Program Overview
Learning Outcomes:
Provide orientation to:
Course Differentiation:
1. Economical program for fast learning of practically used concepts in Financial analytics
2. Tool-based demonstration of the application of analytics to the Financial data
3. Brings multiple practical case-based examples, scenarios and exercises directly from industry practitioners themselves
About the Trainer:
Certified Financial Analytics Practitioner Course is delivered by Empanelled Domain Experts of Henry Harvin Education.
These Industry Professionals have extensive experience as practitioners and trainers of Financial analytics.
DATES | CITY | DURATION | PRICE | ENROLL |
---|---|---|---|---|
On-Campus | On-Campus | 32 Hours | INR15,000.00 | |
9th, 10th, 16th & 17th February 2019 | Live Virtual Classroom | 32 Hours | INR15,000.00 |
Outlier Treatment
Handling Missing Values
Financial Data using - EXCEL
MS Excel Functions
Pivot Tables
MS Excel Charts
Case Study
R-studio
Understanding Basics
Packages in R
Swirl R
Summarise & slice data
Case Study
What is regression?
Introduction to Linear Regression
Applications & examples from the financial world
Case Study
Understanding Data using R
Uni-variate analysis
Bi-variate analysis
Best fit line – regression
R-square, Adjusted R-square concept
Test vs. Train datasets ( concept )
Running the regression in R
Linear Equation & significant variables
Assumptions of Linear Regression
Testing Multi-collinearity
Heteroskedasticity
Case Study
Classification techniques & financial problems
Intro to Logistic Regression models
Why Logistic vs. Linear models
Odds Ratio
Probability of an event
Applications of Logistic regression models
Case Study
Generalized Linear models using R
Shortlist the significant variables
Test vs. Train data sets
Validation of the model – Confusion matrix
Evaluate the model and give business recommendations.
Introduction
Applications & industry examples
Types of Decision Tree ( CART & CHAID )
Splitting Algorithms
Case Study
R-code
Interpretation
Final o/p
Case Study: Net Promoter Score (understand NPS & its Promoters by uncovering the relationship between the variables to devise customer satisfaction improvement strategy)
R-code
Interpretation
Final o/p
Financial analytics & unsupervised techniques
Why and Where to use Clustering
Clustering methods and examples
K-means Clustering Algorithm
K-means – Cluster the given set of customer base to help in segmentation to help identify different marketing campaigns for each cluster
Practice the k-means using R codes
Identify differences in behaviour of
Online shoppers
In-store shoppers
Multi-channel shoppers
Intro to the Basics
Applications
Algorithm
Case Study
R-code
Interpretation
Final o/p