Courses
Data Analytics Courses
BDA211 Introduction to Applied Data Analytics
[3–0, 3 cr.]
This course introduces students to the fundamentals of descriptive analytics highlighting the broader issues relating to framing problems and identifying key performance measures.
BDA311 Data Driven Design Thinking
[3–0, 3 cr.]
This course introduces students to the iterative problem-solving process of discovery, ideation, and experimentation to gain insight and yield innovative solutions for virtually any type of organizational or business challenge.
BDA401 Methodologies and Model Building in Data Analytics
[3–0, 3 cr.]
In this course students will be introduced to a variety of predictive and prescriptive analytics tools. In this context students will explore the common pitfalls in interpreting results from data driven models, especially those associated with big data and real-time streaming data. Collectively, this course will help students internalize a core set of practical and effective machine learning methods and concepts and apply them to solve some real-world problems.
Pre-requisites: BDA211 Introduction to Applied Data Analytics
BDA811 Business Analytics for Competitive Advantage
[3–0, 3 cr.]
Business Data Analytics (BDA) is emerging as an essential driver of competitive advantage, and in today’s dynamic business environment, success in the market and achieving sustainable competitive advantage require understanding the fundamentals of collecting data, describing and developing insights from data sets, and presenting and communicating results.
The goal of this course is to equip participants with fundamental data analytics skills needed to optimize business processes and gain insights that inform business decisions. In an era where data are considered a corporate asset, it is crucial to learn how important it is today for decision makers to convert raw data into insight, solve problems, and seize opportunities. Topics include importance of business analytics, types of analytics – descriptive, predictive, and prescriptive -, data visualization, and reporting. Using case studies, group problem solving, and lab sessions, students will get hands-on learning through the deployment of a variety of powerful software and computer-based data analytics and visualization tools, including advanced Excel, SPSS, and Tableau. Students will also learn how to make more powerful presentations by understanding and implementing the key principles of report and visual presentation of data.BDA812O Cognitive Analytics
[3–0, 3 cr.]
This course offers students an understanding of the “data” or the conscious/non-conscious information processing in our brains explaining the consumer’s decision-making on a non-conscious level. Neuro-metrical and biometrical measures, models and technologies help students understand and predict consumer behavior using non-traditional research methodologies.
Cognitive analytics is about understanding brain science (neuro-science, behavioral economics & social psychology) and exposing as well as measuring the consumer’s hidden data from the mind and body. Applying data mining techniques to Neuro Marketing data (mind-mining) will provide students with a much deeper and richer understanding of consumer preferences, choices and behavior.
BDA813 Data Management for Analytics
[3–0, 3 cr.]
The goal of this course is to give students the fundamental knowledge and abilities needed to comprehend and apply data modification and management. They will get practical knowledge and experience in data management for business intelligence, analytics, and data science endeavors. Professionals working in data and analytics will also be able to comprehend the various newly developed data management solutions.BDA814O Analytics Applications
[3–0, 3 cr.]
The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.BDA815O Big Data Analytics
[3–0, 3 cr.]
This course provides an understanding of the business value of big data, the importance of effective management of big data, and the development of technical competencies using leading-edge platforms for managing and manipulating structured and unstructured big data.BDA816O Machine Learning for Predictive Analytics
[3–0, 3 cr.]
Topics include data cleaning, and exploration; predictive models, linear and nonlinear regression models, decision tree analysis, and discriminant analysis; resampling techniques; clustering analysis and dimension reduction. Applications to marketing, operations, finance, and risk management.BDA817O Information Security for User Behavior Analytics
[3–0, 3 cr.]
This course covers key risks to information systems and business data. Students will apply data analytics techniques across different dimensions to provide effective information security analytics. Threats to normal user behavior are compared and contrasted by utilizing the user behavior analytics approach Normal behavior.BDA818O Healthcare Analytics
[3–0, 3 cr.]
This is an introductory course to the healthcare research fundamentals and methodologies. Topics include healthcare research design, data collection, data analysis, and operations research and operations management tools applied to the health care management sector.BDA819O Ethics in Analytics
[3–0, 3 cr.]
This course examines the ethical aspect of business analytics. It covers topics in data ethics regarding the moral obligations, values and principles governing data protection and privacy rights. It also explores how data driven decisions can have a profound impact on society and how businesses can refine their strategy to learn from past mistakes and built on previous successes.BDA820O Data Visualization
[3–0, 3 cr.]
An introductory course to data visualization. It covers visualization techniques applied to extract information that aids in the data driven decision-making process. Students will use various visualization tools to visually explore business problems, develop data visualizations, dashboards, and stories. Students will focus on communicating the results in an effective and informative manner.BDA825 Artificial Intelligence for Managers
[3–0, 3 cr.]
AI for Managers equips MBA students with a strategic understanding of artificial intelligence and Machine Learning, preparing them to lead AI-driven initiatives in modern organizations. This course covers core AI concepts, including machine learning, building data models, data pre-processing and analytics with an emphasis on real-world business applications. Students will learn how to identify and evaluate AI opportunities, communicate effectively with technical teams, and make data informed decisions. Through case studies and practical exercises, this course provides the skills and knowledge managers need to drive value and innovation within their firms.BDA830 Emerging Trends in Data Analytics
[3–0, 3 cr.]
This course enables students to explore new and emerging approaches, topics and trends in the business data analytics field, as well as their applications in various industries and sectors. Students will research, write about, experience, propose, and prototype trends and possibilities for business data analytics.BDA832 Big Data Processing & Blockchain Technology
[3–0, 3 cr.]
Two developing technologies that are among the top priorities of corporations are blockchain and big data. It is highly anticipated that these will alter the manner in which the company and its operations operate. Offering students a thorough understanding of the technologies of decentralization, or blockchains, and big data analysis is the aim of this course. Students will have the chance to take on the role of the protagonist in a revolution that is underway, one that calls for professionals with specialized training in the most advanced businesses and organizations.BDA833 NLP Text Analytics
[3–0, 3 cr.]
The purpose of this course is to familiarize business school students with the artificial intelligence field of natural language processing, or NLP. Computational linguistics, or NLP, explores methods and offers resources for deciphering and analyzing spoken language. Because we use so many of NLP’s applications in our daily lives—from virtual agents and assistants to phrase autocompletion—it is widely used. NLP techniques are increasingly being used as vital instruments for conducting sophisticated analysis and research in the field of finance due to the volume of easily accessible textual data. Language models, text classification, tagging, dependency parsing, topic modeling, word embeddings, transformers, coreference resolution, named entity identification, and sentiment analysis are just a few of the many NLP subjects that will be covered in this course. We will also go over the many machine learning techniques, such as neural networks, that are applied in NLP. Students will gain knowledge of the issues and difficulties that NLP tackles, as well as the algorithms used to resolve them, throughout the course. Students will also be able to use the knowledge they have learned in their own research projects by the end of the course.BDA834 Analytical Data Mining
[3–0, 3 cr.]
This course will provide students with an understanding of fundamental data mining concepts and tools. The topics covered include data sources, data cleaning techniques and tools, common data mining algorithms, statistical modeling, and widely used tools for both structured and unstructured data mining.BDA835 Optimization
[3–0, 3 cr.]
The focus of this course is on numerical algorithms, theory, and data-driven models for real-variable optimization. Students will examine the study of mathematical function maximizing and minimization, as well as the significance of duality, pricing, optimality criteria, and algorithms in identifying and locating solutions. They will also explore how machine learning, operations, marketing, finance, and economics are applied.BDA836 Analytics for Customer Relationship Management
[3–0, 3 cr.]
Analytics for Customer Relationship Management is a dynamic course designed to equip students with the advanced analytical skills necessary to transform customer data into actionable insights, driving strategic decisions in marketing and customer service. This course delves into the core principles of CRM, exploring how cutting-edge data analytics can optimize customer interactions and enhance organizational growth. Students will learn to harness the power of both descriptive and predictive analytics to understand customer behaviors, preferences, and trendsBDA837 Web and Social Media Analytics
[3–0, 3 cr.]
This course covers concepts and strategies for accessing, exploring, visualizing, and analyzing data from social networks and other social media platforms along with website usage and clickstream data. In addition to doing social network analysis to pinpoint significant social actors, subgroups, and network features in social media, students also learn how to apply key indicators to evaluate objectives and return on investment.BDA838 Supply chain Analytics
[3–0, 3 cr.]
This course presents practical applications of data analytics (descriptive, predictive, and prescriptive) in the manufacturing, trade, and service industries, in a range of supply chain management domains, including forecasting and inventory management, sales and operations planning, transportation, logistics, and fulfillment, purchasing and supply management, supply chain risk management, etc. In order to improve supply chain efficiency and business value, students learn how to identify the correct data set, ask the relevant questions, and utilize the right models and tools to create decisions that are based on facts. Product development analytics, inventory and resource management, spend analytics and supplier selection, transportation analytics, fulfillment diagnostics in logistics systems, sales and operations analytics in production, demand forecasting for new products, and supplier and product line selection are among the topics covered.BDA880L Forecasting Analytics and Data Mining
[3–0, 3 cr.]
Time series forecasting is essential for every organization that deals with quantifiable data. It is widely used in retail stores, international financial organizations, energy companies, banks and lending institutions, and in many other industries. Forecasting analytics enable managers and policy makers to better make informed decisions. This course is a hands-on introduction to quantitative forecasting of time series. Students will learn the most popular forecasting techniques used in practice. The course covers topics such as pre-processing, characterization, and visualizing time series, model performance evaluation, smoothing methods, time series regression models, Box-Jenkins models, autoregressive integrated moving average (ARIMA) models, models with binary outcome, and neural networks for time series (if time permits).