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    Lakshmi Panchagnula

    Water Programs Specialist

    The program is a great introduction to programming and model building even for those without any background in coding.

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    Gabriel Arbe

    Director of Operations, Latin America

    Perfect combination of relevant content, flexibility, academic rigor, and practical content that allowed me to immediately put all into practice at work.

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    Great learning experience and would definitely recommend it to anyone who is looking for a perfect program.

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    Gave me the confidence that I am at the same knowledge level in this course as those who have been working as a data scientist for years.

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    The course of DSBA helped me gain the skills, which I can use at my work.

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    Was able to gain in depth knowledge even with no knowledge or experience in DSBA

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    The course helped me by giving a peek into whether I can make a move from Finance to Data Science

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    I would recommend it to anyone interested in upgrading their skillset or starting a new career.

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Why Choose Our Data Science Program

Global collaborations with peers

Meet other data science learners through micro classes and grow your professional network.

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Weekend online mentorship by experts

Get assistance on 7 industry-relevant projects through weekend sessions with a certified industry professional.

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We help you stay motivated. Get personalised academic and non-academic support during the program.

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Industry-relevant Projects & Skills

Learn in-depth Python, Machine Learning, Data Visualization and Ensemble Techniques.s

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Transform your career with Data Science and Business Analytics

Certificate from the University of Texas at Austin

Great Learning data science and business analytics certificate

* Image for illustration only. Certificate subject to change.

  • MS - Business Analytics

    MS - Business Analytics

    QS World University rankings, 2022

  • Executive Education

    Executive Education

    Custom Programs by Financial Times, 2022

For any feedback & queries regarding the program, please reach out to us at MSB-DSBA@mccombs.utexas.edu

Elevate your skills with optional add on courses

Decision Science and AI Program

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

  • Connect with like-minded AI professionals.

  • Immerse in On-Campus Learning for 3 Days

  • Learn Leadership Skills

  • Create Intelligent Decision Science Systems

Reach out to your Program Advisor for more details

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AI With Deep Learning

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.

Reach out to your Program Advisor for more details

Comprehensive Curriculum

The curriculum has been designed by the faculty at McCombs School of Business at the University of Texas at Austin.

225+ hrs

Learning content

9+

Languages & Tools

Unit 1

Foundations

The ‘Foundations’ module will empower you with the fundamentals of statistics, Python, and domain-specific business knowledge to set the foundations on which the rest of the course will be built. Every concept taught in this module will help you build a strong foundation that will stay with you forever. This is a light warm-up to the world of Data Science. By the end of this foundation course, you will be comfortable talking about Data Science terms.

Module 0: Pre-work

This module will teach some prerequisites before starting with the Data Science and Business Analytics online course like Programming concepts and Python.

  • Basics of Programming
    You will be introduced to programming concepts in this module. Programming is a set of instructions to a computer to perform specific tasks.
  • Introduction to Python
    In this module, you will be introduced to the Python programming language and its fundamentals like syntax.

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.

  • Python Programming

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.

  • Python for Data Science

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.

  • Python for Visualization

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.

  • Exploratory Data Analysis

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.

  • Inferential Statistics Foundations

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.

  • Estimation and Hypothesis Testing

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.

  • Common Statistical Tests

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.

Unit 2

Techniques

The ‘Techniques’ module in this Data Analytics course will empower you with a thorough grounding in the most widely-used analytics and data science techniques so that you can approach any business problem with ease. By this time you have an overview of what is coming and can begin with mastering each technique.

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.

  • ​​​Intro to Supervised Learning - Linear Regression

    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.

  • Linear Regression Assumptions and Statistical Inference

    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.

  • Logistic Regression

This module covers the theoretical foundations of Logistic Regression, performance assessment, and the extraction of meaningful statistical inferences. Participants will grasp the intricacies of model interpretation, evaluate classification model performance, and discover the impact of threshold adjustments in Logistic Regression for enhanced predictive accuracy. Explore applications spanning medicine, finance, and manufacturing, ensuring a robust understanding and application of Logistic Regression in diverse fields.

  • Decision Tree

Explore the power of Decision Trees in our module, uncovering their role as supervised ML algorithms for hierarchical decision-making in both classification and regression scenarios. Delve into the intricacies of modeling complex, non-linear data with Decision Trees. This module elucidates the process of building a Decision Tree, introduces various pruning techniques to enhance performance, and provides insights into different impurity measures crucial for decision-making. Acquire a comprehensive understanding of the Decision Tree algorithm, empowering you to navigate its construction and optimization effectively.

Module 5: Ensemble Techniques and Model Tuning

In this course, you will learn how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs

  • 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)

Unit 3

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

Unit 4

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

  • Welcome & Program Orientation
  • Introduction to Decision Sciences & AI
  • Campus Tour & Group Photo
  • Introduction to Dynamic Programming
  • Programming an AI agent to Play a Variant of Blackjack

Day 2

  • Introduction to Reinforcement Learning
  • Programming an AI Agent that learns by itself to play computer games
  • Session with Industry Mentor 
  • The Art and Science of Negotiations

Day 3

  • Project Brief and Active group work
  • Group work on Project 
  • Certifications and Photo Ops

Languages and Tools covered

Hands-On Case Projects

Data sets from the industry
NETFLIX · UBER · Amazon

  • 1000+

    Projects completed

  • 22+

    Domains

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PYTHON FOUNDATIONS

Data Analysis for Food Aggregator

Explore food aggregator data to address key business questions, uncover trends, and suggest actionable insights for improved operations and customer satisfaction
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BUSINESS STATISTICS

A/B Testing for News Portal

Conduct A/B testing to gauge the effectiveness of a new landing page design for an online news portal, comparing user engagement metrics to optimize website performance.
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ENSEMBLE TECHNIQUES

Visa Approval Prediction with ML

Implement ensemble machine learning models to facilitate visa approval processes, recommending profiles for certification or denial based on comprehensive analysis of applicant data.
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SQL Functions

New Wheels Data Analysis

Analyze a vehicle resale company's listing and customer feedback data, answer business questions, and provide recommendations for the leadership to enable data-driven decision-making.
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SUPERVISED LEARNING FOUNDATIONS

Dynamic Pricing Model for Devices Seller

Utilize linear regression to build a dynamic pricing model for a seller of used and refurbished devices, identifying influential factors to optimize pricing strategies for profitability.
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SUPERVISED LEARNING CLASSIFICATIONS

Classification Analysis for Hotel Bookings

mploy classification models to determine factors influencing hotel booking cancellations, aiding in proactive management strategies and customer retention efforts.
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UNSUPERVISED LEARNING

Stock Clustering for Portfolio Diversification

Analyze financial attributes of stocks to cluster and build a diversified investment portfolio, optimizing risk management and potential returns through strategic asset allocation

Our Faculty and Mentors

Learn from leading academicians in the field of Data Science and Engineering and several experienced industry practitioners from top organisations.

Dr. Kumar Muthuraman - Faculty Director

Dr. Kumar Muthuraman

Faculty Director - Centre for Research and Analytics 20+ Years Work Experience

Dr. Abhinanda Sarkar - Faculty Director

Dr. Abhinanda Sarkar

Faculty Director, Great Learning

Prof. Raghavshyam Ramamurthy - Faculty Director

Prof. Raghavshyam Ramamurthy

Industry Expert in Visualization

Mr. R Vivekanand - Faculty Director

Mr. R Vivekanand

Co-Founder and Director

Industry Mentors from Top Organisations

Prabhat Bhattarai - Mentor

Prabhat Bhattarai

Data Scientist

Nikhila Kambalapalli - Mentor

Nikhila Kambalapalli

Consultant, Data Science

Srihari  Nagarajan - Mentor

Srihari Nagarajan

Senior Data Scientist

Olayinka Fadahunsi - Mentor

Olayinka Fadahunsi

Head of Data Science and Engineering

Anis Sharafoddini - Mentor

Anis Sharafoddini

Data Scientist Lead

Avinash Ramyead - Mentor

Avinash Ramyead

Senior Quantitative UX Researcher / Data Scientist / Behavioral Scientist in Video ML

Edward Krueger - Mentor

Edward Krueger

Principal Data Scientist and Proprietor

Paolo Esquivel - Mentor

Paolo Esquivel

Senior Data Scientist

Michael Keith - Mentor

Michael Keith

Analytics Manager

Anuj Saini  - Mentor

Anuj Saini

Principal Data Scientist, RPX Corporation

Mohit Jain - Mentor

Mohit Jain

Principal Data Scientist

Yogesh Singh   - Mentor

Yogesh Singh

Founder and CEO, NSArrows

Rushabh Shah  - Mentor

Rushabh Shah

Software Developer, Kyra Solutions

Roshan Santhosh   - Mentor

Roshan Santhosh

Data Scientist, Meta

Learner Testimonials

  • "I've done a program before with a different university and I didn't feel there was as much interaction as I would have loved. So I identified a few things, such as mentoring sessions, which you do with an expert from the business and that allows you to interact as if you were on campus. "

    Aziz Elbahri, Manager, Customer Care Administration, American Airlines (United States)

  • "It's very compact, but at the same time it covers everything with a real-world domain context behind each of the problem statements, and in doing so, they provide you with all the collateral materials to help you in your journey. "

    Yuedong Lu, Director Client Partnership & Growth, Fractal Analytics (United States)

  • "The advantage of learning it on your own is that you can start and stop. I liked that aspect of it where I could control how I digested the lectures. "

    Sarah Bittner, Patent Agent, Montgomery McCracken Walker & Rhoads LLP (United States)

  • "The most helpful features of the program are the mentor sessions and the solo projects. It felt like you put everything together and put it in a business case, and that was very helpful for me. "

    Monica Suarez, Former Founder, Arewa (United States)

  • "My two biggest achievements of my life have been my son and then Great Learning. The program has been amazing and everyone from the program office, faculties to mentors were very supportive and helped me achieve my professional and personal goals. "

    Shamelle Chotoki, GSI Analyst, Western Union (United States)

  • "This program is a great start for any individual irrespective of your profession or your comfort level in programming. All the video lectures, the mentored learning sessions, the assignments, the projects, everything will give you a lot of confidence to build a career in Data Science. "

    Indu Chanchal Polpaya, Postdoctoral Research Associate, Lehigh University (United States)

  • "I loved the course because it really just stretched me into a whole new world. I was really happy with my results in the course and that was really thanks to the amazing mentors and lecturers and of course the program management. "

    Leanne Da Cerca, Senior Group Manager, MTN (South Africa)

  • "What Heena [Program Manager] give us, especially to me, is the support that I didn’t receive from any other online courses. I appreciate the constant messaging and constant checking on us, how’s our progress or any other concerns that we need to raise. "

    Fermar B Talosig, Senior Associate Engineer, NetLink Trust (Singapore)

  • "If you are interested in learning the Data Science and Business Analytics course in your own time and at your own pace, this is a great program. I recommend this because it is a course which is starting from the basics and then laying brick-by-brick to get to the final data insights. "

    Sudha Aluri, Shared Information Support Manager, Freddie Mac (United States)

  • "I like the structure of the program as it combines all the theories, practices and case studies together. This program is highly recommended as it gives you the tools to address problem solving in a efficient manner. "

    Flor de María Gómez Esparza, Ex-Compensation Leader, Pemex (México)

  • "The way you have been explaining, clearing doubts wherever possible and even encouraging, I must say that you've really done well and I want to say that I celebrate you. Thank you so much for giving your time to teach and help us in this journey. "

    Moeti Manoto, Service Manager, Openserve (South Africa)

  • "In the hackathon in this program, first thing is, I have learned speed. From the same dataset different models, trying that was a new thing. "

    Sruthi Boojala, Software Engineer, TEKsystems Global Services India Pvt. Ltd (Singapore)

  • "The most important thing what I thought from this program is this is a very good combination of Data Science and Business Analytics. Every week we will be having a quiz, based on the topic that we covered, which is a rigorous learning process. So this actually is very helpful to learn and get in touch. "

    Mohammad Tahmid Bari, Data Analyst II, Expeditors Singapore Pte Ltd. (Bangladesh)

  • "It was a pleasure to deal with all of you, right from the coordinators to the mentors. Special thanks to the doctors who have given valued information about Data Science and Business Analytics. "

    Mohamed Shafik Sabry, Solution Architect, GBM - IBM (United Arab Emirates)

  • "Thank you for how you tried to impart knowledge to us. Thank you for all the time you spend, the resources, the efforts you put into ensuring we have a full grasp of everything you pass across to us, you are truly appreciated. "

    Seun Lawal Anako, Senior Manager, Business Compliance, American Tower Company (Nigeria)

  • "It's quite interesting because I can see my performance and also other's performances. The competition is some kind of motivation for me to work better and to see whether I can beat the others. "

    Low Yu Ning, Product Engineer, Micron Semiconductor (Singapore)

  • "It's quite motivating to see the name on the leaderboard in the hackathon. Don't try to think too much about the model, think about the data. Data cleanup and understanding the data is the most important thing. "

    Sharat Kishore, Training & Development- Technical Instructor, Schlumberger (UAE)

Program Fees

Starting at 219 USD/month*

Program Fee: 3,950 USD

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Upfront Discount:
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3,750 USD

Referral Discount:
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3,750 USD

Payment Partners

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*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.

Benefits of learning from us

  • High-quality content
  • 7 hands-on projects
  • Live mentored learning in micro classes
  • Doubt solving by industry experts
  • Flexible learning approach
  • Career support services

I'm extremely glad I signed up for the program. I definitely got what I wanted from the program and strongly recommend it.

Javier R. Olaechea

Javier R. Olaechea

Data Solution Integration Advisor ExxonMobil

Admission Process

Our admissions close once the requisite number of participants enroll for the upcoming batch. Apply early to secure your seat.

  • steps icon

    1. Fill application form

    Apply by filling a simple online application form.

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    2. Interview Process

    Go through a screening call with the Admission Director’s office.

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    3. Join program

    Selected candidates will receive an offer letter. Secure your seat by paying the admission fee.

Batch Start Date

  • USA & Canada · To be announced

    Admissions Open

  • All other regions · To be announced

    Admissions Open

Frequently asked questions

Program Details

What is unique about the PGP-DSBA Program?

The PGP-DSBA program is unique in the following aspects:
  • Personalised mentored learning in small groups of up to 15 learners
  • Covers industry-relevant topics in depth, with hands-on applications and case studies
  • Provides hands-on exposure to tools such as Python, Tableau and Advanced Excel. Datasets are also provided
  • Experiential learning projects at the end of every module enable candidates 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 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 successful completion, participants earn a certificate of completion from The University of Texas at Austin.
 

What is meant by mentored learning?

Participants are guided through a unique mentored learning process that happens in a micro-class of 20-22 learners, and guided by a senior industry mentor. These are live sessions with two-way audio and video interaction and take place on weekends.

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 completely an 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 group of 20-22 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?

Candidates can access all the necessary learning material online through the Learning Management System.

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.

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, which requires candidates to use and apply the concepts learned across all the different modules in these projects.

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?

  • Python
  • Tableau

What is the ranking of UT Austin analytics programs?

The McCombs School of Business at the University of Texas at Austin is ranked No. 6 in the world by QS Business Analytics Ranking, 2021.

What types of projects and hands-on experience are included in the Business Analyst courses?


The projects include real-world projects and case studies. You'll work on data analysis, predictive modelling, machine learning, and more, utilizing tools like Python, R, and Tableau, providing a practical understanding of the concepts.

Is this program considered the best Business Analyst course for career changers?


Yes. This program is often considered one of the best Business Analyst courses for those looking to change or advance their careers. Its comprehensive curriculum, industry-aligned projects, and connection with top-tier faculty make it suitable for those new to the field or seeking to upgrade their existing skills.

What are the job prospects after completing this Business Analyst course online?


Graduates of this course can explore career opportunities in various domains such as finance, healthcare, retail, technology, and more. Typical roles include Data Analyst, Business Analyst, Data Scientist, and Business Intelligence Specialist. The program's strong reputation and industry connections significantly enhance job prospects.

Can I interact with UT Austin faculty during the Business Analyst course online?


Yes. Participants will have the opportunity to interact with UT Austin faculty. Regular classes, Q&A sessions, and one-on-one mentorship opportunities ensure an engaging learning experience.

How is the business analyst course structured?


The business analyst course combines online sessions, tutorials, hands-on projects, mentorship from industry experts, and career support. This blended approach ensures both theoretical understanding and practical application of data science and business analytics skills. In addition, there is an optional 6-week module that covers Microsoft Power BI, where learners can master the tool for data analysis.

What is a Business Analyst Course?

A business analyst course is a structured educational program designed to teach individuals the skills, knowledge, and techniques required to become effective business analysts. These courses focus on various aspects of business analysis, including requirements gathering, process modelling, data analysis, and communication with stakeholders. Participants learn how to identify business needs, analyze and interpret data, and propose solutions to improve the efficiency and effectiveness of business operations.
 

The Business Analyst course curriculum typically covers topics such as:
 

  • Introduction to business analysis: This includes understanding the role and responsibilities of a business analyst, as well as the importance of business analysis in organizations.
     

  • Requirements elicitation and management: Participants learn various techniques for gathering, documenting, and validating business requirements from stakeholders.
     

  • Business process modelling: This involves the use of diagrams and flowcharts to represent and analyze business processes, identifying areas for improvement.
     

  • Data analysis and interpretation: Students learn to analyze and interpret data using tools and techniques such as spreadsheets, databases, and data visualization software.
     

  • Solution evaluation and validation: This includes assessing the feasibility and effectiveness of proposed solutions, as well as validating them against the original requirements.
     

  • Communication and stakeholder management: Participants develop the ability to effectively communicate with various stakeholders, including managers, team members, and clients.
     

  • Tools and techniques: Students are exposed to different business analysis tools and methodologies, such as SWOT analysis, PESTLE analysis, and Agile methodologies.
     

Business analyst courses can be found in various formats, such as online or in-person classes, part-time or full-time programs, and short-term workshops or long-term certification courses. 

What is the required weekly time commitment?

Each week involves around 2-3 hours of recorded lectures and an additional 2-hour mentored learning session each weekend, which includes hands-on practical applications and problem-solving. The program also involves around an hour of practice exercises or assessments each week. Additionally, based on your background, you should expect to invest 2 to 4 hours every week in self-study and practice. So, that amounts to a time commitment of 8-10 hours per week.

Will I receive a transcript or grade sheet after completion of the program?

The Post Graduate Program in Data Science and Business Analytics is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. You will receive the grade sheet post-completion; however, the program does not carry any credits. Also, your performance will be assessed through individual assessments and module completion to determine your eligibility for the certificate.

Upon completing all the modules in accordance with the qualifying requirements for the program, you'll receive a certificate from the University of Texas at Austin.

Admissions and Eligibility

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 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 +1 512 647 2647 and we shall guide you through the process.

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.

Why Data Science and Business Analytics

What is the future of Data Science and Business Analytics?

Data has become highly influential. Data is the future. The domain of Data Science is already observed to create wonders for most of the industries it is being applied to. Data Scientists are creating a significant impact and are causing a great revolution.

 

The scope of Data Science is far-reaching. The demand and need for data scientists are increasing exponentially as days pass by. This demand is rising at a rapid pace and is expected to grow even more in the future. Hence many professionals are seeking a career transition into the field of Data Science.

 

Business Analytics is another exciting technological domain that has gained utmost popularity in recent times. Business Analytics and Data Science are employed together for the best outcomes. 

 

The technologies of Data Science And Business Analytics are also believed to take over most of the existing job roles. Hence to build a secured career taking up a data science and business analytics course has become a reliable choice. The applications of data science are delivering the most accurate and reliable results. Data Science and business analytics are also applied in solving most complex business problems. 

 

The applications of Data Science are already being applied to many fields such as gaming, robotics, healthcare, marketing, finance, and more. Data Science and Business Analytics are assumed to extend their territories to several other fields and build new dynamics. 

 

These domains also offer one of the highest-paid job roles across the globe. Hence, choose the best data science and Business analytics certificate courses to fit into the best job roles of the 21st century.

 

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

 

The job opportunities in this field are captivating, attracting both young professionals and career changers to pursue a career as Data Scientists or Business Analysts. Online courses in Data Science and Business Analytics have become popular among those seeking to upskill and enter this rapidly growing industry.

Let us look into a few of the major job positions of Data Science and Business Analytics.

1. Data Analyst

2. Data Architect

3. Statistician

4. Business Analyst

5. Database Administrator

6. Data Engineer

7. Data Scientist

Pursuing a data science and analytics course would aid you get into one of the above-mentioned job roles.

 

What are the differences between Data Science and Business Analytics?

Many believe that Data Science and Business Analytics are the same. Many also use these terms interchangeably. Below are a few differences between Data Science and Business Analytics. These two domains when combined together perform wonders. Hence many are desiring to pursue a Data Science and Business Analytics online course.

  • Data Science is the fundamental application of various machine learning methods and ideas to derive significant insights from raw data while Business analytics deals with the collection of data and evaluates the collected data towards accomplishing the business goals.
  • Data Science focuses on problem-solving whereas Business Analytics focuses on decision making.
  • Data Scientists strives to find the reason for driving those trends and Business Analysts aims to discover the trends of the data
  • Data Science applies a lot of coding practices while Business Analytics does not demand programming skills.
  • Data Science implements algorithms, statistics to derive insights from data and Business analytics employs the statistical analysis of structured data.
  • The ultimate agenda of Data Science is to pose questions and understand the analyzed collected data whereas Business Analytics renders reliable solutions to specific business problems.

Learn more about these technologies by taking up a data Science and business analyst course online.

What is the difference between Data Science and Data Analytics?

Data analytics is another interesting domain that is driving interest in many to pursue data analytics courses. Data Science and Data Analytics are two different terms as well as domains that are often referred to as one. Nevertheless, there exists a lot of differences between these two terms. If you are keen about pursuing a data analytics course, it is crucial to learn the differences between Data Science and Data Analytics.

Below are the differences between Data Science and Data Analytics.

  • Data Science is the integral application of several machine learning techniques and concepts to extract meaningful insights from raw data and Data Analytics refers to the analysis and classification of the patterns of the information collected to derive the best conclusions that aids in meeting business goals.
  • Data science detects patterns in the existing data while Data Analytics is used to sort data to fulfill the organizational needs.
  • Data Science has a macro-level scope. However, the scope of Data Analytics is micro.
  • The ultimate agenda of Data Science is to ask questions whereas Data Analytics aims to find the perfect and actionable data.
  • Data Science focuses on problem-solving while Data Analytics concentrates on decision making
  • Data Science exercises Mathematics, Statistics, and Programming Skills. But, Data Analytics applies qualitative and quantitative techniques.
  • Data science is implemented in technological domains such as Artificial Intelligence, Machine Learning, and many more while Data analytics is applied in e-commerce, gaming, and other sectors to resolve the issues associated with data.
  • Data Science predicts the future whereas Data Analytics provides a day-to-day analysis of the data.

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

The domain of Data Science is applied in many industries.The applications of Data Science and Business Analytics  are not limited to the IT sector. Hence, many are seeking the best business analytics courses online to get into the most exciting job roles. We all are witnessing the hand of data science which we never knew.

Below are a few of the applications of Data Science and Business Analytics offered to different domains

1. Internet Search

Data Science and Business Analytics  plays a vital role in displaying the most accurate results for internet search queries. Data Science is an integral technology used by most search engines such as google, bing, opera, and more.

 

2.Speech Recognition

One of the applications of Data Science is speech recognition. Data Science and Business Analytics are employed to understand the voice notes and produce accurate outcomes. Siri, Alexa, Google voice assistant, and more are a few examples that exercise data science to regulate speech recognition services. 

 

3. Targeted Advertising

Data Science promotes target advertising. Data science algorithms are rapidly employed in digital marketing to identify the target audience and advertise accordingly. Data Science and Business Analytics plays a major role in increasing the CTR( call through rate). These targets are made by studying the user's past behavior patterns.

4. Recommendations

Data Science is employed to make the best recommendations of the products and services of the e commerce websites. Amazon, Flipkart, Spotify, Netflix, and more employ data science to show the best recommendations to its users. This enhances the user's experience as they encounter a personalized shopping experience.

 

Why should you choose Data Science and Business Analytics as a career path?

Out of the many, below are a few attributes of Data Science that make it the best technological domain to work for.

1. Data Science and Business Analytics are high in demand

Data Science and Business Analytics are the two domains which have got a great demand. Even as many industries are adapting Data Science and Business Analytics at a rapid pace, there is a significant rise in demand for these technologies.

2. Offers highest-paid career roles

The job roles offered in these domains are considered highly prestigious. These job roles are also observed to be one of the highest-paid ones across the country and abroad.

3. A huge scope

Data Science and Business Analytics are technologies that are not limited to the IT sector. Data Science and Business Analytics have been embraced by most industries such as Healthcare, Gaming, Social Media, Digital Marketing, Agriculture, and many more.

4. The most secure domains.

Even as we live in a world where technological upgrades happen every single day, Data Science and Business Analytics offers secured job roles. Data Science and Business Analytics are expected to take over many of the existing job roles. Hence it is crucial to consider a career that stands tall and secured. Data Science and Business Analytics are surely the domains that offer the most secured job roles.

 

What are the various industries that employ Data Science and Business Analytics?

There are a lot of industries that are already employing Data Science and Business Analytics for the unbelievable benefits they offer. The list of the industries/ domains adapting Data Science and Business Analytics is increasing rapidly. Considering this, the demand for Data Science and Business Analyst online courses is increasing day by day as many are aspiring to get into these job roles.

Business

Data Science and Business Analytics are employed to derive the best business solutions and solve complex business problems. Data Science and Business Analytics are rapidly being applied in businesses as they are observed to serve great benefits such as predict the most accurate outcomes, assessing business decisions, formulating effective business strategies, leveraging the data, and more.

Agriculture

Data Science and Business Analytics are assisting the farmers by offering them many benefits such as weather prediction, analyzing the soil, pest control, disease detection, recommendation of the best fertilizers, and more.

 

Gaming Industry

Data Science and Business Analytics are applied in designing games. These technologies are helping the designers in designing the games that keep the user engaged and enthusiastic. The data science algorithms study the user's moves and give a tough competition which makes the game more exciting.

 

Robotics

Data Science and Business Analytics are employed in designing robots. The tools and techniques of Data Science and Business Analytics are applied to create the most intelligent robots. As Data Science is used in Artificial Intelligence and Machine Learning, it stands in the gap to design many technological innovations.

Apart from the above-mentioned industries, these technologies are also used in many other industries such as E commerce, the financial sector, education, and more.

 

What is Data Analytics Course?

A data analytics course helps students gather, process, analyze, and understand data to gain insights to help them make better decisions. This course covers various aspects of data analytics, such as predictive modelling, statistical analysis, and data visualization. Data analytics courses commonly cover the following subjects:
 

  • Data cleaning, managing missing data, and data transformation methods are all part of data exploration and preprocessing.

  • Basics of probability distributions, hypothesis testing, and descriptive and inferential statistics comprise statistical analysis.

  • Data representation in graphs, charts, and other visual forms.

  • Introduction to supervised and unsupervised learning methods, model assessment, and model optimization in machine learning.
     

Data analytics courses are offered in various forms, such as online or in-person classes, part-time or full-time programs, and short-term workshops or longer certification courses. Upon completion, participants can apply these skills across various industries, such as marketing, finance, healthcare, and more.

Fee & Payment

What is the refund policy?

Please note that submitting the admission fee does constitute enrolling in the program and the below cancellation penalties will be applied:

 

1) Full refund can only be issued within 48 hours of enrollment


2) Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded. 


3) Fee paid in excess of the admission fee: 

  1. Refund or dropout requests requested more than 4 weeks before the Commencement Date are eligible for a full refund of the amount paid in excess of the admission fee

  2. Refund or dropout requests requested more than 2 weeks before the Commencement Date are eligible for a 75% refund of the amount paid in excess of the admission fee

  3. Refund or dropout requests requested more than 24 hours before the Commencement Date are eligible for a 50% refund of the amount paid in excess of the admission fee

  4. Requests received after the Commencement Date are not eligible for a refund. 

  5. Cancellation must be requested in writing to the program office.

Still have queries? Let’s Connect

Get in touch with our Program Advisors & get your queries clarified.

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

career guidance

Data Science and Business Analytics Online in the USA

Data is a multidisciplinary field primarily based on facts, trends, and statistical figures. In the emerging scope of data, Data Science is amongst the most prominent attributes of every sector and business operations as it is helpful in getting relevant insights and making business decisions based on it.

There has been a drastic technologic shift in a couple of years which results in the creation of huge amounts of data every single day. In a 2018 article, the World Economic Forum claimed that by 2025, over 463 exabytes of data will be created globally every day, the equivalent of 212,765,957 DVDs per day.

Data science mainly focuses on using the strategies to extract and dissect data specific to definite domains or sectors. It is more about giving targeted solutions rather than a quality solution applicable to all business operations.  We can observe its use and functions in various sectors like healthcare, education, finance, retail, etc. With the help of its techniques, healthcare has found better solutions to take care of the patients, the education sector is providing better opportunities to students, the banking industry is more focused on providing the best customer service, and more.

 

What is Data Science?

The process of analyzing data and extracting useful insights from it to use it in the growth of businesses is Data Science.

It is not just a single term to understand. One needs to know the other various steps involved in the process, such as:

1. Business Requirements

It only makes more sense to collect data once you know where, how, and for whom it has to be used. Understanding business requirements is key as it helps companies to get critical information that can be used to fulfill their goals and objectives.

 

2. Data Collection

As per a recent study, over 2.5 quintillion bytes of data is generated every single day. The number is expected to grow exponentially with more businesses opting to capture and collect user data. It is important for businesses to understand the type of data to be collected.

 

3. Data Cleaning

Collecting data is not complete until it goes through check processes to retard any unnecessary data. This step is important in terms of reducing the complexity and also making sure that the collected data is efficient for the analysis.

 

4. Data Exploration and Data Analysis

Sometimes, there’s a need to employ data science tools to get the right understanding of the patterns of data. This process is called Data exploration. You can perform data analysis effectively only when you understand the patterns properly.

 

5. Data Modelling

The stage of Data Modelling is considered to be the most crucial one in Data Science. Based on all the insights and trends derived from data analysis and exploration, a predictive model is created. A lot of tests and training happens with this model to make sure it predicts precise outcomes from the data being input.

 

6. Data Validation

This is the stage of validation when the predictive model built in the previous stage is tested rigorously. The certain predictions that are produced from the model are then compared with the previous data and outcomes to judge the efficiency of the model. This stage is basically to test the model and find out all the existing errors, false predictions, inappropriate outcomes, and many more, only to work on them and fix the issues at the earliest. The model goes through several testing stages to improve the preciseness of outcomes.

 

7. Deployment and Optimization

The final stage of data science mainly involves the deployment of the model to the client and then seek feedback. Based on the feedback,  the data scientists rework the errors and improvements after which it is good to go. That’s the whole process of data science.

 

Applications of Data Science Technology

The impact of Data Science Application is non-negligible in almost all domains. Businesses are embracing it at all given points to accelerate the growth. There are so many factors that have contributed to the rapid growth of Data Science and its use across the globe.

Here we list out a few industries and the benefits they are getting from the process of data science and its implications.

1. E-Commerce

E-commerce is one field that is not only applying Data Science but is growing immensely with its implications. A lot of e-commerce websites such as Amazon are using this process to make the most recommendations to the customers when they are surfing the website based on their search histories. It improves the entire user experience while shopping.

 

2. Fraud Detection

Financial sectors have a huge demand for Data Science in managing the indefinite financial transactions happening every other minute. The need of detecting fraudulent activities is real in this sector and hence the use of Data Science is important in sectors like banking, credit cards, loans, and many more. It is the ultimate solution to secure financial transactions and predict fraud.

 

3. Self-Driving Cars

We all know that Self-driving cars are the biggest example of functioning with the use of data. The designing of cars to perceive things on road well in advance and then moving according to the GPS is all because of strategizing the data properly.

 

4. Virtual Assistance

The current trending virtual voice assistants are all a result of the processes of Data Science. Gadgets like Google Home, Siri, Alexa, and many more, which have brought so much ease to our lives are all a result of correct outcomes of data. In fact, it has even replaced customer executive roles these days with a chatbot which is extremely quick and responsive at the same time.

Hence, it has become quite important to learn and data and its techniques as it might replace many existing jobs in the coming times.

 

What are the job roles offered to the candidates pursuing a Data Science Course Online in the USA?

Undoubtedly, all the job roles being offered in this domain are in high-demand all over the world. There’s a huge demand for data scientists and analytics professionals. Hence, many people are looking out for various Data Science Programs Online in the USA.

1. MIS Reporting Executive

2. Data Scientist

3. Data Analyst

4. Data Engineer

5. Statistician.

6. Data Architect

7. Machine Learning Engineer

 

Business Analytics

  • Business Analytics is a broader domain that combines Artificial Intelligence, Machine Learning, and Big Data Analytics. However, all these terms have different meanings depending on the usage.
  • The process of applying data and quantitative analytical techniques for deriving useful insights and making decisions based on them is known as Business Analytics. Data Science and Business Analytics are fairly interrelated to each other. Both the technologies are at the peak and have a huge scope in the 21st century.
  • Under the domain of Business Analytics, there are mainly three types of analytics that are applied to get the desired results.

 

Descriptive Analytics

Descriptive Analytics as the name says derives insights by analyzing the historical data. It is one of the most important analytical techniques to perform advanced and complex analysis of data.

 

Predictive Analytics

Predictive Analytics is highly useful to derive reliable conclusions based on predictive models that are built to identify risks and connect data with effective actions.

 

Prescriptive Analytics

Prescriptive analytics is the stage after predictive analytics. It is built on predictive capabilities. It mainly includes the application of logical and statistical techniques to derive the most preferred insights. 

 

The Demand for Data Science and Business Analytics Online Courses in the USA

In terms of science and technology, the United States of America is recognized as one of the most powerful and advanced countries in the world. The country has seen some amazing technological breakthroughs in every sector of businesses such as Healthcare, Physics, Engineering, Biotechnology, Telecommunications, and many more.

Looking at the growing pace of the technological industry, there is a huge demand and supply gap of Data Science and Business Analytics professionals in and across the United States. As and when more and more businesses start to adopt the data science route in the coming future, the demand will only increase, and thus learning and pursuing Data Analytics courses online in the USA is the need of the hour.

According to some statistics, there are 24000+ job opportunities available in the United States of America for the candidates pursuing a Data Science and Business Analytics course online.

So, don’t let this time slip through your hands. Grab this opportunity and enroll yourself in an online data science degree program or the best data science certification online.

 

Best Data Science and Business Analytics Online Courses in USA

There are plenty of institutes across the country that offer online courses in data science and business analytics, but not all provide the same learning outcome. While deciding upon a course that fits your requirement and expectation, it is important to keep in mind the objective and delivery of the course. Working professionals looking to transform their career in the first of Data Science and Business Analytics should look at mentored learning models, where a learner can interact with industry experts in a small batch and discuss real-life case studies for easy understanding of topics. 

Rated among the best Data Science and Business Analytics online course, PGP-DSBA by UT Austin offers interactive mentored learning that provides collaborative as well as personalized learning.

 

Why choose Great Learning for PGP-DSBA online course in the USA?

1. Ranked #4

The Data Science and Business Analytics Online Program offered by the McCombs School of Business at The University of Texas at Austin has been ranked number 4 in the QS Business Analytics Ranking 2020.

 

2. Curriculum

The comprehensive program is designed to build expertise in the most widely-used analytics tools and technologies. The curriculum will help you learn concepts with real-world case studies by the faculty of UT Austin and the experienced industry leaders. This enables learners to get hands-on training and experiences in leading tools like R, Python, Tableau, Machine Learning and many more.

The live mentored sessions taken by the Industry Professionals of Data Scientists and Business Analytics are a major part of the curriculum that helps learners get a complete understanding of concepts.

 

3. Flexibility

The PG Program in Data Science and Business Analytics provides access to learners to learn at their comfort.  With Great Learning’s proprietary learning management system (LMS) Olympus, learners can access course content with one single login that works on all the platforms. For professionals with a busy schedule, they can watch recordings of missed lectures at their convenience. The mobile app provides notifications on quizzes and assignments to keep learners updated and motivated throughout the program journey. The mentored learning sessions are kept at a time convenient to the cohort. 

 

4. Faculty

Learn Data Science and Business Analytics from the top faculty of The University of Austin at Texas. The course is designed by the leading faculty from UT Austin, ensuring widely-used analytics tools and technologies are a part of the curriculum. The mentors for the program are leading industry experts with a strong practical understanding of core concepts of data science. 

 

5. The Expertly-Designed Program

  • brings esteem academicians and industry experts to help you develop the ability
  • to independently solve business problems using analytics and data science.

Here are a few faculty profiles for your reference:

 

Dr. Kumar Muthuraman

Faculty Director, Center for Research and Analytics, McCombs School of Business,

The University of Texas at Austin. H. Timothy (Tim) Harkins Centennial Professor.

M.S & Ph.D., Stanford University.

 

Dr. Abhinanda Sarkar

Academic Director, Great Learning

B.Stat & M.Stat, Indian Statistical Institute. Ph.D. Stanford University.

 

5. Career Assistance

With dedicated career support, the online data science and business analytics program offers personalized 1:1 Career coaching and preparation for interviews. The career support team also provides assistance in building a resume to help you get the desired job. Learners can use their industry-ready portfolio, e-Portfolio to showcase their projects, skill, and tools.

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