Certified Business Analytics Practitioner Course

ABOUT THE COURSE

Program Overview

  1. 4-days / 32-hours Classroom Training and Certification Course producing competent Business Analytics Practitioners
  2. Delivered by Senior Industry Professionals having extensive experience as practitioner and trainer of Business Analytics. These practitioners are also empanelled as domain experts with Henry Harvin Education
  3. Distinguish your profile with global credential of ‘Certified Business Analytics Practitioner’ and showcase yourself as a certified Business analytics professional by using the hallmark of CBAP next to your name
  4. Get 24x7 access to Henry Harvin’s high-tech LMS platform (App / Mobile App Based) with abundant content on Business Analytics (ppt, pdf, videos, articles, important links, data etc)
  5. Gain hands-on experience of Business 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 CBAP Professionals

Learning Outcomes:

Provide orientation to:

  1. Explore, Analyse & Solve Business Problems using Analytics Tools like R & Advanced Excel.
  2. Understand 'What' & 'How' aspects of Data Collection
  3. Get insights from Industry Best Practices for Business Measurement & Analysis
  4. Communicate Marketing Objectives in a focused manner with data analysts & help evaluate results of data analysis better
  5. Develop Goal Oriented Business 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- Business to drive ROI for your Marketing Campaigns
  9. Take Actionable Data Driven Decisions to increase market share
  10. Ultimately, identify what is working and what is not working for your Marketing Campaigns


Course Differentiation:
1. Economical program for fast learning of practically used concepts in Business analytics
2. Tool-based demonstration of the application of analytics to the Business data
3. Brings multiple practical case-based examples, scenarios and exercises directly from industry practitioners themselves

  About the Trainer:
  Certified Business Analytics Practitioner Course is delivered by Empanelled Domain Experts of Henry Harvin Education.
  These Industry Professionals have extensive experience as practitioners and trainers of Business analytics.

 
UPCOMING BATCHES
Dates City DURATION PRICE  
10th, 17th, 24th June & 1st July 2018 New Delhi 32 Hours INR15,000.00

15th, 22nd, 29th July & 5th August 2018 New Delhi 32 Hours INR15,000.00

On-Campus On-Campus 32 Hours INR15,000.00

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COURSE CURRICULUM | Top 10 Queries

    Module 1: Introduction

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

    Data & Analytics

    Data Collection & Data Understanding – Basics

      • Data Dictionary

      • Data types

      • Data Handling

        • Outlier Treatment

    • Handling Missing Value


     

    Marketing Data using - EXCEL

    R Programming

      • MS Excel Functions

      • Pivot Tables

      • MS Excel Charts

      • Case Study

      • R-studio

      • Understanding Basics

      • Packages in R

      • Swirl R

      • Summarise & slice data

    Case Study

     

    Linear Regression

    Module 2: Logistic Regression

      • What is regression?

      • Introduction to Linear Regression

      • Applications & examples from the marketing 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 & business 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

    RFM- Recency Frequency Monetary models

      • Marketing 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

    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
How it is beneficial for you?

Advantages to different domains and industries

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