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AI project can help predict trends and optimizes operations, allowing businesses to understand not just what has happened but what will happen-and what should be done about it. By the end of this course, students will be prepared to drive financial and operational impact by managing AI project lifecycles: developing comprehensive project plans, managing data and models, ensuring effective deployment, and communicating progress and outcomes to stakeholders.

AI project can help predict trends and optimizes operations, allowing businesses to understand not just what has happened but what will happen-and what should be done about it. By the end of this course, students will be prepared to drive financial and operational impact by managing AI project lifecycles: developing comprehensive project plans, managing data and models, ensuring effective deployment, and communicating progress and outcomes to stakeholders.

AI project can help predict trends and optimizes operations, allowing businesses to understand not just what has happened but what will happen-and what should be done about it. By the end of this course, students will be prepared to drive financial and operational impact by managing AI project lifecycles: developing comprehensive project plans, managing data and models, ensuring effective deployment, and communicating progress and outcomes to stakeholders.

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, and linear optimizations.

Prerequisite: STA 326

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, and linear optimizations.

Prerequisite: STA 326

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, and linear optimizations.

Prerequisite: STA 326

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, and linear optimizations.

Prerequisite: STA 326

This course builds on business analytics (BA328) and provides students with more advanced data analytics techniques to enable them to handle more complicated business situations. The course concentrates on predictive and prescriptive analytics, and covers topics such as advanced regression models, advanced time series forecasting methods, linear and nonlinear optimizations, Monte Carlo simulation, spreadsheet modeling, and data mining.

Prerequisite(s): BA 328

This course combines programming and data mining. Data mining uses techniques and algorithms to study data sets trying to find patterns in them and make forecast based on these patterns. Students will learn how to apply data mining techniques to big data and extract useful information for decision making. This is a hands-on course with many real-world applications in each topic. A computer software, R, Python, or any other will be used extensively in this course. The course covers topics such as data exploration and preparation, database management, dimension reduction, ensemble learning, cluster analysis, classification, association rule, anomaly detection, and web mining and text mining.

Prerequisite(s): BA 329

This capstone course combines programming and senior project. Students will learn basic programming skills and apply them to a final project. Students will learn how to use programming to transform structured and unstructured data into reporting- consumable data structures, and then present it using today’s sophisticated reporting tools. Students will apply these programming skills and the models learned in previous business analytics courses to a senior final project. The course covers topics such as data structure and flow control, data preparation, anonymization, reporting tools and technology, and delivery methodology.

Prerequisite(s): BA 428

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, data mining, Monte Carlo simulation, and linear and nonlinear optimizations.

Note: This course was previously BA-728 | Business Analytics.

Prerequisites: BUS 598

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, data mining, Monte Carlo simulation, and linear and nonlinear optimizations.

Note: This course was previously BA-728 | Business Analytics.

Prerequisites: BUS 598

This course enables students to compete in a data-driven business environment. The availability and extensive use of data in business today made analytics one of the most sought-after skills for students. The course provides student with the analytical tools and techniques to enable them to turn data into meaningful insights, and consequently take better and more profitable business decisions. The course introduces students to the different types of business analytics: Descriptive, predictive and prescriptive analytics. Topics covered in the course include single and multiple linear and nonlinear regressions, time series analysis and forecasting techniques, data mining, Monte Carlo simulation, and linear and nonlinear optimizations.

Note: This course was previously BA-728 | Business Analytics.

Prerequisites: BUS 598

Storytelling is one of the oldest and most powerful tools for action, and this course empowers students to transform raw data into compelling narratives that drive business decisions. By the end of this course, you’ll be able to create data queries and visualizations, integrate data into business planning, and effectively communicate your findings, helping organizations turn data into a strategic tool for competition and innovation.

Storytelling is one of the oldest and most powerful tools for action, and this course empowers students to transform raw data into compelling narratives that drive business decisions. By the end of this course, you’ll be able to create data queries and visualizations, integrate data into business planning, and effectively communicate your findings, helping organizations turn data into a strategic tool for competition and innovation.

Storytelling is one of the oldest and most powerful tools for action, and this course empowers students to transform raw data into compelling narratives that drive business decisions. By the end of this course, you’ll be able to create data queries and visualizations, integrate data into business planning, and effectively communicate your findings, helping organizations turn data into a strategic tool for competition and innovation.

Spreadsheet tools are the most universal and easily accessible program for most people to use and interpret data—and a vital skill for any analyst to know. In this course, students will learn data cleaning, financial modeling, optimization, and data visualization using PivotTables and PivotCharts, enabling students to analyze complex datasets, create forecasts, and effectively communicate insights to support data-driven decision-making in business contexts.

Spreadsheet tools are the most universal and easily accessible program for most people to use and interpret data—and a vital skill for any analyst to know. In this course, students will learn data cleaning, financial modeling, optimization, and data visualization using PivotTables and PivotCharts, enabling students to analyze complex datasets, create forecasts, and effectively communicate insights to support data-driven decision-making in business contexts.

Spreadsheet tools are the most universal and easily accessible program for most people to use and interpret data—and a vital skill for any analyst to know. In this course, students will learn data cleaning, financial modeling, optimization, and data visualization using PivotTables and PivotCharts, enabling students to analyze complex datasets, create forecasts, and effectively communicate insights to support data-driven decision-making in business contexts.

Managers who hope to stay competitive in business analytics need to be able to scale their analytics—and Python provides that solution. This course covers Python's significance in business analytics, setting up the Python environment, and learning basic syntax. You’ll leverage three of Python's essential libraries—NumPy for numerical data, Pandas for data manipulation, and Matplotlib for data visualization—to use data to solve complex business problems.

Prerequisites: BA-600 or BA-620 recommended