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With On-Campus Immersion in Decision Science and AI (Optional)
4.71 ( 10718 Ratings )
Application closes 5th Dec 2024
Curriculum with cutting edge tools and skills
Interactive mentor-led sessions
Personalized projects as per your industry
Learn from top UT Austin faculty
40+ Case Studies
24*7 Dedicated Program Support
in MS in Business Analytics
in Executive education custom programs
The Post Graduate Program in Data Science and Business Analytics is designed for professionals who want to transition their careers into data science and for aspiring data scientists. Unlike any other program, this online data science certificate program offers
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In the 3-day immersive on-campus program you can:
with like-minded AI professionals
in On-Campus Learning for 3 Days
Leadership Skills
Intelligent Decision Science Systems
Dive deeper into the world of Artificial Intelligence and unlock advanced skills in deep learning.
Understand neural networks.
Master CNNs for image classification.
Learn NLP and transformers.
Earn PGP-AIML and PGP-DSBA certificates.
Elevate your career with our comprehensive data science and business analytics course, tailored to nurture the modern business analyst. The curriculum has been designed by the faculty at the University of Texas at Austin. This sought-after course in business analytics encompasses modules such as Data Science Foundations and Techniques, offering deep Domain Exposure and empowering learners with Visualization and Insights tools.
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Data Science and Business Analytics Foundations
The Foundations module is designed to equip you with essential statistics, Python, and business domain skills to establish the groundwork for the remainder of the course. It serves as an introduction to Data Science, and completing this course will give you the confidence to discuss related concepts.
Pre-work
This covers the prerequisites needed to begin the online Data Science and Business Analytics program and includes the basics of programming with Python.
Module 1: Python Foundations
Embark on a data-driven journey with our Python Foundations Module. Learn to read, manipulate, and visualize data using popular Python packages, enabling you to tell compelling stories, solve business problems, and deliver actionable insights with ease.
Grasp the simplicity and readability of Python's syntax as you explore variables, data structures, conditional and looping statements, and functions. Build a robust skill set in Python essentials for effective coding and data organization.
Explore crucial tools in Data Science—NumPy and Pandas. NumPy excels in mathematical computing with arrays and matrices, while Pandas, an open-source library, provides speed and flexibility for data manipulation and analysis. This module deep-dives into these essential libraries, equipping you to adeptly read, manipulate, and derive insights from data in the realm of Data Science.
This module focuses on Matplotlib and Seaborn. Matplotlib, a dynamic library, enables static and animated visualizations, while Seaborn, built on Matplotlib, enhances data visualization in Python. This module provides an in-depth exploration of these tools, empowering you to create impactful visualizations that effectively summarize and communicate insights from diverse datasets.
Explore the depths of Exploratory Data Analysis (EDA), unraveling data patterns and extracting meaningful insights using Python. Acquire the skills to inform strategic business decisions based on the comprehensive analysis of data.
Module 2: Business Statistics
Elevate your analytical skills with the Business Statistics module. Harness the power of Python to assess the reliability of business estimates through confidence intervals and hypothesis testing. Make informed decisions by analyzing data distributions, ensuring precision in resource allocation and strategic commitments.
Delve into the core of statistical analysis. Gain a comprehensive understanding of probability distributions, essential for making statistically-sound, data-driven decisions. Master the fundamentals to draw conclusions about populations based on samples.
Uncover the intricacies of estimation, determining population parameters from sample data, and master the art of hypothesis testing—a framework for drawing meaningful conclusions. Delve into essential concepts like the Central Limit Theorem and Estimation Theory, providing a solid foundation for robust statistical analysis in decision-making.
Gain proficiency in hypothesis tests, essential for validating claims about population parameters in Data Science. This module introduces the most commonly used statistical tests, equipping you to choose the right test for business claims based on contextual nuances. Explore practical implementations in Python through real-world business examples, ensuring a comprehensive understanding of statistical testing in the Data Science realm.
Techniques
This program's Techniques module will give you a solid foundation in the most widely-used analytics and data science techniques. This will enable you to approach any business problem with confidence and ease.
Module 3: Supervised Learning - Foundations
Uncover the power of linear models in deciphering relationships between variables and continuous outcomes. Validate models, draw statistical inferences, and gain invaluable business insights into the key factors shaping decision-making.
Gain insights into Machine Learning, a subset of Artificial Intelligence, dedicated to pattern recognition and predictive analysis without explicit programming. This module specifically delves into the fundamentals of learning from data, the mechanics of the Linear Regression algorithm, and practical aspects of building and evaluating regression models using Python.
Explore the critical facets of Linear Regression with our module on Assumptions and Statistical Inference. Gain insights into the essential assumptions that validate the model statistically. This module guides participants through understanding, checking, and ensuring the satisfaction of these assumptions. Learn how to address violations and draw meaningful statistical inferences from the model's output, ensuring a robust and reliable application of Linear Regression in data analysis.
Module 4: Supervised Learning - Classification
Master classification models to discern relationships between variables and categorical outcomes, extracting vital business insights by identifying key decision-making factors.
Module 5: Ensemble Techniques and Model Tuning
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)
Domain Exposure
Explore a variety of real-life challenges in the Self-Paced Domain Exposure module. Learn how to apply data science and analytics principles to solve diverse problems at your own pace, gaining valuable insights and skills tailored to your schedule.
Introduction to Data Science
Gain an understanding of the evolution of Data Science over time, their application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data driven solution.
Pre-Work
Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build Data Science applications
Data Visualization in Tableau
Read, explore and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards
Time Series Forecasting
Learn how to describe components of a time series data and analyze them using special techniques and methods for time series forecasting.
Model Deployment
In this course, you will learn the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.
Marketing and Retail Analytics
Understand the role of predictive modeling in influencing customer behavior and how businesses leverage analytics in marketing and retail applications to make data-driven decisions
Finance And Risk Analytics
Develop a deep appreciation of credit and market risk and understand how banks and other financial institutions use predictive analytics for modeling their risk
Web and Social Media Analytics
Understand and appreciate the most widely used tools of web analytics which form the basis for rational and sound online business decisions, and learn how to analyze social media data, including posts, content, and marketing campaigns, to create effective online marketing strategies.
Supply Chain and Logistics Analysis
Get exposed to the discipline of supply chain management and its stakeholders, understand the role of logistics in businesses and supply chains, and learn methods of forecasting prices, demand, and indexes
On-Campus Immersion in Decision Science and AI (Optional Paid Program)
The Decision Science and AI is a 3-day on-campus Program that presents a valuable opportunity to explore AI use cases and become a driving force behind AI-driven initiatives within your organization. It comprises of dynamic discussions, collaboration with like-minded professionals, and engaging networking sessions hosted at the prestigious University of Texas at Austin.
Day 1
Day 2
Day 3
AI With Deep Learning (Optional Paid Program)
Introduction to Neural Network
This course is designed to provide you with a comprehensive understanding of Deep Learning, specifically Artificial Neural Networks. These networks consist of multiple hierarchical levels and serve as fundamental building blocks for knowledge discovery, application, and prediction from data. Through this course, you will gain expertise in effectively applying Artificial Neural Networks to real-world scenarios.
Introduction to Computer Vision
Gain expertise in leveraging Convolutional Neural Networks (CNNs) to empower computer systems with visual perception and comprehension. This program equips you with the skills to effectively process and utilize image data for business applications.
Introduction to Natural Language Processing
This course will explore the fascinating application of Neural Networks in enabling computers to comprehend human language. Specifically, you will learn how to analyze text data and determine its underlying sentiment.
Data sets from the industry
Explore a wide range of hands-on projects as part of your data science course to gain practical experience and enhance your skills.
Python Foundations
Data Analysis for Food Aggregator
Business Statistics
A/B Testing for News Portal
Supervised Learning Foundations
Dynamic Pricing Model for Devices Seller
Supervised Learning Classifications
Classification Analysis for Hotel Bookings
Ensemble Techniques
Visa Approval Prediction with ML
Unsupervised Learning
Stock Clustering for Portfolio Diversification
SQL Functions
New Wheels Data Analysis
Become a data scientist
Dive into UT Austin’s top-rated Data Science course & master essential skills for a data-driven future.
Python
Tableau
Matplotlib
Seaborn
NumPy
Pandas
Upskill from UT Austin
Enhance your resume with a certificate in Data Science and Business Analytics & share it with your professional network
* Image for illustration only. Certificate subject to change.
QS World University rankings, 2022
Custom Programs by Financial Times, 2022
For any feedback & queries regarding the program, please reach out to us at MSB-DSBA@mccombs.utexas.edu
Explore valuable learner feedback on mentorship and project management support they received while learning with UT Austin
Senior Solutions Consultant, Databricks
Enrolling in this program helped me in securing a remote position at Microsoft. The program's effective training empowered me to confidently secure a job that exceeded expectations, showcasing my abilities and enabling a successful transition. Equipped with technical skills and a problem-solving mindset, the program continues to contribute to my career success in my subsequent job roles.
Director Data Science & Advanced Analytics, Archrock
Joining the program two months into my role as Director of Data Science & Advanced Analytics, I swiftly adapted to the transformative curriculum. The experience enhanced my skills, elevating marketability and confidence. Leading data science expansion, I collaborate with IT to create predictive models and reports, applying program-acquired knowledge daily to illuminate crucial business operations.
Director of Insights and Visualizations, American Cancer Society
In 2020, I joined the program to boost my data science skills post a role in performance analytics. Diving into diverse projects, I honed expertise in business statistics and Python, shifting to Power BI for reporting. This journey, coupled with a Microsoft data analyst certificate, secured my promotion to Director of Insight and Visualizations.
Self Employed Consultant
In my pursuit of a career pivot and a deeper understanding of Data Science in Business Analytics, I enrolled in the program to supplement my on-the-job knowledge. Dissatisfied with introductory Python classes on various online portals, this program's immersive experience proved crucial for hands-on learning, playing a pivotal role in helping me complete my higher education.
BI Architect,Hach
Even before completion, this program significantly impacted my career; I secured a new job, doubling my compensation by leveraging my evolving skill set. I pursued this course once I recognized the gaps in my self-directed learning, the structured foundation provided by the professors filled these blanks, reinforcing my knowledge and the hands-on approach, bridged theory with practical applications.
I have been amazed by the professional format that Great Learning has offered. I have also been very impressed with the support provided for each student. I realized I would lose my job due to the vaccine/testing mandate. I posted my resume online and purposely listed the Data Analytics Bootcamp. After a quick interview, I was offered a new dream job with a huge company that is 100% remote. I really liked the assistance and recommend all to this program if you want to build your career in Data Science.
The learnings from this program have been invaluable in my current career. I am able to apply what I learnt in my job. Recently, an Apple Inc. recruiter messaged me on LinkedIn and offered me a position at Apple as a Data Engineer. I couldn't be luckier and am now on the AI/ML team. Apple is putting me on a project immediately and the topic is something I studied in this program.
This program was a great stepping stone to begin my career in data analytics. The program office support was top notch. They were always available to respond to my questions and remind me of quizzes and projects deadline. Sunday zoom lectures were my personal favorite part of the program. Everything I didn't understand by watching pre-recorded videos throughout the week, I knew our instructor would find a simpler and practical way of explaining the content.
I work as a Business Intelligence Developer at a local non-profit. I completed Data Science & Business Analytics program by UT Austin in collaboration with Great Learning. I have done my B.S. Marketing, M.S. Computer Science. I've worked as a programmer and systems engineer for a very long time. I took up this program to learn more about current technologies and open the door to newer opportunities. I like the methods used in the program. The recordings were well done and the weekly sessions were fantastic, thanks to our mentor,
I found the program easy to follow and understand even with the limited knowledge I had. There is a lot of assistance from the career counselor and mentor sessions are very helpful. This program has helped me build my confidence not only in the Data Science and Business Analytics world but also in myself and my potential. I would recommend this program to anyone who wants to get into the field. I hope it helps you as much as it helped me.
Post Graduate Program in Data Science and Business Analytics
Post Graduate Program in Data Science and Business Analytics
Post Graduate Program in Data Science and Business Analytics
Post Graduate Program in Data Science and Business Analytics
When you choose the University of Texas Data Science course, you get the best coaching from world-renowned faculty and industry experts
Faculty Director - Centre for Research and Analytics
20+ Years Work Experience
Dr. Kumar Muthuraman is the H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk and Operations Management and the Department of Finance at McCombs School of Business, University of Texas at Austin. He received his Ph.D from Stanford University. Dr. Muthuraman's research focuses on decision-making under uncertainty. Application areas of interest to him are quantitative finance, operations management, and health care.
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Assistant Professor, McCombs School of Business
Prof. Dan Mitchell has a Ph.D. in Financial Analytics from UT with a specialization in continuous time decision-making. He has published papers on topics such as insurance analytics, option pricing and algorithmic trading. Dan has taught courses in machine learning, simulation, as well as statistics and python for Great Learning.
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Academic Director - Data Science & Machine Learning,
Ph.D. from Stanford University, Ex-Faculty - MIT
Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs. Dr. Sarkar has contributed to formulating the program curriculum and learning content.
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Co-Founder and Director
Vivek Anand is a data visualization consultant with 10 years of experience. His area of specialization includes Marketing and Econometrics. Vivek has an MBA from Monash University Melbourne Vic. He has worked as Sales & Marketing professional handling teams of leading Indian hospitality brands across the country. His most recent assignment was for India's largest Luxury hotel by ITC hotels in Chennai. He is a qualified trainer of Tableau 9.0 and has a passion for teaching
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Meet our dedicated mentors and industry insiders guiding DSBA learners on their analytics career journey.
6 years of relevant work experience
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3 years of relevant work experience
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9 years of relevant work experience
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6 years of relevant work experience
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5 years of relevant work experience
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9 years of relevant work experience
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3 years of relevant work experience
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3 years of relevant work experience
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8 years of relevant work experience
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9 years of relevant work experience
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Engage one-on-one with industry experts for valuable insights and guidance.
Gain Insights into Recruiter Expectations.
Showcase Your Strengths Impressively
Create a Professional Portfolio Demonstrating Skills and Expertise
Program Fee: 3,950 USD
Pay in Installments
Recommendedfor 18 months
Upfront Payment & Referral
Payment Partners
*Subject to partner approval based on applicable regions & eligibility.
Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.
Apply by filling a simple online application form.
Go through a screening call with the Admission Director’s office.
Selected candidates will receive an offer letter. Secure your seat by paying the admission fee.
Program Details
What is unique about this Data Science and Business Analytics course?
Experience the best of both worlds with the PGP-DSBA program that combines the academic rigor, peer collaboration, and mentorship of a traditional course with the flexibility and convenience of an online program.
With a personalized learning approach in small groups, the PGP-DSBA takes you on an innovative hands-on learning journey through a comprehensive curriculum covering the most popular data science and business analytics tools and techniques.
With the support of experienced mentors, you'll work towards weekly milestones, sharpening your skills through real-time, interactive sessions, quizzes, assignments, and projects. And, as a working professional, you can pursue your passion without quitting your job or travelling.
What is meant by mentored learning?
Embark on a transformative learning journey with our unique mentored learning process. In a micro-class of 20-22 learners, you'll receive personalized guidance from a senior industry mentor in live sessions with two-way audio and video interaction on weekends. Experience the power of real-time learning and take your skills to new heights.
What are the benefits of enrolling in this Data Science and Business Analytics course from UT Austin McCombs School of Business?
Unlock a world of possibilities by enrolling in our top-rated Data Science and Business Analytics course. With this program, you'll gain access to a host of benefits, including:
How will Post Graduate Program in Data Science and Business Analytics help me progress in my career?
The program is specifically designed to prepare learners for a successful career in the Data Science and Business Analytics field. We understand that gaining credibility, knowledge, and practical experience is essential for securing a job, and we've worked backwards to create a program that helps learners stand out in all aspects. The certificate from UT Austin is globally recognized, giving learners credibility and recognition in the industry.
UT Austin faculty provide best-in-class recorded content, while hands-on training equips learners with practical skills. By completing multiple projects, learners create an industry-ready portfolio of work. Interacting with established practitioners and fellow learners helps build a strong professional network. The program also includes career guidance sessions, mentoring, and support for resume and LinkedIn profile reviews, interview preparation, and more.
Is Post Graduate Program in Data Science and Business Analytics completely an online program?
Yes. You'll have access to an extensive learning experience with our program, combining recorded content from top academic and industry faculty with live online micro-classes led by expert instructors in small groups of 10-15 learners. And for your convenience, all assessments will be conducted online.
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.
Which criteria will be used to assess my performance in this Business Analytics course?
Assessing student performance is a crucial aspect of our rigorous and all-encompassing Data Science and Business Analytics course. We use a continuous evaluation system that encompasses a variety of assessments, including quizzes, assignments, case studies, and project reports.
Are there any experiential projects as part of the Post Graduate Program in Data Science and Business Analytics?
This course is designed to be hands-on, with participants working on several experiential projects in Time Series Forecasting, Predictive Modeling, Advanced Statistics, Estimation & Hypothesis Testing, and Data Mining. These projects put into practice the concepts learned across all modules, ensuring participants are able to apply their newfound knowledge effectively.
Which companies do the industry mentors in the Post Graduate Program in Data Science and Business Analytics, work for?
Our industry mentors collaborate with top-tier organizations worldwide, such as Microsoft, Google, McKinsey, Boeing, HSBC, and Citigroup
What is the ranking of the UT Austin McCombs School of Business?
In the QS World University Rankings 2021, UT Austin secured 6th place worldwide for Business Analytics. With over 40 postgraduate and 15 undergraduate programs that are ranked among the top 10 in the country, UT Austin has been consistently recognized as one of the top 20 public universities by the U.S. News & World Report.
What is the curriculum for this PG in Data Science and Business Analytics from UT Austin McCombs School of Business?
The course syllabus is tailor-made to meet the needs of both recent graduates and early-career professionals. It covers a wide range of topics that equip learners with the skills and knowledge required to pursue successful careers in Data Science and Business Analytics.
What are the learning outcomes of this online Data Science and Business Analytics course from UT Austin McCombs School of Business?
Here are the learning outcomes of this online Data Science and Business Analytics course:
1. Develop your knowledge of the most popular Analytics tools and technologies.
2. Gain the ability to solve business problems independently using Data Science and Business Analytics.
3. Discover the applications and implications of Data Science in multiple industries.
4. Discover the best methods for deriving strategic business insights from data and effectively communicating them to stakeholders.
5. Create models to foresee trends, then use them to guide business strategy.
6.Create a significant body of work and an industry-ready data science and business analytics portfolio.
What projects are included in this course?
Learners will execute 8+ industry-relevant hands-on projects, which include the following:
1. Airplane Passenger Satisfaction Prediction - Marketing
2. Facebook Comments Prediction - Social Media
3. West Nile Virus Prediction - Social + Healthcare
4. Insurance Premium Default Propensity Prediction - Insurance
5. Retail Sales Prediction - Retail
6. Loan Customer Identification - Banking
7. CEO Compensation - HR
8.Insurance Data Visualization - Insurance
Who will be the faculty for this course?
Our faculty members are industry experts and academic professionals from both UT Austin McCombs School of Business and Great Learning. With years of experience in Data Science and Business Analytics, they have a proven track record of achieving great success in their respective fields.
The faculty's vast research and theoretical knowledge in Data Science and Business Analytics are crucial for your educational journey, cultivating a love for data, and preparing you for industry success.
Who are the industry mentors that offer guidance throughout the course?
The program's industry mentors are top-notch Data Science and Business Analytics professionals, with experience working in renowned companies like Amazon, Apple, Dell, and Equity. They bring valuable insights and real-world expertise to help you succeed in your career.
What certificate will I earn after completing this Data Science and Business Analytics certificate course from UT Austin McCombs School of Business?
After completing this program, you will be awarded a "Post Graduate Certificate in Data Science and Business Analytics" from The University of Texas at Austin as a testament to your hard work and dedication.
How does Great Learning fit into this course?
Great Learning, a renowned higher education and professional development ed-tech platform in India and a part of the BYJU’s group, collaborates with the UT Austin McCombs School of Business to offer learners practical, hands-on training and live personalized mentoring sessions. These sessions help learners apply concepts taught by the faculties of both UT Austin and Great Learning, ensuring an optimal learning experience.
The following is a detailed list of the services offered by Great Learning:
How will the Post Graduate Program in Data Science and Business Analytics help me progress in my career?
This program will help learners progress in their careers in the following aspects:
Moreover, the program provides personalized career guidance with industry experts, who offer support in essential job search areas, including resume and LinkedIn profile reviews, interview preparation, and more.
What job opportunities will I receive after completing this Data Science and Business Analytics course from UT Austin McCombs School of Business?
Upon completion of this course, learners will gain access to a diverse range of exciting career opportunities. The following are some of the most sought-after entry-level positions in Data Science and Business Analytics:
Data Scientist
Business Analyst
BI Analyst
Data Analyst
Data Journalist
Data Engineer
Analytics Engineer
Research Analyst
Product Analyst
Data Architect
Furthermore, upon completion of the program, learners can explore diverse career paths in Data Science and Business Analytics, including research and development or education.
What is the program structure for this course?
Experience live mentored learning and engaging webinars, all delivered online through our PGP-DSBA program. With micro-classes of up to 15 students, you'll receive personalized attention to accelerate your learning journey.
What is the duration of this PGP-DSBA from UT Austin McCombs School of Business?
In just 7 months, you'll gain practical knowledge through live mentoring, webinars, and industry-relevant projects.
Do I need to get a laptop, or will one be provided to me?
While you'll need your own laptop to participate, Great Learning will provide you with access to the technology you'll need when you sign up for the course.
What is the Data Science and Business Analytics course from The University of Texas at Austin’s McCombs School of Business?
Elevate your career with the top-tier Post Graduate Program in Data Science and Business Analytics (DSBA) from the McCombs School of Business at the University of Texas at Austin (UT Austin). Designed for recent graduates and early-career professionals, this program is your gateway to success in the rapidly evolving world of data science and analytics.
Immerse yourself in a dynamic learning experience that combines captivating lectures, interactive projects, real-time mentorship, and live webinars. This program equips you with the expertise, practical know-how, and industry insights to secure the ideal Data Science and Business Analytics job or spearhead your company's Data Science endeavors.
What skills, tools, and languages will I learn in this course?
This course covers a wide range of highly sought-after skills, tools, and programming languages, including Python, Business Statistics, Tableau, NumPy, Pandas, Matplotlib, Seaborn, Exploratory Data Analysis, and many more, giving learners the chance to become proficient in a variety of essential areas.
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.
What is the required weekly time commitment?
Participants can expect to dedicate an average of 8-12 hours per week to the program. This includes 2 hours of recorded lectures and 2 hours of hands-on sessions each week, spread across 7 weekends. Learners are also encouraged to invest additional time for self-study and practice, depending on their background and experience.
Eligibility Criteria
What are the eligibility criteria for this online Data Science and Business Analytics course?
The following are the criteria for this course's eligibility:
A Bachelor’s or Undergraduate Degree with a minimum of 50% aggregate marks or equivalent is required.
No prior programming knowledge is necessary.
Note: The Post Graduate Program in Data Science and Business Analytics is designed for working professionals seeking a career transition into analytics roles, as well as students in their final year of graduation. A graduation background in quantitative disciplines such as engineering, mathematics, sciences, statistics, economics, etc. can maximize the program's benefits.
Admission Queries
What is the admission process to enroll in this Data Science and Business Analytics course from UT Austin McCombs School of Business?
Please adhere to the following guidelines to register for this course:
Step-1: Application Form
Don't miss your chance! Apply now by filling out the online application form. As the program follows a rolling application process, we encourage you to apply at the earliest.
Step-2: Shortlisting and Panel Review
Our panel of experts will carefully review your application to assess your eligibility for the program. Your academic performance, professional work experience, and motivation levels will be taken into account.
Step-3: Interview/Screening Process
If your application is shortlisted, you may be invited to a telephonic screening interview (although those with a strong background and experience may be exempt).
Step 4: Admissions Offer
After a thorough evaluation by the admissions committee, you will be invited to join the next cohort of the program.
When is the application cutoff date for this course?
Our program operates on a rolling application process and will close once all seats have been filled. Submit your application promptly to increase your chances of being accepted.
Fee Related Queries
In relation to purchasing books, using online learning resources, or paying license fees, are there any additional costs?
All the required learning materials are included with the program, and learners can access them through our online Learning Management System at no extra cost.
Although the program provides comprehensive learning materials, our faculty understands the ever-changing and vast nature of Data Science and Business Analytics. Thus, they occasionally recommend additional resources for learners to explore and enhance their knowledge.
What is the refund/cancellation policy?
Please be informed that paying the admission fee is not equivalent to enrollment in the program, and cancellation penalties listed below will be enforced. For your reference, kindly review our policies on refunds and dropouts in case you're unable to participate in the course.
A full refund can only be issued within 48 hours of enrollment.
Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded.
The 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.
What payment options are available to pay the course fee?
Learners can pay the course fee through Bank Transfers and Credit/Debit Cards.
[For further details, please get in touch with us at dsba.utaustin@mygreatlearning.com or +1 512-793-9938]
What is the course fee to pursue this online PG Program in Data Science and Business Analytics from UT Austin McCombs School of Business?
The course fee to enroll in this PG Program is USD 3,800. Nevertheless, discounts are also available, which are as follows:
Fee after Upfront Discount: USD 3,600
Fee after Referral Discount: USD 3,600
Please get in touch with the program advisor for more details and flexible payment options.
Does this course accept corporate sponsorships?
Absolutely! We welcome corporate sponsorships and can provide assistance to participants during the application process.
[Participants can connect with us at dsba.utaustin@mygreatlearning.com or +1 512-793-9938]
Why Data Science and Business Analytics?
What is the future of Data Science and Business Analytics?
Data is the powerhouse driving innovation in industries across the board, and it's safe to say that the future belongs to those who can effectively harness its potential. The field of Data Science is already making waves with its ability to transform businesses, and the impact of Data Scientists is nothing short of revolutionary.
Data Science is a field with boundless possibilities. The need for skilled data scientists is surging, and this demand is only projected to increase with time. It's no surprise that many professionals are looking to pivot their careers towards this exciting domain.
In the world of technology, Business Analytics is an emerging domain that has garnered significant attention in recent years. Its collaboration with Data Science has resulted in the most optimal outcomes.
The fields of Data Science and Business Analytics are rapidly evolving and are predicted to disrupt various industries. Consequently, pursuing a course in these domains has become a popular choice for individuals seeking a secure and rewarding career. These technologies are also transforming traditional job roles. With accurate and dependable results, they are capable of solving complex business challenges, making them indispensable tools in the business world.
Data Science has already shown its potential in various fields, including gaming, robotics, healthcare, marketing, finance, and more. As the scope and power of Data Science and Business Analytics continue to grow, they are expected to expand into new territories and create new possibilities.
In today's world, Data Science and Business Analytics are among the most lucrative and highly paid job domains. To seize the best opportunities in these fields, enroll in the finest certificate courses available.
What are the various job roles of Data Science and Business Analytics?
The job roles offered in this domain are fascinating. Many youngsters and existing career professionals are aspiring to get into these job roles such as Data Scientists and Business Analysts. Hence many are aspiring to pursue an online Data Science and Business Analytics course.
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?
While some people use the terms interchangeably, Data Science and Business Analytics are distinct fields with unique differences. Combining these domains can lead to remarkable outcomes, making them highly sought-after areas of study. If you're looking to explore this exciting field, consider an online course in Data Science and Business Analytics.
Data Science involves applying various machine learning techniques to extract meaningful insights from raw data, whereas Business Analytics focuses on collecting and evaluating data to achieve business objectives.
Data Science is all about problem-solving, while Business Analytics centers around decision-making. Data Scientists analyze data to uncover the underlying reasons for trends, while Business Analysts aim to identify patterns in the data. Coding is a core aspect of Data Science, whereas Business Analytics doesn't require extensive programming skills. Data Science uses algorithms and statistics to extract insights from data, while Business Analytics focuses on statistical analysis of structured data. Ultimately, Data Science seeks to pose questions and understand the analyzed data, while Business Analytics offers practical solutions to specific business problems. Want to learn more about these exciting fields? Enroll in an online Data Science and Business Analytics course today.
What is the difference between Data Science and Data Analytics?
Data analytics is an intriguing field that has piqued the interest of many, prompting them to pursue courses in this domain. However, Data Science and Data Analytics are two distinct terms and fields that are frequently used interchangeably. Despite this, there are significant distinctions between these two terms that should be understood if you're interested in pursuing a data analytics course.
Below are the differences between Data Science and Data Analytics-
What are the various applications of Data Science and Business Analytics?
Data Science and Business Analytics have spread their wings beyond the IT sector and are being applied in numerous industries. To secure the most exciting job roles, many are now pursuing the best Business Analytics courses online. The power of data science is pervasive, and we are experiencing its impact in ways we never imagined.
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 are critical components in providing accurate search results for internet queries. Most search engines, including Google, Bing, and Opera, utilize Data Science as a fundamental technology.
2. Speech Recognition
Data Science and Business Analytics are revolutionizing speech recognition technology. They analyze voice data and deliver accurate results. Popular voice assistants like Siri, Alexa, and Google utilize these technologies to provide seamless voice recognition services.
3. Targeted Advertising
Data Science enables precise and effective advertising. With the use of data science algorithms, digital marketers can identify their target audience and create targeted ads. By analyzing user behavior patterns through Data Science and Business Analytics, marketers can increase their CTR (call-through rate) and achieve better campaign results.
4. Recommendations
Data Science is the driving force behind personalized product recommendations on e-commerce websites like Amazon, Flipkart, Spotify, and Netflix. By analyzing users' past behaviors, Data Science algorithms help these platforms provide tailored suggestions, creating a more engaging and customized shopping experience for users.
Why should you choose Data Science and Business Analytics as a career path?
Data Science is an incredibly rewarding field to work in, thanks to several distinguishing attributes that set it apart from other technological domains.
1. Data Science and Business Analytics are high in demand
The demand for Data Science and Business Analytics is skyrocketing as these two domains are witnessing a widespread adoption across various industries.
2. Offers highest-paid career roles
The careers in these fields are highly coveted and often offer top-tier compensation both domestically and internationally.
3. A huge scope
The domains of Data Science and Business Analytics have expanded beyond the IT sector and are now widely adopted by diverse industries like Healthcare, Gaming, Social Media, Digital Marketing, Agriculture, and more.
4. The most secure domains
In a world where technology is advancing every day, securing a job can be challenging. However, Data Science and Business Analytics are two domains that offer job security. These domains are expected to replace many existing job roles, making it essential to choose a career that is secure and promising. Data Science and Business Analytics are the domains that provide the most secure job opportunities.
What are the various industries that employ Data Science and Business Analytics?
Industries across the board are adopting Data Science and Business Analytics for their remarkable benefits. The number of domains integrating these technologies is increasing rapidly. As a result, the demand for online courses in Data Science and Business Analytics is skyrocketing, as more and more people aspire to pursue these highly sought-after job roles.
Business
Data Science and Business Analytics are powerful tools that provide businesses with the most optimal solutions and aid in resolving intricate business challenges. These technologies are increasingly being integrated into businesses due to their ability to accurately predict outcomes, evaluate business decisions, develop effective strategies, and harness the potential of data to its fullest.
Agriculture
Data Science and Business Analytics are empowering the farming industry with an array of benefits, including weather forecasting, soil analysis, pest management, disease detection, fertilizer recommendations, and much more.
Gaming Industry
Data Science and Business Analytics have revolutionized the gaming industry. By analyzing user behavior and game patterns, game designers can now create more engaging and thrilling games. With the help of data science algorithms, games can offer challenging competitions and keep users captivated for longer.
Robotics
The advanced technologies of Data Science and Business Analytics are utilized in the designing of robots. The intelligent features of robots are developed by leveraging the tools and techniques of these technologies. As Data Science is closely associated with Artificial Intelligence and Machine Learning, it plays a significant role in designing the most innovative technological solutions.
In addition to the industries mentioned earlier, Data Science and Business Analytics are also extensively used in other sectors such as finance, education, e-commerce, and many more.
Application Closes
5th Dec 2024
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The University of Texas at Austin is collaborating with Great Learning to deliver PG Program in Data Science and Business Analytics. Great Learning is an ed-tech company that has empowered learners from over 170+ countries in achieving positive outcomes for their career growth.
The need for skilled Data Science and Business Analytics professionals is skyrocketing in our dynamic world, where data reigns supreme. Big and small companies grapple with mountains of data and crave experts who can unlock valuable insights and drive data-powered decisions. Look no further than the McCombs School of Business at the University of Texas at Austin. With their cutting-edge Data Science and Business Analytics course, they're molding tomorrow's trailblazers, arming them with the knowledge and skills to thrive in this exhilarating domain.
Pursuing this program can help you meet the market demand for these skills in several ways.
First and foremost, the program provides a comprehensive understanding of the latest Data Science and Business Analytics techniques and tools. It covers Python, Tableau, Logistic Regression, Decision Trees, Generative AI like ChatGPT, etc.
The course emphasizes practical applications of Data Science and Business Analytics. Learners will be able to work on real-world projects and gain hands-on experience through interactive mentor-led practice sessions. Learners will develop a work portfolio showcasing their skills to potential employers.
It can open up various career opportunities in multiple industries, including finance, healthcare, retail, and more. Amid the demand for data-driven decision-making, companies seek professionals to help them gain a competitive edge. This course will position learners as highly skilled and valuable assets to organizations.
UT Austin was established in 1883 and presently enrols 51,000+ students and 3,000+ teaching faculty. It is well-recognized on a global scale in the areas of social science, business, technology, and science.
According to the QS World University Rankings 2021, the university is ranked 6th overall in the domain of Business Analytics. Additionally, due to its 40+ postgraduate programs and 15 undergraduate programs that are among the top 10 in the United States, UT Austin has consistently been ranked among the top 20 public universities by U.S. News & World Report.
Following successful completion of the course, students will be qualified for a range of entry-level technical job roles, such as the following:
Data Scientist: They help businesses make informed decisions by leveraging data-driven insights. The Data Scientist plays a vital role in identifying trends, uncovering patterns, and creating predictive models to help companies to optimize their operations, reduce costs, and improve customer satisfaction.
Business Analyst: They examine a company's data and processes to enhance its operational efficiency. They are responsible for creating reports and providing recommendations to management on improving the business's overall effectiveness based on their data analysis.
Data Analyst: A Data Analyst is accountable for collecting, scrutinizing, and interpreting data to identify trends, patterns, and relationships within extensive data sets. They use this understanding to assist in business decision-making.
BI Analyst: They analyze complex data sets to identify patterns and insights, create visualizations, dashboards, and reports that communicate data-driven insights, and help organizations make informed business decisions based on their findings.
Data Engineer: A Data Engineer is accountable for ensuring smooth and effective data transmission within an organization. They work with data architects to develop data models and create ETL (extract, transform, load) processes that transfer data between systems.
Data Journalist: A Data Journalist employs various Data Analysis techniques, such as statistical analysis and data visualization, to investigate and report on news stories. They use their skills to research and analyze complex data sets to uncover meaningful insights and trends that help to inform their reporting.
Analytics Engineer: An Analytics Engineer works closely with Data Scientists to understand the business problems that need to be addressed and selects the most efficient methods for collecting and processing data for organizations.
Data Architect: A Data Architect is accountable for designing and implementing an organization's data architecture, including creating and maintaining data models, implementing data management processes, and establishing data governance policies.
Product Analyst: A Product Analyst works closely with product managers and other stakeholders to assist in developing new products. Their responsibilities include analyzing product specifications and conducting market research to identify opportunities for product improvement.
Research Analyst: A Research Analyst is accountable for gathering information through primary and secondary research, examining data, and extracting insights to support decision-making. They work closely with Data Scientists and Business Analysts to understand business requirements and develop research plans that align with those needs.
Data Science and Business Analytics are highly sought-after fields that are proliferating around the world. As a result, the salaries of professionals working in these areas have steadily increased in recent years.
According to data from Glassdoor, the average salary for a Data Scientist in the United States is around $125,000 per year. However, this can vary depending on location, industry, and experience. For example, in major tech hubs like San Francisco and New York City, Data Scientists can earn upwards of $150,000 annually.
Similarly, Business Analysts can also earn competitive salaries. According to Payscale, the average salary for a Business Analyst in the United States is around $70,000 annually, with more experienced professionals earning up to $100,000 or more. Salaries can also vary depending on industry and location, with Business Analysts working in the finance and technology sectors tending to earn more.
Salaries for Data Scientists and Business Analysts fluctuate significantly based on location and industry, yet these sought-after professions promise remarkable career growth and competitive pay. With the escalating demand for data-driven decision-making in every sector, rest assured that salaries in these fields will keep ascending. Embrace the future of high-paying opportunities!
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