Financial Analytics Practitioner | Data Analytics Courses | Henry Harvin

ABOUT THE COURSE

Program Overview

  1. 5-days / 20-hours Online Training and Certification Course producing competent Financial Analytics Practitioners
  2. Delivered by Senior Industry Professionals having extensive experience as practitioner and trainer of Financial Analytics. These practitioners are also empanelled as domain experts with Henry Harvin Education
  3. Distinguish your profile with global credential of ‘Certified Financial Analytics Practitioner’ and showcase yourself as a certified financial analytics professional by using the hallmark of CFAP next to your name
  4. Get 24x7 access to Henry Harvin’s high-tech LMS platform (App / Mobile App Based) with abundant content on Financial Analytics (ppt, pdf, videos, articles, important links, data etc)
  5. Gain hands-on experience of Financial analytics with industry standard tool & techniques applicable in real situations
  6. Practice real-life case studies, strategies and examples through above tools and techniques in session activities, videos and presentation slides (ppts.)
  7. Get 100% Placement and Internship Support exclusively entitled for CFAP Professionals


Learning Outcomes:
Provide orientation to:

  1. Explore, Analyse & Solve Financial Problems using Analytics Tools like R & Advanced Excel.
  2. Understand 'What' & 'How' aspects of Financial Data Collection
  3. Get insights from Industry Best Practices for Financial Measurement & Analysis
  4. Communicate Financial Objectives in a focused manner with data analysts & help evaluate results of data analysis better
  5. Develop Goal Oriented Financial Strategies
  6. Gain Full View of Customers across Different Channels by Accessing+Integrating+Analysing Customer Data through Multiple Sources & Engage Customers in Real Time
  7. Understand Affinity of Product by Analyzing Transactional data
  8. Understand Analytics-Based- Financial to drive ROI for your Financial Campaigns
  9. Take Actionable Data-Driven Decisions to increase market share
  10. Ultimately, identify what is working and what is not working for your Financial Campaigns



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.

 
UPCOMING BATCHES
Dates City DURATION PRICE  
3rd, 10th, 17th & 24th June Live Virtual Classroom 32 Hours INR12,500.00

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COURSE CURRICULUM | Top 10 Queries
     

    Module 1: Introduction

    • What is Analytics
    • Types of Analytics
    • Financial meets Analytics
    • Organisations using Financial  Analytics                                                                                                               

    Data Collection & Data Understanding – Basics

    • Data Dictionary
    • Data types
    • Data Handling

    Outlier Treatment

    Handling Missing Values

    Financial Data using - EXCEL

    • MS Excel Functions

    • Pivot Tables

    • MS Excel Charts

    • Case Study

    R Programming

    • R-studio

    • Understanding Basics

    • Packages in R

    • Swirl R

    • Summarise & slice data

    • Case Study

     Linear Regression

    • 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

    Module 2: Logistic Regression

    • 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.

    Module 3: Decision Tree 

    • 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

    Module 4:  Clustering

    K-Means

    • 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

    RFM- Recency Frequency Monetary models

    • Identify differences in behaviour of

      • Online shoppers

      • In-store shoppers

      • Multi-channel shoppers

    Market Basket Analytics 

    • Intro to the Basics

    • Applications

    • Algorithm

    • Case Study

      • R-code

      • Interpretation

      • Final o/p

    Framework to solve analytics case studies

    • ​​CRISP – DM & other known frameworks
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