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 and R Programming
Need for R, Features of R, Download, Setup, Installation ; Python & Python and R & R Studio; Configuration. eg. Learning to setup Python and R and share Code
- Data Structures in R
Creating and Understanding Basic Data Structures in Python and R - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in Python and R programming
- Data Manipulation & Summarization in R
Understanding how data can be summarized 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 and 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
- Visualization using Graphs
Creating Graph in Python and R understanding which graph to be used when
- Missing Value and Outlier Analysis
Understanding how missing values & outliers are handled in data summarization & modeling
- Logistic Regression
Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables e.g. 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.
- Ensembels (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 strategy to sell more
- Twitter Analysis
Configure Twitter Account & Application; Setup for downloading tweets and analyses them for positive and negative sentiments related to Financial News/ Articles
- Post Learning:
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