STEM MS in Business Analytics Degree for working professionals in USA

In this course, you will be introduced to the core constructs of Python programming, laying a solid foundation in programming skills for data science. You’ll learn to read, manipulate, and analyze data, applying techniques to solve business challenges. Through hands-on exercises, you’ll practice using widely-adopted Python libraries and packages, including numpy, pandas, matplotlib, and seaborn, to perform computations, visualize data, and conduct exploratory data analysis. By the end, you’ll be prepared to apply Python skills to interpret data insights and support data-driven business decisions.

In this course, you will explore statistical methods essential for business applications. This module focuses on probability and inferential statistics to help you make evidence-based business decisions. You’ll learn to analyze data distributions, create business estimates using confidence intervals, and apply a range of hypothesis tests to validate business assumptions across various scenarios. With practical exercises, you’ll understand how to address core business questions using statistical insights, preparing you for deeper analytics in future modules.

In this course, you will gain foundational knowledge in machine learning to help solve business problems. The course covers a range of modeling techniques, including linear regression, logistic regression, decision trees, k-means clustering, and ensemble models, allowing you to discover patterns, make predictions, and identify data groups. You’ll learn to prepare data for modeling, validate models for statistical significance, tune model performance, and interpret model results to gain insights that inform business strategies. Emphasis is placed on practical applications and the use of tuning algorithms to optimize your model’s performance.

In this course, you’ll develop the ability to write complex and efficient SQL queries for data retrieval, aggregation, and manipulation. You’ll also learn to communicate insights effectively through data visualization, using tools like Tableau. With hands-on exercises, you’ll practice SQL for data management tasks and create diverse visualizations to convey findings through storytelling. This course combines SQL analytics with advanced dashboarding techniques in Tableau, empowering you to turn data into actionable insights through visual narratives.

STAT 4450 - Managerial Statistics

In this course, you’ll either review or learn the fundamental concepts of statistics and probability, with a focus on managerial applications. Topics include data collection, descriptive statistics, and understanding the characteristics of discrete and continuous probability distributions. You’ll practice sampling, creating confidence intervals, and hypothesis testing. Additionally, the course covers linear regression and correlation techniques to help interpret data for informed decision-making. Tasks are performed in MS Excel, providing you with practical skills for analyzing business data as a basis for the Big Data Statistics courses.

BAN 4500 - Information Systems for Analytics

In this course, you’ll study the organizational context and infrastructure that support business analytics. You’ll explore information systems and technologies, learning how these components—such as hardware, software, cloud services, databases, and networking—enable analytics. This course emphasizes strategic positioning through technology adoption and organizational change. You’ll also delve into security, risk management, privacy, and ethics. Business case analyses and hands-on exercises are used throughout the course to develop solutions and communicate insights to stakeholders, equipping you to improve information system effectiveness in real business environments.

BAN 4550 - Analytics Programming

In this course, you will receive an introduction to programming for analytics, using Python as the primary language. Topics include programming fundamentals such as data types, conditionals, functions, and file handling. By completing this course, you’ll gain the ability to design and develop Python programs suited for data analysis and scientific computing. No prior programming experience is required, but students are asked to complete an introductory online Python course before class starts, ensuring readiness for practical applications in analytics.

STAT 4600 - Intermediary Statistical Modeling for Analytics

In this course, you’ll build upon your statistical knowledge with a focus on descriptive, diagnostic, and predictive analytics. This module covers data collection, hypothesis formulation and testing, and statistical methods that help answer complex business questions. Using R, you’ll work with real-life datasets to conduct analysis, with topics including inferential statistics, linear regression, and predictive modeling. This course emphasizes both statistical theory and practical applications, enhancing your ability to apply analytics in decision-making contexts.

BAN 5501 - Database Management and SQL for Analytics

In this course, you will be introduced to the Database Management Body of Knowledge (DMBOK) from a managerial perspective. The learning objectives will be to fully understand the basic concepts of database design and usage and develop practical skills for utilizing databases to their fullest extent. Correct database design will be emphasized as a theoretical foundation and a practical necessity.

The following topics are the focus of the course:

•  High-level, general database concepts and design 
•  Design, create, and manipulate an individual, relational database, including the utilization of SQL as a fundamental tool
•  Interpret and apply client database needs 
• Discuss and apply best practices of User Interface Design to database applications 
• Identify and discuss new developments and trends in databases, including data warehouses, data lakes and hubs, and X analytics.
  

STAT 4650 - Machine Learning

In this course, you will explore machine learning techniques used in business analytics, including logistic regression, cross-validation, decision trees, and clustering. You’ll also touch on support vector machines and time series forecasting, if time permits. This module combines mathematical modeling and scenario optimization to enhance decision-making. Using R and Python, you’ll work with real datasets to solve complex problems, focusing on the speed, reliability, and accuracy of decisions informed by machine learning.

BAN 5573 - Visual Analytics and Business Intelligence

In this course, you’ll gain practical experience with business intelligence tools and methods, focusing on enterprise-level analytics through case studies and decision support systems. You’ll spend the initial weeks on descriptive analytics in Tableau, then advance to predictive analytics using KNIME, and finally study prescriptive analytics with Excel Frontline Software and LINGO. By the end, you’ll be able to conduct complete analytic projects, from data gathering and interpretation to project delivery and visual storytelling, enabling effective decision-making support

BAN 5650 - Applied Business Analytics

In this course, you’ll develop the skills to apply data analytics and decision support models to industry problems involving resource constraints and regulatory complexities. Topics include data collection, quantitative modeling, and resource allocation using techniques like capital budgeting, cost-benefit analysis, and Monte Carlo simulation. You’ll learn to frame decision problems in terms of strategy, feasibility, cost-effectiveness, and risk, while applying project management tools to deliver complex analyses and present your findings to stakeholders.

BAN 5600 - Advanced Big Data Computing and Programming

In this course, you’ll learn to work with large data volumes using Big Data platforms within the Hadoop ecosystem, such as Pig, Hive, Flume, Sqoop, and Spark. This course emphasizes the use of distributed and parallel computing to process and analyze structured and unstructured data. You’ll explore real-world Big Data applications, covering essential platforms like Hortonworks Sandbox, Amazon AWS, and Databricks. Topics include visualization, Natural Language Processing, and streaming analytics, equipping you to manage high-volume, high-velocity data challenges.

Marketing Analytics Concentration

• MKT 5401 - Marketing Research and Analysis
  In this course, you’ll study the methods and tools used in marketing research. Topics include survey design, data measurement, evaluation of structured and unstructured data, and sample size determination. You’ll practice primary data collection methods such as focus groups and projective techniques, and apply advanced analytical techniques, including regression analysis, chi-square, and t-tests. This module emphasizes data analysis and interpretation using SPSS, preparing you to support strategic marketing decisions with data.

• MKT 5495 - Digital Marketing Analytics
  In this course, you’ll explore digital marketing strategies such as SEO, display ads, mobile analytics, and social monitoring. You’ll examine key performance metrics and tools like Google Analytics 4 to evaluate campaign effectiveness. This course combines both quantitative and qualitative approaches to digital marketing, guiding you to think critically and gain hands-on experience through assignments and certifications, building a strong foundation in digital marketing analytics.

Financial Analytics Concentration

• FIN 5401 - Investments
  In this course, you’ll receive an introduction to the financial markets, investment theory, and asset valuation. Topics include equity and bond pricing, portfolio theory, the capital asset pricing model, and options. You’ll also gain exposure to emerging investment topics such as ESG, FinTech, and cryptocurrency. The course emphasizes both theoretical and practical knowledge, equipping you to apply quantitative techniques in investment decision-making.

Electives (Choose One)

• FIN 5200 - Corporate Finance
  This course covers the principles of corporate finance, including asset valuation, capital budgeting, capital structure, and dividend policy. You’ll learn to apply financial theory to make sound organizational decisions, building a practical understanding of core finance concepts.

• FIN 5216 - Options, Futures, and Other Derivatives
  This course focuses on the pricing of financial derivatives and includes methods like binomial models and Monte Carlo simulations. Emphasis is placed on computational techniques and portfolio optimization, equipping you with quantitative skills for financial engineering.

• BAN 5700 - Blockchain and Cryptocurrency
  This course provides hands-on experience with blockchain technology. You’ll explore cryptocurrency transactions, NFTs, and blockchain development using Python. Topics include hash functions, cryptocurrency creation, blockchain applications, and practical projects to reinforce your learning.

• 3 courses shall be waived off for Great Learning learners
• Completion of an approved non-credit internship is required.
• The above curriculum is tentative and subject to change.
• Applicants should score a minimum of 3.0/4 GPA in each course and in aggregate in the online program in order to progress to the on-campus program.