Master Program in Marketing Management

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Marketing Management
Data Analytics across Domains What is Analytics? Types of Analytics AI vs ML vs DL vs DS
Introduction to statistics and Central Limit Theorem Measures of Central Tendancies and Measures of Spread Descriptive Statistics with Real Time Examples Measuring Scales Inferential Statistics with Real Time Examples
Introduction to statistics and Central Limit Theorem Measures of Central Tendancies and Measures of Spread Descriptive Statistics with Real Time Examples Measuring Scales Inferential Statistics with Real Time Examples

Python Intro,IDE and Python Packages
Python Programming
Python Data Types – Dictionary, List and Set
Numpy Packages – Array Handling and Manupulation
Pandas Packages – Dataframe and Loading Excel, CSV File
Matplotlib Packages – Line graph and Visualisation
Histogram, Scatter Diagram, Box Plot and Bar Graph
Area Chart, Dual Axis, Array reshaping, reverse matrix analysis
Python – Operators and String Manupulation Control Structures(IF,IF-ELSE,IF-ELIF-ELSE,WHILE & FOR LOOP)
Python – Data Preparation Process
Python – Functions WITH and WITHOUT arguments
Python – File Processing and Data Collection Methods
Python – Time Series Analysis and Forcasting
Python – Simple Predictive Analysis

Data Science with Python
Data Science Application across Multiple Domain and Business Function
Data Science Project LifeCycle
Multiple Predictive Model using Python
Python – Simple and Multiple Predictive Model in Practical
Python Correlation Analysis
Python Classication Model Buildin
Data Science – Experimental Design Analysis
Classication Technique – Discriminant Analyssi
Data Science – Association Rule – Apriori Algorithm
Data Science – Building Recommendation System – (Market Basket Analysis) Data Architecture Design, Data Warehousing and it’s Schema Design Image Processing and Image Extraction Image Processing and Object Recognition Summarisation of Data Science Algorithm (Data Science Process)

Digital Marketing

Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning

Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building

Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classication, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling

ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA

Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis

Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes

Distance as Classier, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classication

Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear

Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling

Project Management

Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA

Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method

Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule

Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation

Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models

Agile Scrum Master

Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau

Data Analytics

What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.

Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions

Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.

R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data

Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages

Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.

Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression

Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example

Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering

Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis

Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes

K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN

What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree

Ensample of Decision Tree.

Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM

RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics


Our Industry mentor and a dedicated placement team will guide you with :






On completion of the Post Graduate Program in Data Science, aspirants will receive an Industry-endorsed Certificate along with Internship Certificate.








Program Fees

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INR 65,000

INR 45,000

*18% GST Extra


INR 85,000

INR 65,000

*18% GST Extra


  • Introduction to Linux
  • Root
  • Basic commands
  • Editors
  • OS installation
  • Basic system configuration and administration
  • Understanding files and directories
  • Schedulers
  • User administration
  • Software installation