Introductory Topics to Analytics

  • Understanding the need for Analytics in Specific Domain; CRISP Modeling
  • Introduction to Data Management
  • Properties & Types of Data, Measurement Scale, Basic Statistics on Data
  • Types of Analytics - Business, Marketing, Finance, etc

Basics of R Programming

  • Need for R, Features of R, Download, Setup, Installation ; R & R Studio; Configuration. eg. Learning to setup R and share Code
  • Data Structures in R
  • Creating and Understanding Basic Data Structures in R - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in R programming
  • Data Manipulation & Summarisation in R
  • Understanding how data can be summarised in different ways to do Descriptive Analysis which describes features of data
  • Import Export Data - CSV, Table, Excel, Google Sheet, Other Formats

Module 2 : Analytical Modeling

  • Understand what is Modeling and how it can be used in Various Domain
  • Statistical Tests -P Value, Z Value, Hypothesis, Null Hypothesis and Alternative Hypothesis, F Test, ANOVA Introduction)
  • Linear Regression (Using R) Start of Machine Learning, Develop a Prediction Model for predicting a financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
  • Visualisation using Graphs. Creating Graph in R and understanding which graph to be used when
  • Missing Value and Outlier Analysis
  • Understanding how missing values & outliers are handled in data summarisation & modeling
  • Logistic Regression . Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables eg. Predicting Cases for Fraud, Default on Payment etc
Module 3

  • Clustering. Grouping Customers based on characteristics so that they can be target for sale increase
  • Decision Trees. When to use CART & CHAID to create decision tree based on categorical variables.
  • Ensemble (Bagging & Boosting)
  • Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from business point of view)

Module 4

  • Association Rule Analysis. Understand how Association Rules can be created using Market Basket Analysis. Finding Interesting association between items purchased by Customers and building strately to sell more
  • Twitter Analysis. Configure Twitter Account & Application; Setup for downloading tweets and analyse them for positive and negative sentiments related to Financial News/ Articles

Code and Data Sharing through Git Hub, Git Configuration, Monthly Brushup Sessions