Post Graduate program in Business Analytics

Modules

Big Data 101

Big Data Characteristics

Volume
Variety
Velocity
Veracity
Valence
Value

Big Data and Business

Data Relationships and Data Model

One-to-one relationship
One-to-many relationship
Many-to-many relationship
Flat model
Hierarchical model
Network model
Relational model
Star schema model
Data vault model

Data Grouping

Clustering Algorithms

partitioning
hierarchical
grid based
density based
model based

Getting ready for Clustering Algorithms

Clustering Algorithms – UPGMA, single Link Clustering

KPIs, Businesses & Data Elements

Mapping for business outcomes

Define the pain point
Define the goal
Identify the actors
Identify the impacts
Identify the deliverables
Creating your impact map

Basic Query

Advanced Query – Embedding

Mathematics Modelling

Introduction to key mathematical concepts

eigenvalues and eigenvectors

Application of eigenvalues and eigenvectors

investigate prototypical problems of ranking big data

Application of the graph Laplacian

investigate prototypical problems of clustering big data

Application of PCA and SVD

investigate prototypical problems of big data compression

Coding in DB Environment

Making Data Sets

R Programming

Introduction to R – I

Introduction to R – II

Common Data Structures in R

Conditional Operation and Loops

Looping in R using Apply Family Functions

Creating User Defined Functions in R

Graphics with R

Advanced Graphics with R

Text Analytics

Basics of text analysis processes

Annotators
analysis results
feature structure
type
type system
annotation
common analysis structure
Web crawling

Web crawling

Web Scraping from downloaded html files

Text classification

Singular Value decomposition concept

Latent Semantic Analysis

Document clustering

Topic Modeling

Class Assignments

Presentation

Statistics 101

Introduction to Statistics

Introduction to Statistics – II

Measures of Central Tendency, Spread and Shape – I

Measures of Central Tendency, Spread and Shape – II

Measures of Central Tendency, Spread and Shape – III

Python

Understanding Basics of Python

Control Structures and for loop

Playing with while loop | break and continue

Strings and files

List

Dictionary and Tuples

Statistics with R

Introduction to Data

Data Basics
Overview of data collection principles
Experiments - Principles of experiment design
Examining Numerical and Categorical data
Comparing numerical data across groups

Introduction to Probability

Introduction
Conditional probability
Bayes’ Rule

Distributions

Discrete Distributions
Continuous Distributions

Introduction to linear regression

Correlation
Line fitting
Fitted values
Residuals
Basic introduction to multiple regression

Foundations for inference and estimation

Variability in estimates
Sampling distribution
Confidence intervals
Margin of error and ascertaining a sample size

Foundations for inference and hypothesis testing

Nearly normal population with known SD
Hypothesis testing framework
Two Tailed and One Tailed tests
Testing hypothesis using confidence intervals and Critical Z values
One-sample means with the t distribution with unknown population SD
Inference for a single proportion
Decision errors (Type 1 and 2)
Hypothesis testing using p-values
Choosing a significance level
Power and the type 2 error rate

Linear Regression and Multiple Regression

Introduction to F-statistic
Hypothesis Tests
Intervals
Coefficient of Multiple Determination
Interpreting the model output

Data Mining 1 - Machine Learning with R & Python

Introduction to NumPy

Introduction to Pandas

Slicing Data

Exploratory Data Analysis

Exploratory Data Analysis (Continue)

Missing Value Imputation and Outlier Analysis

Linear Regression Motivation

Linear Regression optimization objective

Linear Regression in Python

Introduction to Regression Tree

Introduction to Classification Tree

Measures of Selecting the best Split

Cluster Analysis – Hierarchical Clustering & k-Means Clustering

Customer segmentation in Telecom Industry using Cluster Analysis

k-Means clustering

Association Rules mining

Market Basket Analysis

Advanced Statistics with R

Inference and hypothesis testing on single population

Analysis of difference in two populations

Analysis of Variance

Chi-Square Analysis

Analysis of data using Non-parametric Statistics

Linear regression analysis

Multiple regression analysis

Advanced Multiple regression analysis

Logistic Regression

Forecasting

Data Visualization with Tableau

Need for visualizing data

Same dataset, different interpretation
Read texts well, not numbers
Brain processes visuals by short circuiting brain’s pathways
Quicker conclusions | Speed

Research methodologies

Problem Formulation
Literature review
Methodology
Analysis
Finding and Interpretation
Suggestion
Conclusion
Bibliography

Importance of Big data visualization

Traditional Visualization
Big Data Visualization

Tableau product offerings

Installation of Tableau Public

Working with Tableau - Live Case study/Discussion

Creating interactive dashboards with Tableau Public

Case study discussion

HR – Case Study with Data

Story Boarding with Tableau Public

Case study discussion

Geomapping in Tableau

Create a geographic hierarchy
Build a basic map
Change from points to polygons
Add visual detail
Add labels
Customize your background map

Qlik view – Basics

Download QlikView Personal Edition and Install

Google charts – Basics

Creating a simple Google Chart with in data
Image generation, Line, bar, and pie charts.
Scatter plot
Google-o-meter
Map,Radar,Venn diagram
Specification of attributes

Dynamic charts with Google Docs

Using Google Docs as database to store graphical data
Specifying the range of data and selecting columns
Creating an interactive Google Chart with Google Docs data

Supplementary material & Case study discussion

Closing session & Queries

Web Analytics

Introduction to Digital Media Analytics

Introduction to Google Analytics

Concept of Account, Property and View

Concept of Sessions and Users

Concept of Dimension, Metric and Segment

Reading a Google Analytics Report

Audience Analytics

Acquisition Analytics

Behaviour Analytics

Real-Time Analytics

Setting Up and Analysing Events

Intelligent Events

Setting Up and Analysing Experiments

Setting Up and Measuring Conversion Goals

Attribution Modelling

Segment Reporting

Designing Custom Reports

Introduction to Google Adwords

Search Marketing

Display Marketing

Google Adwords Analytics

Managing a Google Analytics Account

RDBMS with SQL and DWH

Introduction to DBMS / RDBMS

Data Modelling

Physical Data Model

Getting Started with SQL Lite

DDL

DML

Introduction to Data Warehousing

Dimensional Modelling

Advanced SQL

Olap Cubes

Olap Cubes Practicals

Mentors

Has spent two decades in the corporate world in areas as diverse as Cryogenics, Steel, International Trade, Consulting and IT with organizations such as British Oxygen, Tata Steel, PwC and Compaq-HP. He has taught at the University of Iowa and has been a visiting faculty at IIM Lucknow, IIM Raipur, IIM Udaipur, IIM Shillong.

Charanpreet Singh

BTech (IIT Kanpur), MBA (University of Iowa), Co-Founder and Associate Dean - PRAXIS

He has worked as an online SAS trainer at Wireclass and provided online analytics training in U.S.A. and U.K. He worked as a Professor of Data Mining, Data Visualization and Advanced Analytics at iNurture Education Solutions Pvt. Ltd after which he has joined Praxis Business School as an Assistant Professor of Business Analytics.

Gourab Nath

MSC (WB State University)

Comes with an experience of 27 years delivering Management and IT consulting services in large organizations like (PwC) and CMC Ltd. He was a partner in the Management Consultancy Business at PwC and led large software projects in India and abroad.

Jaydeep Mukherjee

BTech (IIT Kharagpur), MTech (ISI Kolkata)

Suresh Krishnaswamy (popularly known as Sky) heads our Big Data & Business Analytics Center of Excellence. Sky has rich experience of over 26 years ranging from IT consulting, transition, transformation,applications development, to strategy making and business analyses with leading global IT and Consulting services companies in India and abroad.

Suresh Krishnaswamy

Head - COE - Big Data and Analytics

Has close to 2 decades of industry and 5 years of teaching experience. Has worked as Senior Scientist at TCS Innovation Labs after stints with Akamai Technologies, ONGC and Hindustan Motors. His areas of interest include Advanced Statistical Modeling, Data Mining, Graph Theory, Discrete Math and Internet Technologies.

Jaydip Sen

B.E (Jadavpur University), M.Tech(ISI, Kolkata); Pursuing PhD in Information Privacy in IoT

He is listed as one of the 10 prominent analytics academicians in India by Analytics India Magazine.Has spent nearly two decades in the IT, software and management consultancy business and has worked in Tata Steel, Tata IBM, PwC, where he was a partner and in IBM where he was the head of the Kolkata Delivery Centre.

Dr. Prithwis Mukerjee

B.Tech (IIT Kharagpur), M.S., Ph.D. (University of Texas at Dallas) Program Director - Business Analytics

Has been one of the most prominent market researchers of the country in the last decade and has worked in the top three multinational MR agencies of India - The Nielsen Company, TNS and IMRB International for 13 years in different functional and leadership roles. He was Associate Director at Nielsen and Group Business Director at IMRB International.

Prasenjit Das Purkayastha

M.Sc (J.U); PGDM (Goa Institute of Management)

Has worked in the industry for 4 years in Planning and Operations at HPCL prior to joining academics. He is actively involved in corporate training and has successfully completed training sessions on Data Mining processes in organizations like Abzooba, Infosys, TCG, ICICI Bank.

Subhasis Dasgupta

B. Tech (NIT, Surat); MBA (IBS); Research Scholar (IIM Ahmedabad)

Course Key Features

Training Methodology

This course involves 120 hours of Live Faculty session and recorded live session. It also has multiple guest lectures every month. Along with live classes, the course has industry catalyzers, opinion polls, observers and self-assessments.

Effective & Appealing Content

We offer a unique combination of research-led futuristic pedagogy and globally benchmarked content. Our modules are backed by extensive research which has made our education system both appropriate and exciting for our learners.

New Age Learning Platform

The i-meta platform of 361 DM, enabled with unique augmented and virtual reality-led classes allow mobile learning i.e., you can learn anytime and anywhere. You can also learn by downloading the 361DM App in your smartphone. Our platform facilitates group discussions, learning resources, performance records, post-class assessments, e-library, and opinion polls during classes.

Learn through Case Studies

Case studies help you to emphasize detailed contextual examination of a restricted number of events or circumstances and their relations. It helps in obtaining in-depth info about an individual, group, or occurrence.

Capstone Projects

During the course, the students pursue autonomous research on a question or problem of their choice, involve with the academic learning in the related disciplines, and - with the supervision of a faculty and industry mentor - produce a significant paper.

Placement Support

Our Placement Support Entity helps our students meet companies that work with Big Data and Analytics. Our students have been placed in some of the top companies like Siemens, American Megatrends, Dell and HCL to provide meaningful placement support.

Frequently Asked Questions

What is Business Analytics?

Business Analytics is a subset of Data Analytics. It is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to gain an insight on business operations to make better, fact-based decisions.

Simply put, Business Analytics is the art of analyzing business using analytics in order to fetch maximum profits at the most cost effective manner.

Why should I choose this program?

Choose this program to become one of the most sought after professional in the job market by learning Business Analytics today!

What makes this Program different?

This Business Analytics Certification Program gives equal importance to the technology side of Big Data, Data Science and the business side of Analytics, instead of focusing on one aspect alone: something that has not been done before. This Certification Program gives you an edge in a way that prepares you to thrive in the growing Analytics domain.

Is there a job market for Business Analytics Professionals?

The answer is yes. The job market is full of opportunities for Business Analytics Professionals as every major MNC today, be it a financial firm, or a consultancy, is on the lookout for Business Analytics Professionals.

Am I eligible for this course?

There are no eligibility criteria, but this program is ideal for any Non-IT graduate who wants to build a career in the thriving world of Business Analytics.

Post Graduate Program in Business Analytics