Post Graduate Program in Data Science
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About the course
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
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
Hypothesis Testing and Goodness of Fit test | Introduction to Statistical Tests | Statistical Test with Real Time Example | Analysis of Variance(ANOVA) & Analysis of Covariance (ANCOVA) | Probability Theory for Data Analytics | Types of Probability Distribution
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 Classifcation Model Building
Data Science – Experimental Design Analysis | Classifcation Technique – Discriminant Analysis | 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)
Machine Learning Introduction
Linear Regression | Logistics Regression | ANOVA and ANCOVA | Linear Discriminant Analysis
Naïve Bayes | K-Nearest Neighbour | SVM- Support Vector Machine | -Decision Tree and Random Forest
Factor Analysis | Cluster Analysis | Association Rule | Correlation
Time Series Analysis
Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Building Artificial Neural Networks (ANN)
intro to CNN
Introduction to RNN & Sequence prediction using RNN
Introduction to LSTM,
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data
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
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
PROGRAMMING LANGUAGES AND TOOLS
On completion of the Post Graduate Program in Data Science, aspirants will receive an Industry-endorsed Certificate along with additional certificates.
PROGRAM FACULTY & TRAINERS
B.Tech and MBA ( Finance & Operation ), Ph.D in Data Analysis.
M.Tech (power electronics), MBA-HR, PhD in power quality improvement
M.B.A(Marketing & Finance )
What Can I Become?
LUMPSUM Fees - Rs. 25,000 + GST 18% applicable
INSTALLMENT Fees - Rs. 30,000 + GST 18% applicable
|Regsitration Amount||EMI 1||EMI 2||Exam Fees + GST 18%|
|10,000||10,000||10,000||6,000 + 5,4000|
- Exam Fees of 6,000/- applicable for complete course.
- EMI has to be paid every month. If not paid, fine of 500/- Rs. is applicable.