4.71 ( 10718 Learner Ratings )
Applications close on
20th Jun 2025
4.71
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With On-Campus Immersion in Decision Science and AI (Optional)
4.71 ( 10718 Ratings )
Application closes 20th Jun 2025
Curriculum with cutting edge tools and skills
Interactive mentor-led sessions
Personalized projects as per your industry
Learn from top UT Austin faculty
40+ Case Studies
24*7 Dedicated Program Support
QS World University Rankings (2024)
Financial Times, 2022
The Post Graduate Program in Data Science and Business Analytics is designed for professionals who want to transition their careers into data science and for aspiring data scientists. Unlike any other program, this online data science certificate program offers
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In the 3-day immersive on-campus program you can:
with like-minded AI professionals
in On-Campus Learning for 3 Days
Earn 1.9 on successful completion of the program
Intelligent Decision Science Systems
Dive deeper into the world of Artificial Intelligence and unlock advanced skills in deep learning.
Understand neural networks.
Master CNNs for image classification.
Learn NLP and transformers.
Earn PGP-AIML and PGP-DSBA certificates.
Elevate your career with our comprehensive data science and business analytics course, tailored to nurture the modern business analyst. The curriculum has been designed by the renowned faculty at the McCombs School of Business at The University of Texas at Austin. This sought-after course in business analytics encompasses modules such as Data Science Foundations and Techniques, offering deep domain exposure and empowering learners with visualization and insights tools.
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Data Science and Business Analytics Foundations
The Foundations module is designed to equip you with essential statistics, Python, and business domain skills to establish the groundwork for the remainder of the course. It serves as an introduction to Data Science, and completing this course will give you the confidence to discuss related concepts.
Pre-work
This covers the prerequisites needed to begin the online Data Science and Business Analytics program and includes the basics of programming with Python.
Module 1: Python Foundations
Embark on a data-driven journey with our Python Foundations Module. Learn to read, manipulate, and visualize data using popular Python packages, enabling you to tell compelling stories, solve business problems, and deliver actionable insights with ease.
Grasp the simplicity and readability of Python's syntax as you explore variables, data structures, conditional and looping statements, and functions. Build a robust skill set in Python essentials for effective coding and data organization.
Explore crucial tools in Data Science—NumPy and Pandas. NumPy excels in mathematical computing with arrays and matrices, while Pandas, an open-source library, provides speed and flexibility for data manipulation and analysis. This module deep-dives into these essential libraries, equipping you to adeptly read, manipulate, and derive insights from data in the realm of Data Science.
This module focuses on Matplotlib and Seaborn. Matplotlib, a dynamic library, enables static and animated visualizations, while Seaborn, built on Matplotlib, enhances data visualization in Python. This module provides an in-depth exploration of these tools, empowering you to create impactful visualizations that effectively summarize and communicate insights from diverse datasets.
Explore the depths of Exploratory Data Analysis (EDA), unraveling data patterns and extracting meaningful insights using Python. Acquire the skills to inform strategic business decisions based on the comprehensive analysis of data.
Module 2: Business Statistics
Elevate your analytical skills with the Business Statistics module. Harness the power of Python to assess the reliability of business estimates through confidence intervals and hypothesis testing. Make informed decisions by analyzing data distributions, ensuring precision in resource allocation and strategic commitments.
Delve into the core of statistical analysis. Gain a comprehensive understanding of probability distributions, essential for making statistically-sound, data-driven decisions. Master the fundamentals to draw conclusions about populations based on samples.
Uncover the intricacies of estimation, determining population parameters from sample data, and master the art of hypothesis testing—a framework for drawing meaningful conclusions. Delve into essential concepts like the Central Limit Theorem and Estimation Theory, providing a solid foundation for robust statistical analysis in decision-making.
Gain proficiency in hypothesis tests, essential for validating claims about population parameters in Data Science. This module introduces the most commonly used statistical tests, equipping you to choose the right test for business claims based on contextual nuances. Explore practical implementations in Python through real-world business examples, ensuring a comprehensive understanding of statistical testing in the Data Science realm.
Techniques
Module 3: Supervised Learning - Foundations
Uncover the power of linear models in deciphering relationships between variables and continuous outcomes. Validate models, draw statistical inferences, and gain invaluable business insights into the key factors shaping decision-making.
Gain insights into Machine Learning, a subset of Artificial Intelligence, dedicated to pattern recognition and predictive analysis without explicit programming. This module specifically delves into the fundamentals of learning from data, the mechanics of the Linear Regression algorithm, and practical aspects of building and evaluating regression models using Python.
Explore the critical facets of Linear Regression with our module on Assumptions and Statistical Inference. Gain insights into the essential assumptions that validate the model statistically. This module guides participants through understanding, checking, and ensuring the satisfaction of these assumptions. Learn how to address violations and draw meaningful statistical inferences from the model's output, ensuring a robust and reliable application of Linear Regression in data analysis.
Module 4: Supervised Learning - Classification
Master classification models to discern relationships between variables and categorical outcomes, extracting vital business insights by identifying key decision-making factors.
Module 5: Ensemble Techniques and Model Tuning
Bagging and Random Forest
Random forest is a popular ensemble learning technique that comprises several decision trees, each using a subset of the data to understand patterns. The outputs of each tree are then aggregated to provide predictive performance. This module will explore how to train a random forest model to solve complex business problems.
(Introduction to Ensemble Techniques, Introduction to Bagging, Sampling with Replacement, Introduction to Random Forest)
Boosting
Boosting models are robust ensemble models that comprise several sub-models, each of which is developed sequentially to improve upon the errors made by the previous one. This module will cover essential boosting algorithms like AdaBoost and XGBoost that are widely used in the industry for accurate and robust predictions.
(Introduction to Boosting, Boosting Algorithms (Adaboost, Gradient Boost, XGBoost), Stacking)
Model Tuning
Model tuning is a crucial step in developing ML models and focuses on improving the performance of a model using different techniques like feature engineering, imbalance handling, regularization, and hyperparameter tuning to tweak the data and the model. This module covers the different techniques to tune the performance of an ML model to make it robust and generalized. (Feature Engineering, Cross-validation, Oversampling and Undersampling, Model Tuning and Performance, Hyperparameter Tuning, Grid Search, Random Search, Regularization)
Module 6: Unsupervised Learning
In this course, you will learn to use clustering algorithms to group data points based on their similarity, find hidden patterns or intrinsic structures in the data, and understand the importance of and how to perform dimensionality reduction.
K-means Clustering
K-means clustering is a popular unsupervised ML algorithm that is used for identifying patterns in unlabeled data and grouping it. This module dives into the workings of the algorithm and the important points to keep in mind when implementing it in practical scenarios.
(Introduction to Clustering, Types of Clustering, K-means Clustering, Importance of Scaling, Silhouette Score, Visual Analysis of Clustering)
Hierarchical Clustering and PCA
Hierarchical clustering organizes data into a tree-like structure of nested clusters, while dimensionality reduction techniques are used to transform data into a lower-dimensional space while retaining the most important information in it. This module covers the business applications of hierarchical clustering and how to reduce the dimension of data using PCA to aid in the visualization and feature selection of multivariate datasets.
(Hierarchical Clustering, Cophenetic Correlation, Introduction to Dimensionality Reduction, Principal Component Analysis)
Module 7: Introduction to Generative AI
In this course, you will get an overview of Generative AI, understand the difference between generative and discriminative AI, design, implement, and evaluate tailored prompts for specific tasks to achieve desired outcomes, and integrate open-source models and prompt engineering to solve business problems using generative AI.
Introduction to Generative AI
Generative AI is a subset of AI that leverages ML models to learn the underlying patterns and structures in large volumes of training data and use that understanding to create new data such as images, text, videos, and more. This module provides a comprehensive overview of what generative AI models are, how they evolved, and how to apply them effectively to various business challenges.
(Supervised vs Unsupervised Machine Learning, Generative AI vs Discriminative AI, Brief timeline of Generative AI, Overview of Generative Models, Generative AI Business Applications)
Introduction to Prompt Engineering
Prompt engineering refers to the process of designing and refining prompts, which are instructions provided to generative AI models, to guide the models in generating specific, accurate, and relevant outputs. This module provides an overview of prompts and covers common practices to effectively devise prompts to solve problems using generative AI models.
(Introduction to Prompts, The Need for Prompt Engineering, Different Types of Prompts (Conditional, Few-shot, Chain-of-thought, Returning Structured Output), Limitations of Prompt Engineering)
Module 8: Introduction to SQL
This course will help you gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights.
Querying Data with SQL
SQL is a widely used querying language for efficiently managing and manipulating relational databases. This module provides an essential foundation for understanding and working with relational databases. Participants will explore the principles of database management and Structured Query Language (SQL), and learn how to fetch, filter, and aggregate data using SQL queries, enabling them to extract valuable insights from large datasets efficiently.
(Introduction to Databases and SQL, Fetching data, Filtering data, Aggregating data)
Advanced Querying
SQL offers a wide range of numeric, string, and date functions, gaining proficiency in leveraging these functions to perform advanced calculations, string manipulations, and date operations. SQL joins are used to combine data from multiple tables effectively and window functions enable performing complex analytical tasks such as ranking, partitioning, and aggregating data within specified windows. This module provides a comprehensive exploration of the various functions and joins available within SQL for data manipulation and analysis, enabling them to summarize and analyze large datasets effectively.
(In-built functions (Numeric, Datetime, Strings), Joins, Window functions)
Enhancing Query Proficiency
Subqueries allow one to nest queries within other queries, enabling more complex and flexible data manipulation. This module will equip participants with advanced techniques for filtering data based on conditional expressions or calculating derived values to extract and manipulate data dynamically.
(Subqueries, Order of query execution)
Domain Exposure
Explore a variety of real-life challenges in the Self-Paced Domain Exposure module. Learn how to apply data science and analytics principles to solve diverse problems at your own pace, gaining valuable insights and skills tailored to your schedule.
Introduction to Data Science
Gain an understanding of the evolution of Data Science over time, their application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data driven solution.
Pre-Work
Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build Data Science applications
Data Visualization in Tableau
Read, explore and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards
Time Series Forecasting
Learn how to describe components of a time series data and analyze them using special techniques and methods for time series forecasting.
Model Deployment
In this course, you will learn the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.
Marketing and Retail Analytics
Understand the role of predictive modeling in influencing customer behavior and how businesses leverage analytics in marketing and retail applications to make data-driven decisions
Finance And Risk Analytics
Develop a deep appreciation of credit and market risk and understand how banks and other financial institutions use predictive analytics for modeling their risk
Web and Social Media Analytics
Understand and appreciate the most widely used tools of web analytics which form the basis for rational and sound online business decisions, and learn how to analyze social media data, including posts, content, and marketing campaigns, to create effective online marketing strategies.
Supply Chain and Logistics Analysis
Get exposed to the discipline of supply chain management and its stakeholders, understand the role of logistics in businesses and supply chains, and learn methods of forecasting prices, demand, and indexes
On-Campus Immersion in Decision Science and AI (Optional Paid Program)
The Decision Science and AI is a 3-day on-campus Program that presents a valuable opportunity to explore AI use cases and become a driving force behind AI-driven initiatives within your organization. It comprises of dynamic discussions, collaboration with like-minded professionals, and engaging networking sessions hosted at the prestigious University of Texas at Austin.
Day 1
Day 2
Day 3
AI With Deep Learning (Optional Paid Program)
Introduction to Neural Network
This course is designed to provide you with a comprehensive understanding of Deep Learning, specifically Artificial Neural Networks. These networks consist of multiple hierarchical levels and serve as fundamental building blocks for knowledge discovery, application, and prediction from data. Through this course, you will gain expertise in effectively applying Artificial Neural Networks to real-world scenarios.
Introduction to Computer Vision
Gain expertise in leveraging Convolutional Neural Networks (CNNs) to empower computer systems with visual perception and comprehension. This program equips you with the skills to effectively process and utilize image data for business applications.
Introduction to Natural Language Processing
This course will explore the fascinating application of Neural Networks in enabling computers to comprehend human language. Specifically, you will learn how to analyze text data and determine its underlying sentiment.
Data sets from the industry
Explore a wide range of hands-on projects as part of your data science course to gain practical experience and enhance your skills.
Python Foundations
Data Analysis for Food Aggregator
Business Statistics
A/B Testing for News Portal
Supervised Learning Foundations
Dynamic Pricing Model for Devices Seller
Supervised Learning Classifications
Classification Analysis for Hotel Bookings
Ensemble Techniques
Visa Approval Prediction with ML
Unsupervised Learning
Stock Clustering for Portfolio Diversification
SQL Functions
New Wheels Data Analysis
Become a data scientist
Dive into the top-rated data science course & master essential skills for a data-driven future.
Python
Tableau
Matplotlib
Seaborn
NumPy
Pandas
Upskill with Texas McCombs
Earn 8.5 Continuing Education Units (CEUs) on successful completion of the program
* Image for illustration only. Certificate subject to change.
For any feedback & queries regarding the program, please reach out to us at MSB-DSBA@mccombs.utexas.edu
Explore valuable feedback from learners on mentorship and project management support they received throughout their learning journey.
Senior Solutions Consultant, Databricks
Enrolling in this program helped me in securing a remote position at Microsoft. The program's effective training empowered me to confidently secure a job that exceeded expectations, showcasing my abilities and enabling a successful transition. Equipped with technical skills and a problem-solving mindset, the program continues to contribute to my career success in my subsequent job roles.
Director Data Science & Advanced Analytics, Archrock
Joining the program two months into my role as Director of Data Science & Advanced Analytics, I swiftly adapted to the transformative curriculum. The experience enhanced my skills, elevating marketability and confidence. Leading data science expansion, I collaborate with IT to create predictive models and reports, applying program-acquired knowledge daily to illuminate crucial business operations.
Director of Insights and Visualizations, American Cancer Society
In 2020, I joined the program to boost my data science skills post a role in performance analytics. Diving into diverse projects, I honed expertise in business statistics and Python, shifting to Power BI for reporting. This journey, coupled with a Microsoft data analyst certificate, secured my promotion to Director of Insight and Visualizations.
Self Employed Consultant
In my pursuit of a career pivot and a deeper understanding of Data Science in Business Analytics, I enrolled in the program to supplement my on-the-job knowledge. Dissatisfied with introductory Python classes on various online portals, this program's immersive experience proved crucial for hands-on learning, playing a pivotal role in helping me complete my higher education.
BI Architect,Hach
Even before completion, this program significantly impacted my career; I secured a new job, doubling my compensation by leveraging my evolving skill set. I pursued this course once I recognized the gaps in my self-directed learning, the structured foundation provided by the professors filled these blanks, reinforcing my knowledge and the hands-on approach, bridged theory with practical applications.
I have been amazed by the professional format that Great Learning has offered. I have also been very impressed with the support provided for each student. I realized I would lose my job due to the vaccine/testing mandate. I posted my resume online and purposely listed the Data Analytics Bootcamp. After a quick interview, I was offered a new dream job with a huge company that is 100% remote. I really liked the assistance and recommend all to this program if you want to build your career in Data Science.
The learnings from this program have been invaluable in my current career. I am able to apply what I learnt in my job. Recently, an Apple Inc. recruiter messaged me on LinkedIn and offered me a position at Apple as a Data Engineer. I couldn't be luckier and am now on the AI/ML team. Apple is putting me on a project immediately and the topic is something I studied in this program.
This program was a great stepping stone to begin my career in data analytics. The program office support was top notch. They were always available to respond to my questions and remind me of quizzes and projects deadline. Sunday zoom lectures were my personal favorite part of the program. Everything I didn't understand by watching pre-recorded videos throughout the week, I knew our instructor would find a simpler and practical way of explaining the content.
I work as a Business Intelligence Developer at a local non-profit. I completed Data Science & Business Analytics program by UT Austin in collaboration with Great Learning. I have done my B.S. Marketing, M.S. Computer Science. I've worked as a programmer and systems engineer for a very long time. I took up this program to learn more about current technologies and open the door to newer opportunities. I like the methods used in the program. The recordings were well done and the weekly sessions were fantastic, thanks to our mentor,
I found the program easy to follow and understand even with the limited knowledge I had. There is a lot of assistance from the career counselor and mentor sessions are very helpful. This program has helped me build my confidence not only in the Data Science and Business Analytics world but also in myself and my potential. I would recommend this program to anyone who wants to get into the field. I hope it helps you as much as it helped me.
Post Graduate Program in Data Science & Business Analytics
Post Graduate Program in Data Science & Business Analytics
Post Graduate Program in Data Science & Business Analytics
When you choose the data science and business analytics program by the McCombs School of Business at The University of Texas at Austin, you learn from leading academicians in the field of Data Science and Engineering.
Faculty Director, Center for Analytics and Transformative Technologies, McCombs School of Business, the University of Texas at Austin
Dr Kumar is an H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk and Operations Management and the Department of Finance at McCombs School of Business, the University of Texas at Austin. In addition, he serves as the Faculty Director at the Center for Analytics and Transformative Technologies. Before joining the faculty at UT Austin, Dr Kumar was an assistant professor at Purdue University and a graduate research assistant at Stanford University. He received his Ph.D. and M.S. in Scientific Computing and Computational Mathematics from Stanford University and his research focuses on decision making under uncertainty. Application areas of interest to him are quantitative finance, financial risk management, operations management, healthcare, and energy.
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Clinical Assistant Professor, Department of Information, Risk & Operations Management, McCombs School of Business, The University of Texas at Austin
Dr. Daniel Mitchell is a Clinical Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business, The University of Texas at Austin. He received his Ph.D. in Information, Risk, & Operations Management from the University of Texas. His main research interests are focused on financial engineering, specifically applying stochastic control to problems in finance. He has worked on projects in option pricing, algorithmic trading, federal intervention in the interest rate market, and human mortality forecasting,
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Senior Faculty & Director Academics, Great Learning
Dr. Abhinanda Sarkar has B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He was a lecturer at Massachusetts Institute of Technology (MIT) and a research staff member at IBM. Post this he spent a decade at General Electric (GE). He has provided committee service for the University Grants Commission (UGC) of the Government of India, for infoDev – a World Bank program, and for the National Association of Software and Services Companies (NASSCOM). He is a recipient of the ISI Alumni Association Medal, an IBM Invention Achievement Award, and the Radhakrishan Mentor Award from GE India. He is a seasoned academician and has taught at Stanford, ISI Delhi, the Indian Institute of Management (IIM-Bangalore), and the Indian Institute of Science. Currently, he is a Full-Time Faculty at Great Lakes. He is Associate Dean at the MYRA School of Business where he teaches courses such as business analytics, data mining, marketing research, and risk management. He is also co-founder of OmiX Labs – a startup company dedicated to low-cost medical diagnostics and nucleic acid testing.
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Co-Founder and Director
Vivek Anand is a data visualization consultant with 10 years of experience. His area of specialization includes Marketing and Econometrics. Vivek has an MBA from Monash University Melbourne Vic. He has worked as Sales & Marketing professional handling teams of leading Indian hospitality brands across the country. His most recent assignment was for India's largest Luxury hotel by ITC hotels in Chennai. He is a qualified trainer of Tableau 9.0 and has a passion for teaching
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Meet our dedicated mentors and industry insiders guiding DSBA learners on their analytics career journey.
6 years of relevant work experience
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3 years of relevant work experience
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9 years of relevant work experience
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6 years of relevant work experience
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5 years of relevant work experience
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9 years of relevant work experience
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3 years of relevant work experience
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3 years of relevant work experience
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8 years of relevant work experience
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9 years of relevant work experience
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Engage one-on-one with industry experts for valuable insights and guidance.
Gain Insights into Recruiter Expectations.
Showcase Your Strengths Impressively
Create a Professional Portfolio Demonstrating Skills and Expertise
Program Fees: 3,950 USD
Upfront Payment & Referral
Payment Partners
*Subject to partner approval based on applicable regions & eligibility.
Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.
Apply by filling a simple online application form.
A panel from Great Learning will review your application to determine your fit for the program.
After a final review, you will receive an offer for a seat in the upcoming cohort of the program.
Program Details
This Post Graduate Program in Data Science and Business Analytics (PGP-DSBA) is a certificate program offered by the McCombs School of Business at The University of Texas at Austin. This program features
A comprehensive learning experience combining academic rigor, peer collaboration, and mentorship with the flexibility and convenience of an online format.
Personalized learning through small group sessions.
Hands-on experience with industry-standard tools and techniques.
Interactive real-time sessions, quizzes, assignments, and projects.
Flexibility of completing the program without quitting your job.
Mentored learning is a learning approach where a mentor guides the mentee in a specific area of knowledge and skills. Mentored learning is a key element of this program. It's a guided learning experience where you will be part of a small micro-class of 20-22 learners. In these classes, you will receive personalized guidance from a senior industry mentor.
In this Data Science program, these mentored learning sessions will be conducted over weekends, with a two-way audio and video interaction. It helps improve the skills, knowledge, and confidence of the learners.
Enrolling in this program gives you access to numerous benefits, including:
The McCombs School Advantage: Learn from the renowned McCombs School of Business at The University of Texas at Austin, known for its world-class faculty, innovative teaching methods, and cutting-edge research.
Industry-Relevant Curriculum: Gain expertise in popular data science and business analytics tools and techniques like Python, Machine Learning, Predictive Modeling, Time Series Forecasting, Gen AI and its applications, and more.
Interactive Sessions: Engage in two-way interactive sessions designed to enhance your learning and help you build a strong foundation in data science and business analytics.
Hands-On Learning: Apply what you learn through industry-relevant and real-world projects, gaining practical skills that are essential for success in your career.
Industry-Relevant Projects: Work on 7+ hands-on projects across different modules to develop a portfolio that showcases your skills.
Expert Faculty and Mentors: Benefit from the expertise of Texas McCombs faculty and industry mentors, who bring their in-depth knowledge and real-world experience to the program.
Career Support: Receive personalized career guidance, resume reviews, mock interviews, and more to make you ready to enter the workforce successfully.
The program is designed to help you gain the knowledge and skills necessary to succeed in the data science and business analytics field. The key benefits of the program include:
A globally recognized Certificate of Completion from Texas McCombs with 9 CEUs (Continuing Education Units).
Expert-led, recorded content and hands-on training that develops a strong understanding of data science and business analytics.
Industry-aligned projects that help you build a robust professional portfolio.
Mentored learning that helps you advance your career.
Opportunity for networking with experienced practitioners and peers.
Career guidance services include resume reviews, LinkedIn profile enhancement, and an ePortfolio.
In the QS World University Rankings 2021, the McCombs School of Business at The University of Texas at Austin is ranked #6 worldwide for Business Analytics.
With over 40 postgraduate and 15 undergraduate programs that are ranked among the top 10 in the country, UT Austin has been consistently recognized as one of the top 20 public universities by the U.S. News & World Report.
The curriculum of this data science program is designed for recent graduates, professionals, and leaders. It covers a wide range of topics essential for success in the Data Science and Business Analytics domain, including:
Data Science Foundations: Python, Pandas, Data Visualization with Tableau, and Exploratory Data Analysis (EDA)
Data Science Techniques: Supervised Learning, Machine Learning, Model Tuning, Time Series Forecasting
Business Analytics: Marketing, Retail, Social Media, Supply Chain, and Finance Analytics
Generative AI: Supervised vs. Unsupervised Machine Learning, Overview of Generative Models, Applications of Generative AI
Upon completing this program, you will:
Develop expertise in popular data analytics tools and techniques.
Learn to independently solve complex business problems using data science and business analytics.
Gain a deeper understanding of how data science is applied across industries.
Master the skills needed to derive actionable business insights from data and communicate them effectively to stakeholders.
Build predictive models to inform strategic business decisions.
Create a professional portfolio showcasing your data science and business analytics capabilities.
The program is designed to help you gain the knowledge and skills necessary to succeed in the data science and business analytics field. The key benefits of the program include:
A globally recognized Certificate of Completion from Texas McCombs with 9 CEUs (Continuing Education Units).
Expert-led, recorded content and hands-on training that develops a strong understanding of data science and business analytics.
Industry-aligned projects that help you build a robust professional portfolio.
Mentored learning that helps you advance your career.
Opportunity for networking with experienced practitioners and peers.
Career guidance services include resume reviews, LinkedIn profile enhancement, and an ePortfolio.
This course covers a wide range of highly sought-after skills, tools, and programming languages, including Python, Business Statistics, Tableau, NumPy, Pandas, Matplotlib, Seaborn, Exploratory Data Analysis, and many more, giving learners the chance to become proficient in a variety of essential areas.
Eligibility Criteria
The eligibility criteria for this online DSBA program include:
A Bachelor’s or Undergraduate Degree with a minimum of 50% aggregate marks or equivalent is required.
No prior programming knowledge is necessary.
Note: The Post Graduate Program in Data Science and Business Analytics is designed for working professionals seeking a career transition into analytics roles, as well as students in their final year of graduation. A graduation background in quantitative disciplines such as engineering, mathematics, sciences, statistics, economics, etc. can maximize the program's benefits.
Please adhere to the following guidelines to register for this program:
Step-1: Application Form
Apply now by filling out the online application form. As the program follows a rolling application process, we encourage you to apply at the earliest.
Step-2: Shortlisting and Panel Review
Our panel of experts will carefully review your application to assess your eligibility for the program. Your academic performance, professional work experience, and motivation levels will be taken into account.
Step-3: Interview/Screening Process
If your application is shortlisted, you may be invited to a telephonic screening interview (although those with a strong background and experience may be exempt).
Step-4: Admissions Offer
After a thorough evaluation by the admissions committee, you will be invited to join the next cohort of the program.
The program operates on a rolling application process and will close once all seats are filled. We encourage you to apply as soon as possible to secure your spot.
Admission Queries
Please adhere to the following guidelines to register for this course:
Step-1: Application Form
Don't miss your chance! Apply now by filling out the online application form. As the program follows a rolling application process, we encourage you to apply at the earliest.
Step-2: Shortlisting and Panel Review
Our panel of experts will carefully review your application to assess your eligibility for the program. Your academic performance, professional work experience, and motivation levels will be taken into account.
Step-3: Interview/Screening Process
If your application is shortlisted, you may be invited to a telephonic screening interview (although those with a strong background and experience may be exempt).
Step 4: Admissions Offer
After a thorough evaluation by the admissions committee, you will be invited to join the next cohort of the program.
Our program operates on a rolling application process and will close once all seats have been filled. Submit your application promptly to increase your chances of being accepted.
Fee Related Queries
No, all necessary learning materials are provided through the Learning Management System (LMS) and are included in the program fee.
The program fee to enroll in this PG Program is USD 3,800. Discounts are also available, which are as follows:
Fee after Upfront Discount: USD 3,600
Fee after Referral Discount: USD 3,600
Please get in touch with the program advisor for more details and flexible payment options.
Yes, corporate sponsorships are welcome. We can provide assistance during the application process for participants sponsored by their employers.
[Participants can connect with us at dsba.utaustin@mygreatlearning.com or +1 512-793-9938]
Others
The field of data science and business analytics offers diverse and high-impact career opportunities across industries. With companies further adopting data as the basis for decision-making, the need for experts in these areas is rising.
Here are some popular job roles in Data Science and Business Analytics:
Data Analyst – Analyzes and processes the data to provide actionable insights.
Data Scientist – Develops predictive models and applies machine learning techniques.
Business Analyst — Analyzes data to support business decisions and increase efficiency.
Data Engineer – Develops and maintains data pipelines and infrastructure.
Statistician – Applies statistical methods to analyze and interpret data trends.
Database Administrator – Manages databases to ensure data integrity and accessibility.
Data Architect – Designs scalable data solutions for organizations.
Pursuing a data science and business analytics program will help you acquire the skills needed to succeed in one of these roles.
Data Science And Business Analytics are similar in some respects, yet they have different roles in an organization. Below are the key differences:
It is a well-known fact that both areas of the field work together to improve business performance.
Data Science and Business Analytics can be used in a variety of different industries, such as:
Internet Search – Popular search engines such as Google and Bing utilize data science algorithms to present relevant outcomes.
Speech Recognition – Siri and Alexa are virtual assistants that use data science to perform voice recognition and natural language processing.
Targeted Advertising – Businesses leverage analytics to optimize digital marketing campaigns and improve customer engagement.
Recommendation Systems – Netflix and Amazon are among the platforms that use data science to offer a more personalized experience in recommending products based on user activity.
Healthcare – Data Science helps in predictive diagnostics, personalized treatment, & operational efficiency.
Finance – Business Analytics is applied in banks and financial institutions in the detection of fraud as well as in the management of risks, and in formulating investment strategies.
Such technologies are transforming industries, and therefore, are valuable skills for professionals.
There are a lot of reasons why Data Science and Business Analytics should be your career choice:
High Demand – All industries nowadays rely on data-driven insights, which leads to the high demand for professionals.
High Earning Potential – Careers in Data Science and Business Analytics are among the best-paying careers in the world.
Job Security – The demand for these positions will only continue to rise as businesses continue to adopt data-driven strategies.
Therefore, a Data Science and Business Analytics program can make you successful in an intense and promising career in a rapidly changing field.
Application Closes
20th Jun 2025
The McCombs School of Business at The University of Texas at Austin is collaborating with Great Learning to deliver the PG Program in Data Science and Business Analytics. Great Learning is an ed-tech company that has empowered learners from over 170+ countries to achieve positive career growth outcomes.
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