PG Program in Data Science and Business Analytics

Certificate from The University of Texas at Austin

PG Program in Data Science and Business Analytics

Online | 7 Months
Apply Now
Application Deadline - 12th Dec 2019
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#2nd worldwide

Business Analytics Rankings, 2018

Why Join PGP-DSBA?

Great Learning gl certificate

Certificate from UT Austin

Ranked #2 worldwide in Business Analytics

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Rated 4.5+/5

60% career transition within 6 months of program completion

Great Learning job opportunity

Unique Mentored Learning

Customized learning in small learning groups

Data Science and Analytics are impacting businesses worldwide and companies are on the lookout for professionals who are business ready in analytics.

The PGP-DSBA program combines the McCombs School of Business’ proven academic leadership in the field of business analytics with Great Learning’s unique mentored learning model that combines a live interactive online classroom experience, program support and career coaching to ensure successful learning outcomes. The program provides an unmatched opportunity for individuals to begin or shift their career into the exhilarating field of business analytics and data science.

PGP-DSBA is a specially designed variant for global markets of the PGP-BABI Program, India's number 1 analytics Program for the last 5 years.

Upon successful completion of the course, participants will receive a verified digital certificate from the McCombs School of Business at The University of Texas at Austin that is ranked #2 in the world for Business Analytics by the QS World University Rankings 2018.

Read more about the program on The University of Texas at Austin’s website: Visit UT Austin Website

2nd Worldwide

Business Analytics Rankings 2018

Certificate from The University of Texas

All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University.

KUMAR MUTHURAMAN

Faculty Director, PGP-DSBA

H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics

MS & PhD: Stanford University

Watch the video to know more about the program

Unique Mentored Learning

Personalized Attention:
Mentoring in Small Groups

Self-study online is difficult.
Our unique Mentored Learning model supports you at every step

  • 2 hours of live mentoring sessions every weekend
  • Mentors are industry experts who bring real-world insights
  • Mentors are matched to your domain and experience level
  • A passionate mentor can motivate you to learn!
Great Learning Mentoring

Mentoring is interactive and happens in small groups

Our Mentors work at the best companies

Program Structure

A 7-month structured online program with hands-on projects and weekend mentorship sessions

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Online content

Best-in-class recorded content from expert faculty and industry mentors

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Hands-on projects

Practical assignments, case studies, and instructor-led practice sessions on data sets

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Weekend mentoring sessions

48 hours of personalised mentorship from analytics professionals working in leading companies

Get a Certificate from The University of Texas at Austin

Online Content | Weekly Mentorship

Download Brochure

PGP-DSBA Learning Experience

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Personalized learning

Each module has recorded content that is followed by a session with one of our distinguished industry mentors, in a small group of participants. These mentors are thought leaders in different domains with several years of industry experience that enables them to impart practical knowledge and real-world insights.

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Industry exposure sessions

Each week, participants get access to industry videos and webinars conducted by industry experts, in addition to the usual mentoring sessions. These resources provide insights into current industry trends & real-life business problems.

Great Learning capstone project
Experiential learning projects

An experiential learning project at the end of every module helps candidates internalize their understanding of the content consumed. Our coursework and practical assignments are designed to enable candidates to apply what they have learned during self-study and industry sessions.

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Career enhancement sessions

The program includes career development sessions which help candidates identify their strengths and empower them to clear analytics interviews. Interacting with industry practitioners provides exposure and experience that helps them in transitioning to business analytics roles.

ePortfolio

As you complete your experential projects we will automatically create a document to showcase your learning & projects in a snapshot that we call an ePortfolio. This can also be easily shared on social media channels to establish your credibility in Business Analytics with potential employers.

Curriculum

Introduction to Analytics
Welcome and Orientation
  • Welcome to the Program
  • Orientation ( Structure of the Program)
  • Analytics Landscape
  • Industry Session
  • Assessment 1
Linear Programming with Excel
  • Introduction to Linear Programming
  • Sensitivity Analysis
  • Mentoring Session 1
  • Assessment 2
Data Scientist Toolbox - R
  • Introduction to R
  • Getting and Cleaning Data using R
  • Mentoring Session 2
  • Self Study
Playing with Data
  • Project Brief (Twitter Data)
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Fundamentals of Business Statistics
Descriptive Statistics
  • Overview of the Course and Problem 2 - Brief (Continue with Twitter Data)
  • Presentation of Data
  • Measures of Central Tendency and Variation
  • Correlation
  • Industry Session
  • Self Study
Inferential Statistics
  • Basic Probability Concepts
  • Probability Distribution
  • Mentoring Session 1
  • Self Study
Estimation and Hypothesis Testing
  • Estimation
  • Hypothesis Testing
  • Mentoring Session 2
  • Self Study
Playing with Data
  • Project Brief - Healthcare Problem
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Advanced Statistics
Regression
  • Overview of the Course and Problem 3 - Brief (Continue Healthcare Data)
  • Introduction to Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Industry Session
  • Self Study
Anova
  • Purpose of ANOVA
  • Assumptions of ANOVA
  • One way ANOVA
  • Two way ANOVA
  • Mentoring Session 1
  • Self Study
Factor Analysis and Principal Component Analysis
  • Factor Analysis
  • Principal Component Method
  • Dimension Reduction Problems
  • How to Select an Analysis Method?
  • Mentoring Session 2
  • Self Study
Playing with Data
  • Project Brief - Marketing Problem
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Machine Learning
Data Mining Using Decision Tree
  • Overview of the Course and Problem 4 - Brief (Continue with Marketing Problem )
  • Unsupervised Learning: Clustering
  • Unsupervised Association Rules
  • Industry Session
  • Self Study
Classification Algorithm - Decision Trees
  • Decision Tree
  • CART
  • CHAID
  • Random Forest
  • Mentoring Session 1
  • Self Study
Classification Algorithm - Discriminant Analysis
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • SVM
  • K nodes Classification
  • Mentoring Session 2
  • Self Study
Playing with Data
  • Project Brief - Finance Problem
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Predictive Modelling
Neural Networks
  • Overview of the Course and Problem Brief 5 (Finance Problem)
  • Introduction to NN
  • Basic Structure
  • Application of NN
  • Industry Session
  • Self Study
Linear and Non-Linear Regression
  • Predictive Continuous Response
  • Non-Linear Regression 1
  • Non-Linear Regression 2
  • Non-Linear Regression 3
  • Mentoring Session 1
  • Self Study
Model Comparison and Further Improvement
  • Machine Learning Techniques
  • GBM
  • Model Validation
  • Model Comparison and Further Improvement
  • Mentoring Session 2
  • Self Study
Playing with Data
  • Project Brief - Supply Chain Problem
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Data Visualization in Tableau
Essential design principle for Tableau
  • Getting started with Effective and Ineffective visual
  • Design best practices and exploratory analysis
Creating visualization with Tableau
  • Getting started and charting
  • Mapping
  • Mentoring Session 1
Telling stories with Tableau
  • Key metrics indicator and decision triggers
  • Dashboard and storytelling with data
  • Mentoring Session 2
Project Dashboarding Syndicate
  • Project Brief
  • Project Submission
  • Project Debrief
  • Mentoring Session 3
Time Series Forecasting (Self-paced)
Time Series Analysis
  • Overview of the course and problem brief 6 (Continue supply chain)
  • Time series analysis (Components of time series)
  • Holt-Winters Model
  • Industry session (self study)
ETS Models
  • Exponential Smoothing Techniques
  • Exponential Moving Average
  • Forecasting (Construction an ETS model)
  • Mentoring session - 1 (Self study)
Advanced Techniques
  • Stationarity
  • Estimating ARIMA
  • SVM (Structured Break Collinearity
  • Mentoring session - 2 (Self study)
Playing with data
  • Project brief - Macro problem
  • Project submission
  • Project debrief
  • Mentoring session - 3
Project
  • Project Brief
  • Project Debrief
Introduction to Big Data (Self-paced)
Big Data: Why and where
  • Big Data era
  • Applications: What make Big Data valuable
Getting started with Hadoop
  • Introduction to Hadoop ecosystem
  • Map reduce
Getting started with R Spark
  • Programming with Spark
Project
  • Project Brief
  • Project Debrief
Python for Data Science (Self-paced)
Introduction
Numpy Library and Arays
Pandas Library and Dataframes
Statistics Using Python
Data Visualization
Project
Business Foundations (Self-paced)
Marketing & CRM
  • Core concepts of marketing
  • Customer Life Time Value
  • Marketing metrics for CRM
Business Finance
  • Fundamentals of Finance
  • Working Capital Management
  • Capital Budgeting
  • Capital Structure
Project
  • Project Brief
  • Project Debrief
Domain Exposure (Self-paced)
Marketing and Retail Analytics
  • Marketing and Retail Terminologies: Review
  • CustomerAnalytics
  • KNIME
  • Retail Dashboards
  • Customer Churn
  • Association Rules Mining
Web and Social Media Analytics
  • WebAnalytics: Understanding the metrics
  • Basic & Advanced Web Metrics
  • Google Analytics: Demo & Hands on
  • CampaignAnalytics
  • Text Mining
Finance and Risk Analytics
  • Why Credit Risk-Using a market case study
  • Comparison of Credit Risk Models
  • Overview of Probability of Default (PD) Modeling
  • PD Models, types of models, steps to make a good model
  • Market Risk
  • Value at Risk- using stock case study
  • Fraud Detection
Supply chain and Logistics Analytics
  • Introduction to Supply Chain
  • Demand uncertainty
  • Inventory Control & Management
  • Inventory classification Methods
  • Inventory Modeling (Reorder point, Safety stock)
  • Advanced Forecasting Methods
  • Procurement analytics
Project
  • Project Brief
  • Project Debrief
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Capstone - The Cornerstone

The Capstone Project is an application-oriented industry project where candidates are mentored and evaluated by Great Learning faculty and Industry Experts. It allows them to apply their learning to real life projects and add it to their portfolio as a tangible 'body of work' for potential employers to see. It is a growth enabler that instills both confidence and conviction in our candidates.

Hackathons

Participate in company sponsored hackathons and establish your expertise. Apply your new skills to solve real-world business problems. Here are some of our recent hackathons
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Hackathon: 20th Jan 2019

Real Estate

Predict the monetary value of a house using machine learning based on features of the house

40 Teams
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Hackathon: 16th Dec 2018

Inventory Management

Predict the total number of customers visiting each store of a pharmacy chain to plan inventory effectively

38 Teams
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Hackathon: 28th Oct 2018

Airline Industry

Predict the factors that will contribute to a passenger's positive in-flight experience.

27 Teams

Hands-on Projects

Work on real-world analytics projects and showcase them to potential employers through the ePortfolio

Meet Your Faculty

Meet Some of our Mentors

Learner Profiles

10000+ Learners from 50 countries across 5 continents

Industry Diversity

Work Experience Distribution

PGP-DSBA alumni work for world-class companies

Admission Details

Eligibility

  • done Bachelor's degree with a minimum of 50% aggregate marks or equivalent. Final year students can also apply to the program.
  • done Preference will be given to candidates with Engineering, Mathematics, Statistics, and Economics background.
Grear Learning admission and selection process

Selection Process

Every year, thousands of professionals apply to the PGP-DSBA program. We believe it is our responsibility to give enough time and attention to every candidature, even if it means going through hundreds of profiles to select one professional that could benefit from the program. So here is a glimpse into our rigorous selection process:
Step 1

Application Form

Register by filling up the online application form >

Step 2

Call from your Program Advisor

A program advisor is assigned to assist you through the selection process and answer all your questions.

Step 3

Shortlisting and Panel Review

A panel will review your application to determine your fit for the program. The panel will evaluate you on your work experience, past academic credentials and motivation for joining the program.

Step 4

Interview/Screening

If shortlisted, you will go through a telephonic interview (this may be waived for candidates with strong profiles and experience).

Step 5

Admissions Offer

After a final admissions committee and faculty review, if selected, you will receive an ‘offer of admission’ letter for a seat in the upcoming cohort of the program.

Fee Details

5,000 USD

Reach out to a Program Advisor for flexible payment options

Payments

Candidates can pay the program fee through

account_balance
Bank Transfer
credit_card
Credit/Debit Cards

Fee Includes

Great Learning tution fee

Tution Fee

Great Learning material

Learning Material

Great Learning mentorship session

Mentorship Sessions

Upcoming Application Deadline

12th Dec 2019

We follow a rolling admission process and admissions are closed once the requisite number of participants enroll for the upcoming cohort. So, we encourage you to apply early to secure your seat.

Apply Now

Frequently Asked Questions

What is the ranking of UT Austin analytics programs?
The McCombs School of Business at The University of Texas at Austin is ranked No. 2 in the world by QS World University Business Analytics Rankings (2018)
What is the role of The University of Texas at Austin - McCombs School of Business in the PGP-DSBA?
The PGP-DSBA curriculum has been designed in collaboration with UT Austin. The teaching and content in the program is by faculty from UT Austin, Great Learning and other practicing data scientists and analytics experts. The capstone projects are approved by UT Austin Faculty.

Upon completion, all successful participants get certificates from The University of Texas at Austin Executive Education.
What is unique about the PGP-DSBA Program?
The PGP-DSBA program is unique in the following aspects:
  • Personalised mentoring and industry interaction sessions every month
  • Learning happens in micro-classes of 10 learners
  • Covers industry-relevant topics in statistics and analytics in depth
  • Provides hands-on exposure to tools such as R, Python, Tableau and Advanced Excel. Datasets are also provided
  • Experiential learning projects at the end of every module enable the candidate to apply their learning to real-world business problems
  • Interactive live sessions with industry experts and mentors provide current industry knowledge and insights
  • The online delivery model makes it convenient for working professionals to pace their learning and get doubts cleared without having to quit their jobs or travel anywhere
What is meant by mentored learning?
Participants are guided through a unique mentored learning process which happens in a micro class. These micro classes take place in a group of 10 learners which is guided by a senior industry mentor. These classes are live classes where you will have a video interaction with your mentor and other 10 learners on 3 weekends.
What are the eligibility criteria for PGP-DSBA?
PGP-DSBA is mostly pursued by working professionals planning to make a career transition into analytics roles. We also have students in their final year of graduation benefiting from the program. Graduation in a quantitative discipline like engineering, mathematics, sciences, statistics, economics, etc, would help participants get the most out of PGP-DSBA program.
How will PGP-DSBA help me progress in my career?

The primary objective of the program is to help you prepare for a career in the domain. Understanding the importance of gaining credibility, knowledge, and a body of work in landing you a job, we have worked backwards to design a program that helps you stand out on all 4 fronts. 

  • The certificate from UT Austin serves to give you credibility and recognition in the global industry.
  • Best-in-class recorded content from UT Austin faculty and hands-on practical training equip you with the knowledge to succeed.
  • The projects you complete add on to your body of work to prepare an industry-ready portfolio by the end of the program.
  • Interacting with established practitioners and other aspiring data science professionals helps you build your network.

In addition, the program provides career guidance sessions with industry practitioners and mentoring/support on the softer aspects of job hunting such as resume review, LinkedIn profile review, interview preparation, etc.

Is PGP-DSBA a completely online program?
Yes. The program is covered using recorded content delivered by academic and industry faculty and live instructor-led online micro-classes which happens in a batch of 10 learners. All assessments will also be conducted online.
Will the program certificate be awarded by The University of Texas at Austin?
Yes. Upon completion, all successful participants get Certificates from The University of Texas at Austin Executive Education.
Would I have to spend extra on books, online learning material or license fee?
All the requisite learning material is provided online to candidates through the Learning Management System
Would the content be available to me after completion of the course?
We believe that learning should be continuous and hence, all the learning material in terms of lectures and reading content would be available to the candidates on the LMS even after the completion of the course.
How will I be evaluated during the program?
PGP-DSBA is a holistic and rigorous program and follows a continuous evaluation scheme. Quizzes, assignments, and experiential learning projects help us evaluate a candidate's understanding of the concepts learned.
Can my company sponsor me?
We accept corporate sponsorships and can assist you with the process. For more information, you can write to us at dsba.utaustin@greatlearning.in
How can I apply for the program?
You can apply through the online application form. If you need assistance from our team, write to us at dsba.utaustin@greatlearning.in and we shall guide you through the process.
Are there any experiential projects as part of PGP-DSBA?
Participants do several experiential projects on Time series forecasting, Predictive modeling, Advanced statistics, Estimation & Hypothesis testing, and Data mining and a Capstone project at the end which requires candidates to use concepts learnt across all the different modules in one project.
What is the admission process?
You will need to fill up a simple online application form. The admissions committee will review all the applications and shortlist candidates based on their profiles.
Which companies do PGP-DSBA industry mentors work for?
Our industry mentors work with some of the leading organizations in the world like Microsoft, Google, McKinsey, Boeing, HSBC, Citi Group, etc.
What are the tools covered in the program?
What is the refund policy for the program?
Fee once received will not be refunded.