Last Enrollment Date : 30th September'21

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  • Post Graduate Program in Business Analytics
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    E 5

    INR 67,800

    No Cost EMI of Rs. 5635 per month learn more

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    Program Summary

    • Credits 30
    • Duration 1 Year
    • projects/Assignments 95 Hours
    • online sessions 212 Hours

    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.

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    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


    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


    Understanding Basics of Python

    Control Structures and for loop

    Playing with while loop | break and continue

    Strings and files


    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


    • 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


    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



    Introduction to Data Warehousing

    Dimensional Modelling

    Advanced SQL

    Olap Cubes

    Olap Cubes Practicals

    Artificial Intelligence & Deep Learning - Industry Practices

    Industry Connect - See what experts say about this program

    • G Infotech

      We endorse this program. Thoughtfully designed and delivered. We are happy to screen and recruit candidates who meets our The programs are thoughtfully designed. We will be happy to look for candidates from 361 Degree Minds for our manpower requirements.

      -Praveen, Director, G Infotech

      Aaum Analytics

      This product is endorsed by Aaum Analytics.A company specialised in analytics with strong focus on research and technology

      Anthro Labs

      Being a Business Anthropologist, I largely work on Data and its Science for areas like HR Analytics, Market Research Analytics, Culture Research & Consulting. I find the programmes very diligently designed to meet the industry needs and for consultants like us. Great work by the team.

      -Suresh G V, Founder & CEO, Anthro Labs


      As an Industry person with over 20 years of experience,I have witnessed multiple training programmes and training providers.This program of 361DM stands out from all of them for the expertise of professionals delivering, the quality of the content and the engaging model of the platform.Truly Enriching!

      - VijayKumar, Senior Manager, CapGemini

      Infodrive Analytics

      very good on trainings

      -G.Karpagavalli, Sr Manager - HR

    About 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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    Course Highlights

    Key Features of the Program

    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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