Business analytics -Abhay Mahalley

© EduPristine For [Business Analytics]
© EduPristine – www.edupristine.com
Business Analytics
Course Catalogue
ABHAY MAHALLEY

© EduPristine For [Business Analytics] 1
Business Analytics Program

What is Analytics:-
 Analytics is the application of computer technology, statistics and domain knowledge to solve
problems in business and industry, to aid efficient and effective design making.
 Analytics is the simply the scientific process of converting row data into knowledge to support
design making.
 Analytics involves finding patterns in data.
 The goal of Analytics is to improve business, society or personal performance by gaining
knowledge from data.
 Analytics is moving design making from Gut feel and guesstimates to better, more informed ones
driven by data.
2

About Data:-
 Data is growing at 40% compound annual rate reaching by 45ZB by 2020
 2.5 Quintillion bytes of data created each yr.
 90% of data in world was created in last 2 yr.
 Why is Analytics is USED-
 Design making is now fact and performance based.
 Intuition is out, metrics are in.
 Shorter time to market, demanding customer.
 Make each and every dollar count and increase return on investment.
 The real time design.
3

Different types of Analytics:-
 What happened or happening in the business?-Descriptive Analytics
 Why did it happened?-Inquisitive Analytics
 What is likely to happen based on historical Information?-Predictive Analytics
 What action should be taken?-Prescriptive Analytics
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Business Analytics-Concepts
 Statistical Analysis-Why is this happening?
 Forecasting-what if these trends continues?
 Predictive modelling-what will happen next?
 Optimization –What’s the best that can happen?
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Business Analysis Vs Business Analytics
Business Analysis-
 Creating business Architecture.
 Requirement Elicitation, Documentation of
Requirements.
 Business Process Analysis.
Business Analytics-
 Mine a data ware house to report past
performance.
 Analyze why something happened.
 Create predictive models to understand what
would happen in a given scenario.
 Prescribe a strategy based on rigorous
statistical analysis of data to ensure results.
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Why is Analytics used?
 Decision making is now fact and performance based
 Intuition is out, metrics are in
 Intense connotation, shorter time –to –market, demanding customers
 Make each and every dollar count and increase return on investment
 The real- time decisions
7

Uses of Analytics
Marketing
 Customer Segmentation
 Up Selling/Cross Selling
 Market Basket Analytics
 Marketing Media Mix Analysis
Financial Sector
 Credit Risk Management
 Credit Scorecard Modeling
 Fraud Detection
 Stock Market Analysis
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Uses of Analytics
Retail Analytics
 Shelf space allocation
 Analysis of customers preference for store
brand or brand names
 Pricing decisions
 Promotions and product bundle offerings
Media Analytics
 Decision making on allocation of air- time
of a new TV show
 Prime time rate for advertisement
 Analysis of channel viewership
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Retailing
Media
Analytics

What The Market Buzz On Analytics
 India’s analytics market to double to us$ 2.3 bn by 2018-Nasscom
 Analytics outsourcing to grow from us$ 42bn to us$ 71bn in 2016-Nasscom
 83% business leaders globally identified as their top priority-IBM
 Shortage of 1.5mn business analytics professional by 2018-McKinsey
 India has become a global analytic hub-Times of India
 The next big job boom is in analytics-up to 250k job openings in analytics over next 2 yrs. starting
salaries to be in region od Rs 5-9lacs PA-DNA
 Indian companies grooming data scientists to feed global jobs demand-Business Today
10

Training Objective-EduPristine
Business Analytics is a specialized course designed to deliver knowledge on application of
Statistical concepts in-
 Real world scenarios. This course is designed to equip professionals working in Finance,
Marketing, Economist,
 Statistical, Mathematics, Computer Science, IT, Analytics, Marketing Research, or Commodity
markets with the
 Essential tools, techniques and skills to answer important business questions.
Participants will be able to:
 Explore data to find new patterns and relationships (data mining)
 Predict the relationship between different variables (predictive modeling, predictive analytics)
Predict the probability of default and create customer Scorecards (Logistic Regression)
After completion of this program, the participants
 Understand a Problem in Business, Explore and Analyze the problem
 Solve business problems using analytics (in “R Studio”) in different fields
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Pre-requisites:-
 Pre-requisites for the course:
The participants are expected to have the basic understanding of the following topic:
* Basic Statistics
EduPristine provides comprehensive recordings of basis statistics concept along with its Business Analytics
course ware.
*Should have good analytical skills.
*Basic Excel knowledge.
12

Day 1 & 2 :(Online)-Basic Stats
Day 3 :Introduction and Data Analytics
Day 3 :Introduction and Data Analytics
Introduction to Analytics - Overview
Analytics v/s Analysis
Business Analytics
Business domains within Analytics
Data – Topic Covered
Summarizing Data
Data Collection
Data Dictionary
Outlier Treatment
Case: Categorization of data variables
Exploring credit card customer database to define the
variable types and categorizing each type into relevant
group.
Tool for Practice MS Excel
Introduction to Commonly used Tool in Analytics R software
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Day 4 & 5 : Linear Regression
Day 4 & 5 : Linear Regression
Linear Regression – Topic Covered
Correlation and Regression
Multivariate Linear Regression Theory
Coefficient of determination (R2) and Adjusted R2
Model Misspecifications
Economic meaning of a Regression Model
Bivariate Analysis
ANOVA (Analysis of Variance)
Multivariate Linear Regression Model
 Variable identification
 Response variable exploration
 Distribution analysis
 Outlier treatment
 Independent variables analyses
 Heteroskedasticity detection and correction
 Multicollinearity detection and correction
 Fitting the regression
 Model performance check
Case: Multivariate Linear Regression
 Identify and Quantify the factors responsible for loss amount
for an Auto Insurance Company
Domain Covered  Insurance Industry
Tool for Practice  MS Excel and R
14

Day 6 & 7 : Logistic Regression
Day 6 & 7 : Logistic Regression
Logistic Regression – Topic Covered
Identifying problems in fitting linear regression on data having “Binary
Response” variable
Introduction to Generalized Linear Modeling (GLMs) Logistic Regression
Theory
Logistic Regression Case
 Variable identification
 Response variable exploration
 Independent variables analyses
 Fitting the regression using SAS language
 Scoring equation
 Model diagnostics
 Analysis of results
 Check for reduction in Deviance/AIC
 Model performance check
 Actual vs Predicted comparison
 Lift/Gains chart and Gini coefficient
 K-S stat
 Score Card Development
Case: Multivariate Linear Regression
 Identify bank customers who will most likely default in making the
payment on balance due.
Domain Covered  Banking Industry
Tool for Practice in Class  R
15

Day 8: Decision Tree and Clustering
16
Day 8: Decision Tree and Clustering
Decision Tree & Clustering – Topic Covered
Data Mining and Decision Trees
Decision Tree Example
CHAID analysis
Method and Algorithms
Running the CHAID analysis and Interpreting the results
CART
Method and Algorithms
Running the CART analysis and Interpreting the results
When to use CART and when to use CHAID
Defining Clustering
Why and Where to use Clustering
Clustering methods
Clustering examples
K-means Clustering Algorithm
Case: CHAID & CART Analysis Identifying the classes of customer having higher default rate
Case: K-means Clustering
Identifying similar groups in database containing auto insurance
policy records using K-means Clustering
Domain Covered Insurance and Banking Industry
Tool for Practice in Class R

Day 9 & 10 : Time Series Modeling
17
Day 9 & 10 : Time Series Modeling
Time Series Modeling – Topic Covered
Models of time series
 Moving averages
 Autoregressive Models
The Box-Jenkins model building process
Model Estimation
Model Validation
Model forecasting
 Identify the ARIMA model
 Estimate the best ARIMA models
 Validate the model
 Forecast the sales based on model
Case I: Time Series Modeling on R
Case II: ARIMA Modeling
Forecasting future sales based on historical data for an automobile
company.
Domain Covered Automobile Industry

Day 11: Logistic Regression
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Day 11 : Logistic Regression
Logistic Regression – Topic Covered
Identify and develop Dependent variable
Perform initial variable reduction and missing value imputation
Perform extreme value treatment
Prepare correlation matrix and VIF chart
Variable reduction through Multicollinearity
Perform Binning to prepare modeling dataset
Perform sampling to prepare training and validation dataset
Run the model
Develop report for model outcomes
Write the Scoring or implementation strategy
Case: Up-Sell Model Propensity Model for Up-Sell in Telecom Industry
Domain Covered Telecom Industry

Day 12: Market Basket Analysis
19
Day 12 : Market Basket Analysis
Association Rule – Topic Covered
Affinity analysis to understand purchase behavior
Understanding Apriority algorithm
Capturing the insightful association available in the transaction
records
Analysis of output results to plan store layout, promotions and
recommendations
Case : Market Basket Analysis
Understanding apriority algorithm to identify affinity among the
purchase data in the basket based on historical transactions.
Domain Covered Retail Industry
Session End

Case studies (20Hrs Classroom Session)-Optional
Case Synopsis
Cross Sell Model Propensity to Cross sell health insurance products to
general insurance customers.
Market Mix Modeling Optimization of the promotion expense using Market
mix modeling
Churn Analytics Developing a churn model to gauge the propensity of
attrition among loyal and profitable customer segment.
Buy Till You Die Model Predicting the future number of transactions a
customer will make, thereby calculating the value of the
customer in his/her lifetime.
Customer Lifetime Value Analysis Predicting the customer survival along with the
profitability to model the life time value of each
customer
Telecom Model to Estimate Bill Building a model that can suggest right tariff plan based
on estimated bill amount
20

Data Visualization (20Hrs Classroom Session)-Optional
21
Data Visualization (20Hrs Classroom Session)-Optional
 Introduction
 The visualization design methodology
 The Data Visualization Process
 Working with Single Data Sources
 Using Multiple Data Source
 Using Calculations in Tableau
 Comparing Measures Against a Goal
 Tableau Geo coding, Advanced Mapping
 Showing Distributions of Data
 Statistics and Forecasting
 Dashboard Best Practices
 Sharing Your Work
 Case Study
 Exam/Exam Preparation

Course Features BA
Training Highlight
 10 Days Classroom Training (50 Hours) :- Get trained by topic experts with interactive learning.
 100 Hours Virtual Lab Practice (On SAS Language) :- Get hand on experience on SAS language
analytic Tool.
 25 Hours Live - Instructor Based Training ( On SAS Language) :- Get trained on SAS Language through
Live Instructor.
 Pre-requisite Video Tutorial on Basic Statistic and Data, along with "R Studio" Software :- Prepare
yourself before attending the classes by referring Basic Stats videos.
 Different domain case studies for practice purpose. Get the best training in analytics by
understanding real world problems and scenarios
 Subject wise Video recording for each module. Download the study notes to supplement video
tutorials.
 Webinar Video recording for each module. Download the recording to understand the topic in
better way.
 Forum to Discuss with Fellow Students and Experts Access material any time. Write to us and get
your doubts solved by our experts within 2 business days. You can also initiate a discussion by posting
it on our active forum.
 Lecture Handout Refer lecture material before & after the session
22

Course Features BA
Training Highlight
 Downloadable Course Material Download the whole material anytime during your 1 year
subscription and use it for any future reference.
 Tool used for Training – Classroom Session - MS Excel ; R Studio and online :- SAS Language Get
hand on experience of various analytic tools.
 24 * 7 Access to Online Materials Write to us and get your doubts solved by our experts within 2
business days. You can also initiate a discussion by posting it on our active forum.
 Certificate of Completion / Excellence A reference to get ahead in your career. At the end of the
course, you will receive a Certificate of Participation. You can also earn the Certificate of Excellence
upon completing our course assignment (Please get in touch with our sales representative for more
details).
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BA-Plus And Premium-Optional
Case Studies:-
 Get additional 4 Days Domain/Industry Specific Training.
Data Visualization Program Features:-
• 20 Hrs. Classroom Training
– Get trained by topic experts with interactive learning in small batches.
• Exam Preparation Session
– Prepare rigorously before competitive exam.
• Assignments & cases
– Work on real time cases from different domains.
• 24x7 Online Access
– to Course Material (Unlocked Excel Models, Presentations, etc.)
• Doubt Solving By Experts
– Write to us and get your doubts solved by our experts within 2 business days. You can also initiate a
discussion by posting it on active forums.
24

Course Highlights
25
Course Highlights BA PRO BA PLUS BA PREMIUM
Rs. 30,000 Rs. 45,000 Rs. 60,000
10 Days Classroom Training (50 Hours)   
100 Hours Virtual Lab Practice (On SAS Language)   
25 Hours Live - Instructor Based Training ( On SAS Language)   
Pre-requisite Video Tutorial on Basic Statistic and Data, along with "R
Studio" Software.
  
10 different domain case studies for practice purpose.   
Subject wise Video recording   
Webinar Video recording for each module.   
Forum to Discuss with Fellow Students and Experts   
Lecture Handout   
Downloadable Course Material   
Tool used for Training – Classroom Session - MS Excel ; R Studio and online
:- SAS Language
  
24 * 7 Access to Online Materials   
Certificate of Completion / Excellence   
4 Days Industrial Case Studies For Practice purpose -(20 Hours)  
4 Days Data Visualization Training -(20 Hours)  
PPT for Data Visualization Preparation  
Data Visualization Assignments for practice  
Exam Preparation Session
Preparing for Data visualization global Certification
 
Tableau Desktop version 8. examination:
Exam Registration-$250 Fees Included


Available Packages
Packages Available
BA-Pro BA-Plus BA- Premium
Rs.30,000 Rs.45,000 Rs.60,000
BA Training
BA Training + Case Studies
+ Data Visualization
training
BA Training + Case Studies
+ Data Visualization
training + Tableau
certification
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Payment Mode
Procedure to ENROLL
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• <click<Fees<Buy (debit/credit card)
 The bank details are given below:
• Bank Account Name: Neev Knowledge management Pvt. Ltd
• Bank Name: HDFC
• Branch Address: Maneji Wadia building, ground floor, Nanik Motwani Marg fort, Mumbai,
• Account Number: 00602560008449
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• Swift Code: HDFCINBB
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• Account Type: Current
• Address: 702, Raaj Chambers, Near Andheri Subway, Old Nagardas Road, Andheri East Mumbai 69
 Cash Payment (Handover to venue co-ordinator & collect the receipt on spot)
 Cheque Payment in favor of “Neev Knowledge Management Pvt Ltd”
 For Registration Please Contact: Anjana Singh-022 40938527 / 08879342887
27

© EduPristine – www.edupristine.com
support@edupristine.com
www.edupristine.com
See you in Class…
Anjana Singh
anjana@edupristine.com
+91- 22 4093 8527/088 793 42 887

Business analytics -Abhay Mahalley

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