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31st Jul 2025
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Master data science applications and secure a future-ready career
4.71 ( 10718 Ratings )
Application closes 31st Jul 2025
Program Outcomes
Use emerging technologies to drive business insights and innovation
Learn to solve complex business problems with Data Science and Generative AI concepts
Build real-world solutions using Python, LLMs, machine learning, and advanced analytics tools
Build an impressive, industry-ready portfolio with hands-on projects
Earn 9 CEUs and a certificate of completion from Texas McCombs upon completion of the program
#1 (U.S., Big Data Management) in MS Business Analytics
Eduniversal (2024)
#6 in MS - Business Analytics
QS World University Rankings (2024)
#6 in Executive Education
Custom Programs Financial Times, 2022
#6 in MS Business Analytics
The Financial Engineer Times (2024)
Key Highlights
Earn a certificate of completion from a world-renowned university
Master Python, SQL, machine learning, and GenAI techniques like prompt engineering and LLMs for real business use cases.
Work on 7 hands-on projects and 40+ case studies using tools like Python, Hugging Face, and Tableau to build a job-ready data science portfolio.
Get 1:1 personal assistance from a Program Manager to complete your course with ease.
Skills you will learn
view more
Align your learning with your professional aspirations
Mid-to senior-level
professionals looking to influence business decisions using data-driven insights
Professionals transitioning
into Data Science who want a strong foundation in analytics and Generative AI
Future-ready professionals
seeking to stay ahead in a business environment increasingly shaped by data and AI
Leaders and changemakers
aiming to implement AI-enabled strategies within their organizations
Texas McCombs Programs
Other Courses
Certificate
Certificate of completion from Texas McCombs
No university certificate
Gen AI modules
Extensive coverage of Gen AI topics
Limited coverage
Live mentored learning
Live interactive online classes with industry professionals
Limited to no live classes
Career support
Yes, with 1:1 Career Mentoring, Resume and LinkedIn Profile Review, and more.
No career support
Hands-on projects
7 hands-on projects, and 40+ case studies
Fewer projects
Dedicated Program support
Dedicated support to complete your course
Limited support
Decision Science and AI Program
In the 3-day immersive on-campus program you can:
Reach out to your Program Advisor for more details
The curriculum has been designed by the faculty at McCombs School of Business at the University of Texas at Austin.
225+ hrs
hands-on projects
9+
Case studies
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.
This module will teach some prerequisites before starting with the Data Science and Business Analytics online course like Programming concepts and Python.
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.
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.
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.
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.
Master classification models to discern relationships between variables and categorical outcomes, extracting vital business insights by identifying key decision-making factors.
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.
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.
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)
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)
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)
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)
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.
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.
Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build Data Science applications
Read, explore and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards
Learn how to describe components of a time series data and analyze them using special techniques and methods for time series forecasting.
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
Develop a deep appreciation of credit and market risk and understand how banks and other financial institutions use predictive analytics for modeling their risk
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.
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
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.
Engage in practical projects and case studies to solve real-world business problems using data and GenAI tools.
7
Hands-on projects
20+
case studies
Gen-AI Augmented
Practical Learning
Hospitality
About the Project
Predict hotel booking cancellations in advance to reduce revenue loss and optimize occupancy using classification models.
Skills you will learn
Food & Beverages
About the Project
Use GenAI and LLMs to analyze and tag customer reviews, uncovering sentiment insights at scale for better decision-making
Skills you will learn
Manufacturing
About the Project
Predict machine failures, identify key risk factors, and help manufacturing units reduce downtime and maintenance costs.
Skills you will learn
Transportation
About the Project
Predict hourly and daily demand for rental bikes to optimize fleet allocation and improve operations during peak hours.
Skills you will learn
Dive into the top-rated data science course & master essential skills for an AI-powered future.
Python
Tableau
Matplotlib
Seaborn
NumPy
Pandas
Get a certificate from one of the top universities in USA and showcase it to your network
* Image for illustration only. Certificate subject to change.
When you choose the data science with generative AI program by the McCombs School of Business at The University of Texas at Austin, you learn from leading academicians in the field of Data Science and Engineering.
Faculty Director, Center for Analytics and Transformative Technologies, McCombs School of Business, the University of Texas at Austin
Faculty Director, Center for Analytics and Transformative Technologies
21+ years' experience in AI, ML, Deep Learning, and NLP.
Clinical Assistant Professor, Department of Information, Risk & Operations Management, McCombs School of Business, The University of Texas at Austin
Research Director, Center for Analytics and Transformative Technologies
15+ years of experience in financial engineering and quantitative finance.
Senior Faculty & Director Academics, Great Learning
30+ years of experience in data science, ML, and analytics.
Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
Industry Expert in Visualization
Co-Founder and Director
Expert in data visualization and marketing econometrics with 10+ years
Qualified Tableau trainer passionate about teaching business analytics
Collaborate with mentors in small group sessions to apply skills, overcome challenges, and network globally
Get personalized guidance from industry experts to plan your data science and AI career
Access curated interview questions and prep tips from top recruiters
Have your profile reviewed by experts to highlight key strengths
Get access to a live session to explore roles, paths, and career direction strategies
The course fee is 3,950 USD
Understand data science from business, technical, and conceptual perspectives
Build proficiency and practical skills in data science tools, including Generative AI applications
Tackle business challenges using data science, analytics, and Generative AI techniques
Perform end-to-end analysis to extract insights, applying Generative AI solutions
Easy payment plans
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*Subject to third party credit facility provider approval based on applicable regions & eligibility
Admissions close once the required number of participants enroll. Apply early to secure your spot
Apply by filling a simple application form.
A panel from Great Learning will review your application to assess your suitability for the program.
After the final review, you will receive an offer for a seat in the program's upcoming cohort.
What is unique about the PGP-DSBA Program?
The Post Graduate Program in Data Science and Business Analytics (PGP-DSBA) offers a comprehensive and industry-relevant curriculum, which includes:
Personalized mentorship in small groups of up to 15 learners.
Hands-on learning with real-world case studies and projects.
Hands-on experience with industry-standard tools like Python, Tableau, and Advanced Excel.
Experiential learning projects at the end of each module to apply theoretical knowledge to business challenges.
Interactive live sessions with industry experts and mentors for insights on current industry trends.
Flexible online learning model specifically for working professional
What is the role of the McCombs School of Business at The University of Texas at Austin in the PGP-DSBA?
What is meant by mentored learning?
How will PGP-DSBA help me progress in my career?
The PGP-DSBA program aims to prepare individuals with the essential skills, hands-on learning, and industry recognition to boost career prospects. Key benefits include:
A certificate of completion from UT Austin with 9 CEUs (Continuing Education Units) recognized worldwide, providing global credibility.
Expert-led, recorded content and hands-on training that develops a strong understanding of data science and business analytics.
Industry-aligned projects that help you build a robust professional portfolio.
Opportunity for networking with experienced practitioners and peers.
Career guidance services include resume reviews, LinkedIn profile enhancement, and an ePortfolio.
Is the PGP-DSBA a completely online program?
Will the program certificate be awarded by The University of Texas at Austin?
Would I have to spend extra on books, online learning material or license fee?
How will I be evaluated during the program?
Are there any experiential projects as part of PGP-DSBA?
Which companies do PGP-DSBA industry mentors work for?
What are the tools covered in the program?
What is the ranking of Texas McCombs' Analytics Programs?
What types of projects and hands-on experience are included in the Business Analyst courses?
Is this program rated among the best Business Analytics courses for a career transition?
What are the job prospects after completing this Business Analyst course online?
How is this business analyst course structured?
What is the required weekly time commitment?
Participants should expect to commit approximately 8–10 hours each week, including:
2-3 hours of recorded video lectures
2-hour mentored learning sessions each weekend
1 hour of assessments or exercises
2–4 hours of self-study and practice
Will I receive a transcript or grade sheet after completion of the program?
You will receive a grade sheet post-completion; however, it is to be noted that the program does not carry any credits. You will be assessed on your performance through individual assessments and modules to be eligible to receive a certificate.
Upon completion of all modules and fulfilling the program requirements, you will receive a certificate of completion from the University of Texas at Austin.
What are the eligibility criteria for PGP-DSBA?
This program is ideal for working professionals transitioning toward analytics roles. A background in quantitative disciplines like engineering, mathematics, statistics, or economics is beneficial but not necessary.
How can I apply for the program?
What is the admission process?
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:
Full Refund can only be issued within 48 hours of enrolment
If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded.
Fee Paid Beyond Admission Fee:
Refund or dropout requests submitted more than 4 weeks before the commencement date are eligible for a full refund, excluding 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, excluding 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, excluding the admission fee
Requests received after the course commencement are not eligible for a refund.
Cancellation must be requested in writing to the program office.
What is the future of Data Science and Business Analytics?
Data Science is already being applied across diverse industries, including gaming, robotics, healthcare, marketing, finance, and more, and its impact is expected to expand into new domains. Business analytics, in combination with data science, helps organizations optimize performance, forecast trends, and improve decision-making.
With the increasing adoption of these technologies, many traditional job roles are evolving or being replaced by data-centric positions. As a result, pursuing a career in data science and business analytics is a strategic and future-proof choice. These fields also offer some of the highest-paying roles globally, making them an attractive career path for professionals seeking growth and stability.
What are the various job roles of Data Science and Business Analytics?
Here are some popular job roles in Data Science and Business Analytics:
Data Analyst – Analyses and processes the data to provide actionable insights.
Data Scientist – Develops predictive models and applies machine learning techniques.
Business Analyst — Analyzes data to support business decisions and increase efficiency.
Data Engineer – Develops and maintains data pipelines and infrastructure.
Statistician – Applies statistical methods to analyze and interpret data trends.
Database Administrator – Manages databases to ensure data integrity and accessibility.
Data Architect – Designs scalable data solutions for organizations.
Pursuing a data science and business analytics program will help you acquire the skills needed to succeed in one of these roles.
What are the differences between Data Science and Business Analytics?
Data Science is a discipline that applies machine learning and artificial intelligence to analyze huge datasets, discover patterns, and build predictive models to process data.
Business Analytics focuses on utilizing data in decision-making and strategy development within organizations, with a heavy emphasis on statistical analysis and presenting findings through data visualization.
Data Scientists develop algorithms and work with structured and unstructured data, whereas business analysts use only structured data to generate insight.
Data science requires more programming languages like Python, etc. Business analytics, on the other hand, is more tool-based, such as Excel, Tableau, SQL, etc.
It is a well-known fact that both areas of fields work together to improve business performance.
What is the difference between Data Science and Data Analytics?
While Data Science and Data Analytics are related, they have distinctions in their scope and application:
Data Science focuses on developing algorithms and predictive models to solve complex problems using machine learning, artificial intelligence, and statistical techniques.
Data Analytics focuses on the processing and analysis of data to detect trends, optimize operations, and create actional wisdom.
Data Science has a macro-level scope, handling vast datasets, whereas Data Analytics has a micro-level approach, focusing on specific business challenges.
Data Science is used in areas such as AI, robotics, and automation. Data analytics is mainly applied in marketing, finance, healthcare, and customer analytics.
What are the various applications of Data Science and Business Analytics?
Internet Search – Popular search engines such as Google and Bing utilize data science algorithms to present relevant outcomes.
Speech Recognition – Siri and Alexa are virtual assistants that use data science to perform voice recognition and natural language processing.
Targeted Advertising – Businesses leverage analytics to optimize digital marketing campaigns and improve customer engagement.
Recommendation Systems – Netflix and Amazon are among the platforms that use data science to offer a more personalized experience in recommending products based on user activity.
Healthcare – Data Science helps in predictive diagnostics, personalized treatment, & operational efficiency.
Finance – Business Analytics is used in banks and financial institutions for fraud detection and risk management and for making investment strategies.
These technologies are revolutionizing industries and, thus, are highly valuable skills for professionals.
Why should you choose Data Science and Business Analytics as a career path?
High Demand – Organizations across all industries rely on data-driven insights, increasing the need for skilled professionals.
High Earning Potential – Careers in Data Science and Business Analytics are one of the top-paying jobs across the globe.
Diverse Applications – These skills are applied in technology, healthcare, finance, e-commerce, and in many more domains.
Job Security – Demand for these roles will only increase as businesses continue to embrace data-driven strategies.
Thus, a Data Science and Business Analytics program can help you to excel in a challenging and promising career in an ever-evolving field.
What are the various industries that employ Data Science and Business Analytics?
Business & Finance - Predictive analytics, fraud detection, investment strategies.
Healthcare – Disease prediction, patient diagnostics, and personalized medicine.
E-commerce & Retail – Customer behavior analysis, recommendation engines, and demand forecasting.
Agriculture – Climate predictions, crop monitoring, and yield optimization.
Gaming Industry – User engagement analysis and AI-driven gaming enhancements.
Robotics & AI – Smart automation, intelligent systems, and deep learning applications.
These industries continue to integrate data science and business analytics, creating abundant career opportunities for professionals.
4.71 ( 10718 Ratings )
Application Closes
31st Jul 2025
McCombs School of Business at The University of Texas at Austin
7 months · Online · Weekend
MIT Professional Education
14 Weeks · Live Virtual · Weekdays & Weekend
McCombs School of Business at The University of Texas at Austin
4 Months · Online · Weekend
Data is a multidisciplinary field primarily based on facts, trends, and statistical figures. In the emerging scope of data, Data Science is amongst the most prominent attributes of every sector and business operations as it is helpful in getting relevant insights and making business decisions based on it.
There has been a drastic technologic shift in a couple of years which results in the creation of huge amounts of data every single day. In a 2018 article, the World Economic Forum claimed that by 2025, over 463 exabytes of data will be created globally every day, the equivalent of 212,765,957 DVDs per day.
Data science mainly focuses on using the strategies to extract and dissect data specific to definite domains or sectors. It is more about giving targeted solutions rather than a quality solution applicable to all business operations. We can observe its use and functions in various sectors like healthcare, education, finance, retail, etc. With the help of its techniques, healthcare has found better solutions to take care of the patients, the education sector is providing better opportunities to students, the banking industry is more focused on providing the best customer service, and more.
The process of analyzing data and extracting useful insights from it to use it in the growth of businesses is Data Science.
It is not just a single term to understand. One needs to know the other various steps involved in the process, such as:
1. Business Requirements
It only makes more sense to collect data once you know where, how, and for whom it has to be used. Understanding business requirements is key as it helps companies to get critical information that can be used to fulfill their goals and objectives.
2. Data Collection
As per a recent study, over 2.5 quintillion bytes of data is generated every single day. The number is expected to grow exponentially with more businesses opting to capture and collect user data. It is important for businesses to understand the type of data to be collected.
3. Data Cleaning
Collecting data is not complete until it goes through check processes to retard any unnecessary data. This step is important in terms of reducing the complexity and also making sure that the collected data is efficient for the analysis.
4. Data Exploration and Data Analysis
Sometimes, there’s a need to employ data science tools to get the right understanding of the patterns of data. This process is called Data exploration. You can perform data analysis effectively only when you understand the patterns properly.
5. Data Modelling
The stage of Data Modelling is considered to be the most crucial one in Data Science. Based on all the insights and trends derived from data analysis and exploration, a predictive model is created. A lot of tests and training happens with this model to make sure it predicts precise outcomes from the data being input.
6. Data Validation
This is the stage of validation when the predictive model built in the previous stage is tested rigorously. The certain predictions that are produced from the model are then compared with the previous data and outcomes to judge the efficiency of the model. This stage is basically to test the model and find out all the existing errors, false predictions, inappropriate outcomes, and many more, only to work on them and fix the issues at the earliest. The model goes through several testing stages to improve the preciseness of outcomes.
7. Deployment and Optimization
The final stage of data science mainly involves the deployment of the model to the client and then seek feedback. Based on the feedback, the data scientists rework the errors and improvements after which it is good to go. That’s the whole process of data science.
The impact of Data Science Application is non-negligible in almost all domains. Businesses are embracing it at all given points to accelerate the growth. There are so many factors that have contributed to the rapid growth of Data Science and its use across the globe.
Here we list out a few industries and the benefits they are getting from the process of data science and its implications.
1. E-Commerce
E-commerce is one field that is not only applying Data Science but is growing immensely with its implications. A lot of e-commerce websites such as Amazon are using this process to make the most recommendations to the customers when they are surfing the website based on their search histories. It improves the entire user experience while shopping.
2. Fraud Detection
Financial sectors have a huge demand for Data Science in managing the indefinite financial transactions happening every other minute. The need of detecting fraudulent activities is real in this sector and hence the use of Data Science is important in sectors like banking, credit cards, loans, and many more. It is the ultimate solution to secure financial transactions and predict fraud.
3. Self-Driving Cars
We all know that Self-driving cars are the biggest example of functioning with the use of data. The designing of cars to perceive things on road well in advance and then moving according to the GPS is all because of strategizing the data properly.
4. Virtual Assistance
The current trending virtual voice assistants are all a result of the processes of Data Science. Gadgets like Google Home, Siri, Alexa, and many more, which have brought so much ease to our lives are all a result of correct outcomes of data. In fact, it has even replaced customer executive roles these days with a chatbot which is extremely quick and responsive at the same time.
Hence, it has become quite important to learn and data and its techniques as it might replace many existing jobs in the coming times.
Undoubtedly, all the job roles being offered in this domain are in high-demand all over the world. There’s a huge demand for data scientists and analytics professionals. Hence, many people are looking out for various Data Science Programs Online in the USA.
1. MIS Reporting Executive
2. Data Scientist
3. Data Analyst
4. Data Engineer
5. Statistician.
6. Data Architect
7. Machine Learning Engineer
Business Analytics
Descriptive Analytics
Descriptive Analytics as the name says derives insights by analyzing the historical data. It is one of the most important analytical techniques to perform advanced and complex analysis of data.
Predictive Analytics
Predictive Analytics is highly useful to derive reliable conclusions based on predictive models that are built to identify risks and connect data with effective actions.
Prescriptive Analytics
Prescriptive analytics is the stage after predictive analytics. It is built on predictive capabilities. It mainly includes the application of logical and statistical techniques to derive the most preferred insights.
In terms of science and technology, the United States of America is recognized as one of the most powerful and advanced countries in the world. The country has seen some amazing technological breakthroughs in every sector of businesses such as Healthcare, Physics, Engineering, Biotechnology, Telecommunications, and many more.
Looking at the growing pace of the technological industry, there is a huge demand and supply gap of Data Science and Business Analytics professionals in and across the United States. As and when more and more businesses start to adopt the data science route in the coming future, the demand will only increase, and thus learning and pursuing Data Analytics courses online in the USA is the need of the hour.
According to some statistics, there are 24000+ job opportunities available in the United States of America for the candidates pursuing a Data Science and Business Analytics course online.
So, don’t let this time slip through your hands. Grab this opportunity and enroll yourself in an online data science degree program or the best data science certification online.
There are plenty of institutes across the country that offer online courses in data science and business analytics, but not all provide the same learning outcome. While deciding upon a course that fits your requirement and expectation, it is important to keep in mind the objective and delivery of the course. Working professionals looking to transform their career in the first of Data Science and Business Analytics should look at mentored learning models, where a learner can interact with industry experts in a small batch and discuss real-life case studies for easy understanding of topics.
Rated among the best Data Science and Business Analytics online course, PGP-DSBA by UT Austin offers interactive mentored learning that provides collaborative as well as personalized learning.
1. Ranked #4
The Data Science and Business Analytics Online Program offered by the McCombs School of Business at The University of Texas at Austin has been ranked number 4 in the QS Business Analytics Ranking 2020.
2. Curriculum
The comprehensive program is designed to build expertise in the most widely-used analytics tools and technologies. The curriculum will help you learn concepts with real-world case studies by the faculty of UT Austin and the experienced industry leaders. This enables learners to get hands-on training and experiences in leading tools like R, Python, Tableau, Machine Learning and many more.
The live mentored sessions taken by the Industry Professionals of Data Scientists and Business Analytics are a major part of the curriculum that helps learners get a complete understanding of concepts.
3. Flexibility
The PG Program in Data Science and Business Analytics provides access to learners to learn at their comfort. With Great Learning’s proprietary learning management system (LMS) Olympus, learners can access course content with one single login that works on all the platforms. For professionals with a busy schedule, they can watch recordings of missed lectures at their convenience. The mobile app provides notifications on quizzes and assignments to keep learners updated and motivated throughout the program journey. The mentored learning sessions are kept at a time convenient to the cohort.
4. Faculty
Learn Data Science and Business Analytics from the top faculty of The University of Austin at Texas. The course is designed by the leading faculty from UT Austin, ensuring widely-used analytics tools and technologies are a part of the curriculum. The mentors for the program are leading industry experts with a strong practical understanding of core concepts of data science.
5. The Expertly-Designed Program
Here are a few faculty profiles for your reference:
Dr. Kumar Muthuraman
Faculty Director, Center for Research and Analytics, McCombs School of Business,
The University of Texas at Austin. H. Timothy (Tim) Harkins Centennial Professor.
M.S & Ph.D., Stanford University.
Dr. Abhinanda Sarkar
Academic Director, Great Learning
B.Stat & M.Stat, Indian Statistical Institute. Ph.D. Stanford University.
5. Career Assistance
With dedicated career support, the online data science and business analytics program offers personalized 1:1 Career coaching and preparation for interviews. The career support team also provides assistance in building a resume to help you get the desired job. Learners can use their industry-ready portfolio, e-Portfolio to showcase their projects, skill, and tools.
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