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Comprehensive Data Science and Generative AI Course

Comprehensive Data Science and Generative AI Course

Master data science applications and secure a future-ready career

Application closes 17th Jul 2025

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Program Outcomes

Champion skills in Data Science and Generative AI

Use emerging technologies to drive business insights and innovation

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    Learn to solve complex business problems with Data Science and Generative AI concepts

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    Build real-world solutions using Python, LLMs, machine learning, and advanced analytics tools

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    Build an impressive, industry-ready portfolio with hands-on projects

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    Earn 9 CEUs and a certificate of completion from Texas McCombs upon completion of the program

Earn a certificate of completion

  • Eduniversal (2024)

    #1 (U.S., Big Data Management) in MS Business Analytics

    Eduniversal (2024)

  • ranking 6

    #6 in MS - Business Analytics

    QS World University Rankings (2024)

  • ranking 6

    #6 in Executive Education

    Custom Programs Financial Times, 2022

  • The Financial Engineer Times (2024)

    #6 in MS Business Analytics

    The Financial Engineer Times (2024)

Key Highlights

Why choose this program

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    Learn from a leading university

    Earn a certificate of completion from a world-renowned university

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    Industry-ready curriculum

    Master Python, SQL, machine learning, and GenAI techniques like prompt engineering and LLMs for real business use cases.

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    Work on real-world projects

    Work on 7 hands-on projects and 40+ case studies using tools like Python, Hugging Face, and Tableau to build a job-ready data science portfolio.

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    Personalized program support

    Get 1:1 personal assistance from a Program Manager to complete your course with ease.

Skills you will learn

Generative AI & Prompt Engineering

Python Foundations

Data Visualization

Business Statistics

Ensemble Techniques

Supervised & Unsupervised Learning

Forecasting Methods

Exploratory Data Analysis

Inferential Statistics

Linear Regression

Generative AI & Prompt Engineering

Python Foundations

Data Visualization

Business Statistics

Ensemble Techniques

Supervised & Unsupervised Learning

Forecasting Methods

Exploratory Data Analysis

Inferential Statistics

Linear Regression

view more

  • Overview
  • Why GL
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Career support
  • Fees
  • FAQ
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This program is ideal for

Align your learning with your professional aspirations

  • Mid-to senior-level

    professionals looking to influence business decisions using data-driven insights

  • Professionals transitioning

    into Data Science who want a strong foundation in analytics and Generative AI

  • Future-ready professionals

    seeking to stay ahead in a business environment increasingly shaped by data and AI

  • Leaders and changemakers

    aiming to implement AI-enabled strategies within their organizations

Upskill with one of the best Data Science programs

  • Texas McCombs Programs

    Other Courses

  • Certificate

    hands upCertificate of completion from Texas McCombs

    hands downNo university certificate

  • Gen AI modules

    hands upExtensive coverage of Gen AI topics

    hands downLimited coverage

  • Live mentored learning

    hands upLive interactive online classes with industry professionals

    hands downLimited to no live classes

  • Career support

    hands upYes, with 1:1 Career Mentoring, Resume and LinkedIn Profile Review, and more.

    hands downNo career support

  • Hands-on projects

    hands up7 hands-on projects, and 40+ case studies

    hands downFewer projects

  • Dedicated Program support

    hands upDedicated support to complete your course

    hands downLimited support

Elevate Your Skills with On-Campus Immersion (Optional Add-On)

Decision Science and AI Program

In the 3-day immersive on-campus program you can:

  • Network: Connect with like-minded AI professionals

  • Immersive learning: Experience a 3-day on-campus event at Texas McCombs

  • CEUs: Earn 1 CEU on successful completion of the program

  • Create: Intelligent Decision Science Systems

Reach out to your Program Advisor for more details

Comprehensive Curriculum

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.

  • 225+ hrs

    hands-on projects

  • 9+

    Case studies

Pre-Work | 1 week

In this week, learners will build a foundation in core Data Science and Generative AI concepts, develop Python skills for data manipulation and analysis, and explore practical applications of Generative AI. The week concludes with a hands-on case study applying Data Science to solve a real-world business problem.

  • Introduction to Data Science
  • Introduction to Generative AI
  • Python Programming Essentials

Module 01: Data-driven Insights using Python | 4 Weeks

In this module, learners will read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and business recommendations by performing exploratory data analysis using some of the most widely used Python packages.

Week 1: Python Fundamentals for Working with Data

  • Variables and Data Types 
  • Data Structures 
  • Conditional and Looping Statements 
  • Functions

Week 2: Data Manipulation Using NumPy and Pandas

  • NumPy Arrays and Functions 
  • Accessing and Modifying NumPy Arrays 
  • Saving and Loading NumPy Arrays 
  • Pandas Series (Creating, Accessing, and Modifying Series) 
  • Pandas DataFrames (Creating, Accessing, Modifying, and Combining DataFrames) 
  • Pandas Functions 
  • Saving and Loading Datasets Using Pandas

Week 3: Exploratory Data Analysis for Extracting Insights

  • Data Overview 
  • Univariate Analysis (Histograms, Boxplots, and Bar Graphs) 
  • Bivariate/Multivariate Analysis (Line Plot, Scatterplot, LM Plot, Jointplot, Violin Plot, Strip Plot, Swarm Plot, Cat Plot, Pairplot, Heatmap) 
  • Customizing Plots 
  • Missing Value Treatment 
  • Outlier Detection and Treatment

PROJECT 1

Module 02: Generative AI for Text Analysis | 2 Weeks

In this module, learners will build practical expertise in Generative AI by mastering Prompt Engineering and Large Language Model workflows. The module covers prompt design, text classification, and summarization, and applying LLMs to solve real-world business problems.

Week 1: Introduction to Prompt Engineering

  • 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

Week 2: Text Analysis with LLMs

  • Introduction to Text-to-Label Generation 
  • Data Preparation Process 
  • Introduction to Text-to-Text Generation 
  • Structure of Text Generation Tasks

Module 03: Decision Making with Business Statistics | 5 Weeks

In this module, learners will use Python for statistical analysis to evaluate business estimates with confidence intervals and test assumptions before committing resources. They will analyze data distributions and perform hypothesis testing to support data-driven decisions.  

Week 1: Inferential Statistics Foundations

  • Experiments, Events, and Definition of Probability 
  • Introduction to Inferential Statistics 
  • Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions) 
  • Binomial Distribution 
  • Normal Distribution

Week 2: Data Sampling and Estimation for Accurate Insights

  • Sampling, Central Limit Theorem, and Estimation 
  • Introduction to Hypothesis Testing 
  • Hypothesis Formulation and Performing a Hypothesis Test 
  • One-Tailed and Two-Tailed Tests 
  • Confidence Intervals and Hypothesis Testing

Week 3: Common Statistical Tests for Informed Decisions

  • Test for One Mean 
  • Test for Equality of Means 
  • Chi-Square Test of Independence 
  • One-Way ANOVA

PROJECT 2

Module 04: Predictive Modeling with Linear Regression | 3 Weeks

In this module, learners will explore linear models to capture relationships between variables and continuous outcomes. They will check the statistical validity of these models and draw inferences to gain business insights into key factors influencing decision-making.

Week 1: Introduction to Modeling Linear Relationships

  • Introduction to Learning from Data 
  • Simple and Multiple Linear Regression 
  • Evaluating a Regression Model 
  • Pros and Cons of Linear Regression

Week 2: Statistical Inferences from Linear Regression

  • Statistician vs ML Practitioner 
  • Linear Regression Assumptions 
  • Statistical Inferences from a Linear Regression Model

PROJECT 3

Module 05: Classification Techniques for Predictive Modeling | 3 weeks

In this module, learners will explore classification models to capture relationships between variables and categorical outcomes. They will gain business insights by identifying key factors that influence decision-making.


Week 1: Logistic Regression for Probability-Based Insights

  • Introduction to Logistic Regression 
  • Interpretation from a Logistic Regression Model 
  • Changing the Threshold of a Logistic Regression Model 
  • Evaluation of a Classification Model 
  • Pros and Cons

Week 2: Decision Trees for Transparent Decision-Making

  • Introduction to Decision Tree 
  • Different Impurity Measures 
  • Splitting Criteria in a Decision Tree 
  • Methods of Pruning a Decision Tree 
  • Regression Trees 
  • Pros and Cons

PROJECT 4

Module 06: Robust Data Modeling with Ensembling and Tuning Techniques | 5 weeks

In this module, learners will use ensemble techniques to combine decisions from multiple models and improve predictions. They will apply feature engineering and hyperparameter tuning to build robust models that help optimize business costs.

Week 1: Bagging Ensembles for Improved Predictive Performance

  • Introduction to Ensemble Techniques 
  • Introduction to Bagging 
  • Sampling with Replacement 
  • Introduction to Random Forest

Week 2: Boosting Ensembles for Improved Predictive Performance

  • Introduction to Boosting 
  • Boosting Algorithms (AdaBoost, Gradient Boost, XGBoost) 
  • Stacking

Week 3: Tuning and Validation Techniques for Optimized Model Performance

  • Feature Engineering 
  • Cross-Validation 
  • Oversampling and Undersampling 
  • Model Tuning and Performance 
  • Hyperparameter Tuning 
  • Grid Search 
  • Random Search 
  • Regularization

PROJECT 5

Module 07: Pattern Discovery with Unsupervised Learning | 3 Weeks

In this module, learners will apply clustering algorithms to group data based on similarity and uncover hidden patterns. The content also includes dimensionality reduction techniques to enhance understanding of intrinsic data patterns and structure.

Week 1: Insightful Data Segmentation with K-Means Clustering

  • Introduction to Clustering 
  • Types of Clustering 
  • K-Means Clustering 
  • Importance of Scaling 
  • Silhouette Score 
  • Visual Analysis of Clustering

Week 2: Discovering Patterns with Hierarchical Clustering and PCA

  • Hierarchical Clustering 
  • Cophenetic Correlation 
  • Introduction to Dimensionality Reduction 
  • Principal Component Analysis

PROJECT 6

Module 08: Data Querying and Analytics with SQL | 4 Weeks

In this module, learners will build a foundation in database concepts and SQL. They will write simple queries to filter and retrieve data and use advanced SQL techniques with joins, window functions, and subqueries to solve real-world data problems and extract business insights.

Week 1: Data Retrieval & Aggregation Essentials

  • Introduction to Databases and SQL 
  • Fetching Data, Filtering Data 
  • Aggregating Data

Week 2: Querying Techniques for Relational Data Analysis

  • In-Built Functions (Numeric, Datetime, Strings) 
  • Joins 
  • Window Functions

Week 3: Advanced Querying for Enhanced Proficiency and Insights

  • Subqueries 
  • Order of Query Execution

PROJECT 7

Work on 7 hands-on projects

Engage in practical projects and case studies to solve real-world business problems using data and GenAI tools.

  • 7

    Hands-on projects

  • 20+

    case studies

  • Gen-AI Augmented

    Practical Learning

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Hospitality

Hotel Booking Prediction

About the Project

Predict hotel booking cancellations in advance to reduce revenue loss and optimize occupancy using classification models.

Skills you will learn

  • Decision Trees EDA Random Forest Classification
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Food & Beverages

Restaurant Review Analysis

About the Project

Use GenAI and LLMs to analyze and tag customer reviews, uncovering sentiment insights at scale for better decision-making

Skills you will learn

  • LLMs Sentiment Analysis
  • Prompt Engineering
  • Text Mining
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Manufacturing

Predictive Machine Maintenance

About the Project

Predict machine failures, identify key risk factors, and help manufacturing units reduce downtime and maintenance costs.

Skills you will learn

  • Decision Trees Feature Importance Predictive Analytics Visualization
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Transportation

Rental Bike Demand Forecasting

About the Project

Predict hourly and daily demand for rental bikes to optimize fleet allocation and improve operations during peak hours.

Skills you will learn

  • XGBoost Regression
  • Trees Feature
  • Engineering EDA
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BFSI

Credit Card Eligibility (CredPay)

About the Project

Analyze financial data to identify customer segments eligible for credit cards and build insights for targeted marketing.

Skills you will learn

  • EDA Business Analytics Segmentation Predictive Modeling
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Healthcare

Diabetes Risk Prediction

About the Project

Develop a classification model to predict diabetes risk based on patient health records and key clinical indicators

Skills you will learn

  • Random Forest Classification
  • Data Cleaning
  • Model Evaluation
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Healthcare

Music-Startup Data Analysis

About the Project

Analyze music record sales to discover consumer trends by demographics and offer growth recommendations to the business.

Skills you will learn

  • SQL Aggregation
  • Data Filtering
  • Recommendation Logic
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Tourism

Tourism Services Investment Clustering

About the Project

Cluster countries based on tourism indicators to identify high-return investment locations for travel and tourism businesses.

Skills you will learn

  • K-Means PCA Cluster Profiling EDA
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Health & Wellness

Diet Plan Effectiveness Study

About the Project

Evaluate the effectiveness of different diet plans using statistical hypothesis testing and inferential analytics.

Skills you will learn

  • Hypothesis Testing ANOVA
  • Statsmodel
  • Confidence Intervals
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EdTech

Online Course Engagement Dashboard

About the Project

Visualize and analyze student behavior on an EdTech platform to guide academic planning and course recommendations.

Skills you will learn

  • Tableau EDA Data Visualization Behavior Analysis

Master industry-relevant tools

Dive into the top-rated data science course & master essential skills for an AI-powered future.

  • tools-icon

    Python

  • tools-icon

    Tableau

  • tools-icon

    Matplotlib

  • tools-icon

    Seaborn

  • tools-icon

    NumPy

  • tools-icon

    Pandas

  • And More...

Earn a certificate of completion from The McCombs School of Business at The University of Texas at Austin

Get a certificate from one of the top universities in USA and showcase it to your network

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* Image for illustration only. Certificate subject to change.

Learn from UT Austin Faculty

When you choose the data science with generative AI 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.

  • Dr. Kumar Muthuraman - Faculty Director

    Dr. Kumar Muthuraman

    Faculty Director, Center for Analytics and Transformative Technologies, McCombs School of Business, the University of Texas at Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

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  • Dr. Daniel A Mitchell - Faculty Director

    Dr. Daniel A Mitchell

    Clinical Assistant Professor, Department of Information, Risk & Operations Management, McCombs School of Business, The University of Texas at Austin

    Research Director, Center for Analytics and Transformative Technologies

    15+ years of experience in financial engineering and quantitative finance.

    Know More
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  • Dr. Abhinanda Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Senior Faculty & Director Academics, Great Learning

    30+ years of experience in data science, ML, and analytics.

    Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

    Know More
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  • Mr. R Vivekanand - Faculty Director

    Mr. R Vivekanand

    Co-Founder and Director

    Expert in data visualization and marketing econometrics with 10+ years

    Qualified Tableau trainer passionate about teaching business analytics

    Know More
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  • Prof. Mukesh  Rao - Faculty Director

    Prof. Mukesh Rao

    Senior Faculty, Academics, Great Learning

    20+ years of expertise in AI, machine learning, and analytics

    Director - Academics at Great Learning

    Know More
    Great Learning Logo
  • Dr. Pavankumar Gurazada - Faculty Director

    Dr. Pavankumar Gurazada

    Senior Faculty, Academics, Great Learning

    15+ years of experience in marketing, digital marketing, and machine learning.

    Ph.D. from IIM Lucknow; MBA from IIM Bangalore; IIT Bombay graduate.

    Know More
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  • Mr. Faraaz Ahmed Sheriff  - Faculty Director

    Mr. Faraaz Ahmed Sheriff

    Senior Data Scientist

    Expert in advanced analytics and evidence-based decision-making.

    Extensive experience consulting for Fortune 500 companies.

    Know More
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Industry mentors from top organizations

Collaborate with mentors in small group sessions to apply skills, overcome challenges, and network globally

  •  Prabhat Bhattarai - Mentor

    Prabhat Bhattarai linkin icon

    Data Scientist Apple
    Apple Logo
  •  Dale Seema - Mentor

    Dale Seema linkin icon

    Data Science Specialist FNB
    FNB Logo
  •  Yogesh Singh   - Mentor

    Yogesh Singh linkin icon

    Founder and CEO, NSArrows
    Company Logo
  •  Paolo Esquivel   - Mentor

    Paolo Esquivel linkin icon

    Senior Data Scientist, Course Hero
    Company Logo
  •  Olayinka Fadahunsi - Mentor

    Olayinka Fadahunsi linkin icon

    Head of Data Science and Engineering
    Company Logo

Get dedicated career support

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    1:1 career mentoring (Optional)

    Get personalized guidance from industry experts to plan your data science and AI career

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    Interview prep resources

    Access curated interview questions and prep tips from top recruiters

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    Resume & LinkedIn review

    Have your profile reviewed by experts to highlight key strengths

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    Career orientation session

    Get access to a live session to explore roles, paths, and career direction strategies

Course fees

The course fee is 3,950 USD

Invest in your career

  • benifits-icon

    Understand data science from business, technical, and conceptual perspectives

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    Build proficiency and practical skills in data science tools, including Generative AI applications

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    Tackle business challenges using data science, analytics, and Generative AI techniques

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    Perform end-to-end analysis to extract insights, applying Generative AI solutions

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Easy payment plans

Avail our flexible payment options & get financial assistance

  • discount available

    Upfront discount:3,950 USD 3,750 USD

    Referral discount:3,950 USD 3,800 USD

Payment Partners

Check our different payment options with trusted partners

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

Take the next step

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Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 17th Jul 2025

Application Closes: 17th Jul 2025

Talk to our advisor for offers & course details

Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

  • steps icon

    1. Fill application form

    Apply by filling a simple application form.

  • steps icon

    2. Review

    A panel from Great Learning will review your application to assess your suitability for the program.

  • steps icon

    3. Join the program

    After the final review, you will receive an offer for a seat in the program's upcoming cohort.

Batch start date

  • USA & Canada · To be announced

    Admissions Open

  • All other regions · To be announced

    Admissions Open

Frequently asked questions

Program Details
Eligibility Criteria
Admission Queries
Fee Related Queries
Others

What is unique about this Data Science and Business Analytics course?

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.

What is meant by mentored learning?

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.

What are the benefits of enrolling in this Data Science and Business Analytics certificate course from the McCombs School of Business at The University of Texas at Austin?

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.

How will this online course in Data Science and Business Analytics help me progress in my career?

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.

Is this PG in Data Science and Business Analytics a completely online program?

Yes, this entire program is online. You'll receive a combination of recorded lectures from the McCombs School of Business faculty and live online mentored classes, all delivered in small groups of learners. All assessments will also be conducted online for your convenience.

Which criteria will be used to assess my performance in this Data Science course?

Performance is assessed through continuous evaluation, including quizzes, assignments, case studies, and project reports. We use a continuous evaluation system that encompasses a variety of assessments, including quizzes, assignments, case studies, and project reports.

Are there any experiential projects as part of the Post Graduate Program in Data Science and Business Analytics?

Yes, this program includes several experiential projects that use Time Series Forecasting, Predictive Modeling, Advanced Statistics, and Data Mining. These hands-on projects help you apply the knowledge and concepts learned across all modules, ensuring you're fully equipped for real-world challenges.

Which companies do the industry mentors in the Post Graduate Program in Data Science and Business Analytics work for?

Our industry mentors have extensive experience working with top global organizations such as Microsoft, Google, McKinsey, Boeing, HSBC, and Citigroup, bringing valuable real-world insights to the program.

What is the ranking of the McCombs School of Business at The University of Texas at Austin?

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.

What is the curriculum for this PGP-DSBA from the McCombs School of Business at The University of Texas at Austin?

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

What are the learning outcomes of this online Data Science and Business Analytics course from the McCombs School of Business at The University of Texas at Austin?

Upon completing this program, you will:

  1. Develop expertise in popular data analytics tools and techniques.

  2. Learn to independently solve complex business problems using data science and business analytics.

  3. Gain a deeper understanding of how data science is applied across industries.

  4. Master the skills needed to derive actionable business insights from data and communicate them effectively to stakeholders.

  5. Build predictive models to inform strategic business decisions.

  6. Create a professional portfolio showcasing your data science and business analytics capabilities.

Who will be the faculty for this course?

The faculty for this program consists of experienced academicians from the UT Austin McCombs School of Business and Great Learning. The learners will benefit from live, mentor-led sessions with industry experts in Data Science and Business Analytics, delivering top-quality education and valuable real-world insights.

How will this online course in Data Science and Business Analytics help me progress in my career?

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.

What is the required weekly time commitment?

The learners should expect to commit approximately 8-12 hours per week to the program, including 2 hours of recorded lectures, 2 hours of hands-on sessions each week, and additional time for self-study and project work.

What skills, tools, and languages will I learn in this course?

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.

What are the eligibility criteria for this online Data Science and Business Analytics course?

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.

What is the admission process for enrolling in this Data Science and Business Analytics course from the McCombs School of Business at The University of Texas at Austin?

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.

What is the application cutoff date for this course?

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.

What is the admission process to enroll in this Data Science and Business Analytics course from UT Austin McCombs School of Business?

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.

When is the application cutoff date for this course?

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.

Would I have to spend extra on books, online learning material, or license fees?

No, all necessary learning materials are provided through the Learning Management System (LMS) and are included in the program fee.

What is the course fee to pursue this online PG Program in Data Science and Business Analytics from the McCombs School of Business at The University of Texas at Austin?

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.

Does this course accept corporate sponsorships?

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]

What are the various job roles in Data Science and Business Analytics?

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.

What are the differences between Data Science and Business Analytics?

Data Science And Business Analytics are similar in some respects, yet they have different roles in an organization. Below are the key differences:


Data Science

Business Analytics

Data Science is a discipline that applies machine learning and artificial intelligence to analyze huge datasets, discover patterns, and build predictive models to process data.


Business Analytics focuses on utilizing data in decision-making and strategy development within organizations, with a heavy emphasis on statistical analysis and presenting findings through data visualization.


Data Scientists develop algorithms and work with structured and unstructured data.

Business Analysts use only structured data to generate insight.


Data science requires more programming languages, like Python.

Business analytics, on the other hand, is more tool-based, such as Excel, Tableau, SQL, etc.




It is a well-known fact that both areas of the field work together to improve business performance.

What are the various applications of Data Science and Business Analytics?

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.

Why should you make Data Science and Business Analytics your career path?

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.

Got more questions? Talk to us

Connect with a program advisor and get your queries resolved

Speak with our expert +1 512 793 9938 or email to dsga.utaustin@mygreatlearning.com

career guidance

Delivered in Collaboration with:

The McCombs School of Business at The University of Texas at Austin is collaborating with Great Learning to deliver the Post Graduate Program in Data Science with Generative AI: Applications to Business. 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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