis Case
Study – Business Perspective
13. Text
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