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Post Graduate Program in Business Analytics

Evolved and designed by veterans in the Analytics industry, this program prepares students and working professionals to establish a hi-flying globe-trotting career in the growing Data and Analytics domain.

  • Duration6-12 months

  • EligibilityBBA, B.Com, B.Sc (Stat), MBA, M.Sc(Stat)

  • FEESRs.67,800/-

  • Learning ModeFaculty-led Online

Course Highlights

  • Big Data Hadoop Training with Certification

    Real-time Internship

    An internship allows you to apply classroom knowledge in real life situations. We help you find relevant work experience opportunities in organizations involved in Big Data Analytics.

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    Placement Support

    Our Placement Division helps our students meet companies that practice Big Data Analytics. Our Placement Division has positioned students in some of the top companies like Siemens, American Megatrends, Dell and HCL.

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    Training Methodology

    This course involves 180 hours of Live Faculty session and 132 hours 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.

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    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.

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    Industry Connect

    We connect you with industry leaders who play a major role in the Data and Analytics Domain. Connecting with such people can help you in job hunting, career networking and professional progress.

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    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.

Key Features of the Program

  • 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.

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  • Highly Experienced Faculty for Big Data

    At 361 DM, we have a group of highly qualified faculty from IIT Kanpur and IIT Kharagpur. Our faculties have worked with PwC and IBM and have years of experience in pioneering several Big Data and Analytics projects globally.

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  • Certificate from Praxis Business School

    PPraxis Business School, Kolkata, is a premier B-School whose courses are rated among India’s top two in Big Data and Analytics domain. It is one of the most trusted and influential management education institutions in India.

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  • Education Loan

    Good Education ensures good future. We ensure your financial needs are taken care of as you move ahead in getting educated to build an awesome career. You can always apply for an Education Loan to join select courses.

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  • New Age Learning Platform

    The i-meta platform of 361 DM, enabled with unique augmented and virtual reality-led classes allows you to learn anytime and anywhere. Our platform facilitates group discussions, learning resources, performance records, post-class assessments, e-library, and opinion polls during classes.

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  • Dedicated Support Team

    We have a dedicated team to support you throughout the course – from the application to the certification! You can reach us whenever there is any need for assistance, just at the click of a button.

    MBA E Business Syllabus Annamalai University

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

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

Introduction to R, R Studio and Basic R Operations

Import, Export and Data Manipulation with R

Concept of data structure in R

Concept of Index in R

Creating Boolean index in R based on conditions

Hands on exercise

Understanding Loops in R

Building User functions in R

Basic R Graphics

Understanding data clustering concepts and techniques

Running clustering in R with Silhouette distance measure for cluster validity

Understanding data classification concepts and techniques using decision trees

Model building concepts and techniques

Concepts of Association Rule Mining

Building association rules and interpretation

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

Outcome of the Program

Post Graduate Program in Business Analytics is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity.

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About the Praxis

Praxis Business School, Kolkata, is a premier B-School whose courses are rated among India’s top two in Big Data and Analytics domain. It is one of the most trusted and influential management education institutions in India. Praxis Business School is motivated by the desire to generate business professionals who can partake in and add to the economic development of the country.

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