Curriculum for the GSSC Taining plan

This document contains the Curriculum for the Training plan. The plan is divided into 4 basic modules. The Business Translator Module cuts across the other modules and is listed in green. If time permits we will be doing some additional concepts. These are highlighted in red

• Essentials
• Specialist - Modeling
• Specialist - Visualization
• Business Translator

Essentials Module

The Essentials Module is designed to provide a gentle introduction to the basics of Data Analysis using the R Statistical programming Language. This module also includes assignments after each class

• Business Translator series
  • Problem design and Problem Definition frameworks
  • Business Context
    • Knowing your Client and Client organization
    • Understanding overall Organizational imperatives
    • Analytical Design thinking in Business scenarios
    • Case Study 1 - Customer value analysis
      • Defining a value metric
      • Setting up a good Exploratory Data analysis (EDA)
      • Generating a list of Hypothesis
      • Checking Business feasibility
      • Shortlisting hypotheses to Analytically feasible list
      • Presenting EDA results
    • Case Study 2 - Retention Analysis
      • Defining a Retention metric
      • Generating Hypotheses for churn
      • Identifying data for each hypotheses
      • Setting up a good EDA
      • Choice of Modeling techniques available

• Introduction to R
  • Installing the software
  • Understanding R Studio
  • Introduction to OOP paradigm and underlying language syntax
  • Data Structures in R
  • Using R as a calculator
  • Installing packages
  • How to use documentation
  • Loops and Conditionals
• Importing Datasets
  • Importing from Excel sheets and CSV files
  • Writing to disk or Outputting files
• Data Wrangling (using the dplyr package) on an Insurance Dataset
  • Working with the Dataframe object
  • The 5 verbs of dlpyr
  • Performing joins
  • Cleaning a dataset
    • Missing value treatment
    • Creation of flag variables and new columns
    • Subsetting a dataset
    • apply() functionals
• Advanced R
  • Connecting to Databases
  • Using SQL in R
  • Dealing with DateTime objects
  • Using the plot function
  • Univariate analysis of data
    • Simple binning and plotting
    • Trendlines
    • Scatter plots
  • Introduction to ggplot package
• Introduction to SQL Databases
• Introduction to PowerBI

Specialist - Modeling Module

The Modeling module is designed to introduce Statistical modeling concepts. This module will provide examples and practice of basics of Univariate and Bi variate models. You will also be introduced to the GLM package in R. Finally there will be an application project - Cross sell of Insurance products

• Introduction to Summary Statistics
• Revist Outlier treatments and creation of an analytical dataset
• Univariate analyses
• Understanding Variance and Dimentionality of Data
• Introduction to the GLM package
  • Logistic Regression
  • Linear Regression
  • Poisson Regression
• Modeling concepts
  • Selection of a model
  • Building a model
    • Variable selection
    • Variable transformations
    • Understanding Model outputs and contributions
  • Comparing 2 models using Model Characteristics
  • Model deployment using R Shiny app
• Project - End to End creation of a Cross sell model
  • Business imperatives to do Cross sell
  • Survey of available techniques
  • Model selection
  • Building a model and real challenges
  • Interpreting model results
  • Creation of a presentation

• Business Translator series
  • Solution visualization
  • Storyboarding
  • Insight generation

• (If time permits)
  • Basics of Machine Learning and AI
  • Supervised and Unsupervised Learning
  • Support Vector Machines
  • Random Forest
  • Basics of Artificial Neural Nets using Tensorflow (Google Platform for Neural Nets)

Specialist - Visualization Module

• Thinking of a Dashboard in the Server-Client paradigm
  • Frontend as a User friendly layer
    • User base and their usage characteristics
    • How to design drill downs - When are they useful vs When do they confuse?
  • Backend as a data efficiency layer
    • Designing for required user load
    • Determining architecture based on usage
• Advanced PowerBI
  • Connecting to databases
  • Creating self updating dashboards
  • Advanced R visuals for PowerBI
  • Using Maps
• R Shiny
  • Creating a basic server client code
  • Publishing a dashboard
  • Frequently used apps and the code for them
  • A few tips and tricks for publishing apps
    • How to customize interactions
    • How to customize appearance
• Model performance monitoring using a Dashboard
• Dashboard build 1 - Power BI application
• Dashboard build 2 - R Shiny application

• (If time permits)
  • D3 - Data driven documents (Javascript based Visualization)
  • Bokeh - Python based Visualization library

Business Translator series

• Understanding your organization
• Importance of Client context
• Importance of Problem context
• Problem Design
  • Hypotheses driven vs Data Driven
  • Analytical design of a Business problem
  • Understanding required data sources
  • Data sources outside the firewall
• Solution Visualization
  • Data source mapping
  • Code flow and Problem flow diagrams
  • Creating Solution artifacts
  • Visualizing end product and its usability by end users
  • Insight Generation - A helpful framework
  • Storyboarding and Client presentation
  • Estimating and Tracking Impact of a project
  • Communication with stakeholders and setting expectations
• Creating project artifacts
• Creating an Analytics roadmap