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PG Program in Data Science
Online
79,000 INR 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)
Good
Verified ReviewPosted on 4 Dec'25 by Anonymous
Batch of 2028-01-01 | PG Program in Data Science
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 ReviewPosted on 31 Jan'24 by Anonymous
Batch of 2023-01-01 | PG Program in Data Science
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.