Post-Graduate Program in Data Science & Business Analytics

Advance your career with Data Science

With On-Campus Immersion in Decision Science and AI (Optional Paid Program)

7 Months

Online Learning with Mentorship

Application Closes 5^th Dec 2024

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97%

Program Satisfaction

4.8/5

TrustPilot

4.81/5

Course Report

Thousands of jobs enabled in Business Analyst, Data Analyst

50%

Average salary hike
3,300+

Hiring Companies

*Across Great Learning programs

Lakshmi Panchagnula

Water Programs Specialist

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

Data Analyst

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

Customer Service Administrator

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.
Osama Salem

Senior Application Manager

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

Senior Consultant - Project Delivery Specialist

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

Research Analyst / Independent Trader

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

Senior Business Analyst Lead

This program helped me gain the right mix of technical and fundamental skills to be better at my role.
Naeem Sufi

Senior Webfocus Developer

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

Get Industry ready with Career Support

1:1 Industry Interactions

Resume & Linkedin Profile Review

Interview Preparations & Demos

Online Portfolio Assessment

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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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Program Support

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

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

QS World University rankings, 2022
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

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

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

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.

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.

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.

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 - Centre for Research and Analytics 20+ Years Work Experience

Dr. Daniel A Mitchell

Clinical Assistant Professor - Department of Information, Risk & Operations Management

Dr. Abhinanda Sarkar

Academic Director - Data Science & Machine Learning, Ph.D. from Stanford University, Ex-Faculty - MIT

Prof. Raghavshyam Ramamurthy

Industry Expert in Visualization

Mr. R Vivekanand

Co-Founder and Director

Industry Mentors from Top Organisations

Prabhat Bhattarai

Data Scientist

Nikhila Kambalapalli

Consultant, Data Science

Srihari Nagarajan

Senior Data Scientist

Olayinka Fadahunsi

Head of Data Science and Engineering

Anis Sharafoddini

Data Scientist Lead

Avinash Ramyead

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

Edward Krueger

Principal Data Scientist and Proprietor

Paolo Esquivel

Senior Data Scientist

Michael Keith

Analytics Manager

Anuj Saini

Principal Data Scientist, RPX Corporation

Mohit Jain

Principal Data Scientist

Yogesh Singh

Founder and CEO, NSArrows

Rushabh Shah

Software Developer, Kyra Solutions

Roshan Santhosh

Data Scientist, Meta

Learner Testimonials

Learners review our program support & mentors

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

Lakshmi Panchagnula

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

Gabriel Arbe

Director of Operations, Latin America
Great learning experience and would definitely recommend it to anyone who is looking for a perfect program.

Aparna Bharath Kumar

Data Analyst
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.

Kati Laev

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

Osama Salem

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

Ndohnwi A Moma

Senior Consultant - Project Delivery Specialist
The course helped me by giving a peek into whether I can make a move from Finance to Data Science

Renato Barroco

Research Analyst / Independent Trader
This program helped me gain the right mix of technical and fundamental skills to be better at my role.

Sudarshan Murali

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

Naeem Sufi

Senior Webfocus Developer

Read All reviews

Program Fees

Program Fees: 3,950 USD

Apply Now

As low as 317 USD/month

for 12 months

View All Installment Plans

Upfront Discount:

3,950 USD

3,750 USD

Referral Discount:

3,950 USD

3,800 USD

Payment Partners

*Subject to partner approval based on applicable regions & eligibility.

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

Data Solution Integration Advisor ExxonMobil

Installment
Plans Full Fee
Payment Plan

Monthly Installment
Installments	Starts at
6 Months	658 USD/month
12 Months	329 USD/month
18 Months	219 USD/month

Monthly Installment available for learners from Brazil, Colombia & Mexico only.

Installments	Starts at
12 months	329 USD/month

Total Fee Payment

3950 USD

Admission Process

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

1. Fill application form

Apply by filling a simple online application form.
2. Interview Process

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

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

Limited seats available

Apply today and become a Data Science expert

Batch Start Date

USA & Canada · To be announced

Admissions Open

Apply Now
All other regions · To be announced

Admissions Open

Apply Now

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:

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
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
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
Requests received after the Commencement Date are not eligible for a refund.
Cancellation must be requested in writing to the program office.