Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	UPES Dehradun

The Syllabus

Introduction to business analytics

What is analytics & why is it so important?
Applications of analytics
Different kinds of analytics
Various analytics tools
Analytics project methodology
Real world case study

Fundamentals of R

Installation of R & R Studio
Getting started with R
Basic & advanced data types in R
Variable operators in R
Working with R data frames
Reading and writing data files to R
R functions and loops
Special utility functions
Merging and sorting data
Case study on data management using R
Practice assignment

Data visualization in R

Need for data visualization
Components of data visualization
Utility and limitations
Introduction to grammar of graphics
Using the ggplot2 package in R to create visualizations

Data preparation and cleaning using R

Needs & methods of data preparation
Handling missing values
Outlier treatment
Transforming variables
Derived variables
Binning data
Modifying data with Base R
Data processing with dplyr package
Using SQL in R
Practice assignment

Understanding the data using univariate statistics in R

Summarizing data, measures of central tendency
Measures of variability, distributions
Using R to summarize data
Case study on univariate statistics using R
Practice assignment

Hypothesis testing and ANOVA in R to guide decision making

Introducing statistical inference
Estimators and confidence intervals
Central Limit theorem
Parametric and non-parametric statistical tests
Analysis of variance (ANOVA)
Conducting statistical tests
Practice assignment

Correlation and Linear regression

Correlation
Simple linear regression
Multiple linear regression
Model diagnostics and validation
Case study

Logistic regression

Moving from linear to logistic
Model assumptions and Odds ratio
Model assessment and gains table
ROC curve and KS statistic
Case Study

Techniques of customer segmentation

Need for segmentation
Criterion of segmentation
Types of distances
Hierarchical clustering
K-means clustering
Deciding number of clusters
Case study

Time series forecasting techniques

Need for forecasting
What are time series?
Smoothing techniques
Time series models
ARIMA

Decision trees & Random Forests

What are decision trees
Entropy and Gini impurity index
Decision tree algorithms
CART
Random Forest
Case Study

Boosting Machines

Concept of weak learners
Introduction to boosting algorithms
Adaptive Boosting
Extreme Gradient Boosting (XGBoost)
Case study

Cross Validation & Parameter Tuning

Model performance measure with cross validation
Parameter tuning with grid & randomised grid search

Introduction to Machine Learning in Python

What is machine learning & why is it so important?
Applications of machine learning across industries
Machine Learning methodology
Machine Learning Toolbox
Tool of choice- Python: what & why?
Course Components

Introduction to Python

Installation of Python framework and packages: Anaconda and pip
Writing/Running python programs using Spyder, Command Prompt
Working with Jupyter Notebooks
Creating Python variables: Numeric, string and logical operations
Basic Data containers: Lists, Dictionaries, Tuples & sets
Practice assignment

Iterative Operations & Functions in Python

Writing for loops in Python
List & Dictionary Comprehension
While loops and conditional blocks
List/Dictionary comprehensions with loops
Writing your own functions in Python
Writing your own classes and functions as class objects
Practice assignment

Data Summary; Numerical and Visual in Python

Need for data summary
Summarising numeric data in pandas
Summarising categorical data
Group wise summary of mixed data
Need for visual summary
Introduction to ggplot & Seaborn
Visual summary of different data combinations
Practice Exercise

Data Handling in Python using NumPy & Pandas

Introduction to NumPy arrays, functions &properties
Introduction to pandas
Dataframe functions and properties
Reading and writing external data
Manipulating Data Columns

Basics of Machine Learning

Business Problems to Data Problems
Broad Categories of Business Problems
Supervised and Unsupervised Machine Learning Algorithm
Drivers of ML algorithms
Cost Functions
Brief introduction to Gradient Descent
Importance of Model Validation
Methods of Model Validation
Introduction to Cross Validation and Average Error

Generalised Linear Models in Python

Linear Regression
Limitation of simple linear models and need of regularisation
Ridge and Lasso Regression (L1 & L2 Penalties)
Introduction to Classification with Logistic Regression
Methods of threshold determination and performance measures for classification score models
Case Studies

Tree Models using Python

Introduction to decision trees
Tuning tree size with cross validation
Introduction to bagging algorithm
Random Forests
Grid search and randomized grid search
ExtraTrees (Extremely Randomised Trees)
Partial Dependence Plots
Case Studies
Home exercises

Boosting Algorithms using Python

Concept of weak learners
Introduction to boosting algorithms
Adaptive Boosting
Extreme Gradient Boosting (XGBoost)
Case study
Home exercise

Support Vector Machines (SVM) and KNN in Python

Introduction to idea of observation based learning
Distances and Similarities
K Nearest Neighbours (KNN) for classification
Introduction to SVM for classification
Regression with KNN and SVM
Case study
Home exercises

Unsupervised learning in Python

Need for dimensionality reduction
Introduction to Principal Component Analysis (PCA)
Difference between PCAs and Latent Factors
Introduction to Factor Analysis
Patterns in the data in absence of a target
Segmentation with Hierarchical Clustering and K-means
Measure of goodness of clusters
Limitations of K-means
Introduction to density based clustering (DBSCAN)

Neural Networks

Introduction to Neural Networks
Single layer neural network
Multiple layer Neural network
Back propagation Algorithm
Moment up and decaying learning rate in context of gradient descent
Neural Networks implementation in Python

Text Mining in Python

Quick Recap of string data functions
Gathering text data using web scraping with urllib
Processing raw web data with BeautifulSoup
Interacting with Google search using urllib with custom user agent
Collecting twitter data with Twitter API
Introduction to Naive Bayes
Feature Engineering for text Data
Feature creation with TFIDF for text data
Case Studies

Ensemble Methods in Machine Learning

Making use of multiple ML models taken together
Simple Majority vote and weighted majority vote
Blending
Stacking
Case Study

Bokeh

Introduction to Bokeh charts and plotting

Version Control using Git and Interactive Data Products

Need and Importance of Version Control
Setting up git and github accounts on local machine
Creating and uploading GitHub Repos
Push and pull requests with GitHub App
Merging and forking projects
Examples of static and interactive data products

Banking – Finance
E-commerce
Power
Logistics and Supply Chain
Aviation

Introduction To SQL

What is SQL?
Why SQL?
What are relational databases?
SQL command group
MS SQL Server installation
Exercises

SQL Data Types & Operators

SQL Data Types
Filtering Data
Arithmetic Operators
Comparison operators
Logical Operators
Exercises

Useful Operations in SQL

Distinct Operation
Top N Operation
Sorting results
Combine results using Union
Null comparison
Alias

Aggregating Data in SQL

Aggregate functions
Group By clause
Having clause
Over clause
Exercises

Writing Sub-Queries in SQL

What are sub-queries?
Sub-query rules
Writing sub-queries
Exercises

Common function in SQL

Ranking functions
Date & time functions
Logical functions
String functions
Conversion functions
Mathematical functions
Exercises

Analytic Functions in SQL

What are analytic functions?
Various analytic functions
SQL syntax for analytic functions
Exercises

Writing DML Statements

What are DML Statements?
Insert statement
Update statement
Delete statement

Writing DDL Statements

What are DDL Statements?
Create statement
Alter statement
Drop statement
Exercises

Using Constraints in SQL

What are constraints?
Not Null Constraint
Unique constraint
Primary key constraint
Foreign key constraint
Check constraint
Default Constraint
Exercises

SQL Joins

What are joins?
Cartesian Join
Inner Join
Left & Right Join
Full Join
Self Join

Views in SQL

What are views?
Create View
Drop view
Update view

Student Reviews for PG Program in Data Science

4.2/5(2 Reviews)

College Infrastructure: 5/5

Academics: 5/5

Placements: 3.5/5

Value for Money: 3/5

Campus Life: 4.5/5

Good

Verified Review

Posted on 4 Dec'25 by Anonymous

This user chose to post anonymously

Batch of 2028-01-01 | PG Program in Data Science

College Infrastructure

Yes, the college have good infrastructure with spacious classroom, functional labs, a well stocked library and separate hostel with proper dining facilities. The has sports ground, medical support and common area for student hangout. Overall facilities are satisfactory and support academics well, though wi-fi connectivity and maintenance in a few hostel could be better.

Great experience

Verified Review

Posted on 31 Jan'24 by Anonymous

This user chose to post anonymously

Batch of 2023-01-01 | PG Program in Data Science

College Infrastructure

Though the facility and infrastructure were decent enough, the campus is one of a kind. But the Wi-Fi hardly works in any part of the campus except for the library, which works too rarely. The quality of the facilities in the hostel was adequate, and the food was decent too. Sports and games facilities were also available.

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