Module 1
- 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
- Basics of Python Programming
Need for R, Features of R, Download, Setup, Installation; Python & Python Studio; Configuration. eg. Learning to set up Python and share Code
- Data Structures in R
Creating and Understanding Basic Data Structures in Python - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in Python programming.
- Data Manipulation & Summarisation in R
Understanding how data can be summarised in different ways to do Descriptive Analysis which describes features of data.
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, Alternative Hypothesis, F Test, ANOVA Introduction)
- Linear Regression (Using Python)
Start of Machine Learning, Develop a Prediction Model for predicting financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
- Visualization using Graphs
Creating Graph in Python 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 a target for sale increase
- Decision Trees
When to use CART & CHAID to create a decision tree based on categorical variables.
- Ensembles (Bagging & Boosting)
Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from a business point of view).
Module 4
- Understand how Association Rules can be created using Market Basket Analysis. Finding Interesting association between items purchased by Customers and building strategy 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.
- Post Learning
- Final Assessment
- Certificate Dispatch
- Monthly Brush Up Sessions- Live Online (12months)
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