MAA-509 Big Data Ecosystem (Summer)
This course examines the data management process from access to data sources through implementation of scalable processes. Big data requires understanding database design, and increasingly involves parallel processing and cloud-based data storage and analysis. Data formats and database architectures are examined. Tools for extracting data from relational, structured, and non-structured databases are explored. Included are issues related to data security and governance. Students will also learn how to evaluate technologies used to implement scalable decision analytic systems. 3 credits.
MAA-515 Ethical and Social Responsibilities of Data Analysis (Summer)
This course consists of two parts. In the first, relatively brief part, we examine the ethical principles and theories that are relevant to resolving any moral issue. In the second part, we apply these principles and theories to the key moral issues in business (with emphasis, where appropriate and relevant, on questions dealing with data/information acquisition, analysis, and application) by studying, discussing, and debating them, principally through a case-study approach. The focus of our attention is on the three basic kinds of moral relationships in business: a) between the firm and the employee; b) between the firm and other economic agents (i.e., customers, competitors); and c) between the firm and various non-business groups (i.e., the environment). 3 credits.
MAA-520 Data Science (Fall)
Analytics is the process of taking data and turning it into new forms of value. The beginning of this process is often referred to as Data Science and the second stage of the process encompasses algorithms and visualization. As an introduction to data science, we proceed to cover practical data analytic skills including accessing and transferring data (ETL — extract, transform, load), applying analytical frameworks or patterns, applying methods from data mining and machine learning, and learning analysis methods for processing text. The course will also provide students an opportunity to do hands-on exercises with Big Data. The emphasis will be on practical usefulness and analytics patterns. 3 credits.
MAA-521: Database Programming (January Term)
This course explores the fundamental concepts of relational databases: how they are designed, accessed, protected, and managed. The primary focus is on database programming – statements that retrieve, modify, summarize, analyze, and extract data. Storing of queries within the database for repeated use will be covered. 3 credits.
MAA-530 Programming for Analytic Methods (Spring)
Business Analytics is the process of transforming data into business value. The Data Science course explores a variety of analytical methods for building models (predictive or explanatory). This course will focus on programming languages used for accessing, analyzing, and implementing such models. While many software platforms are available to automate various parts of this process, programming languages are commonly used – primarily R and Python at present. This course exposes students to the use of these languages, focusing on their use for accessing and cleaning data sources and implementing models in a production environment. The subsequent course (Big Data Ecosystem) utilizes these languages for an understanding of the entire process of Business Analytics. While some students may develop a proficiency with coding in these programming languages, the purpose of the course is to provide sufficient exposure to the use of these languages for making business decisions regarding choices of software, human resources, and organizational structures necessary for developing Business Analytics efforts. 3 credits
MAA-540 Machine Intelligence (Spring)
Machine intelligence involves the set of technologies that permit computers to learn, including pattern recognition (text, image, and data), classification modeling, recommendation systems, natural language processing, and a variety of applications that increasingly are part of everyday life. Often referred to as “artificial intelligence,” this course goes further to explore techniques (such as recurrent neural networks and deep learning) which automate the ability of computers to recognize and identify patterns and learn from these. These techniques are scalable in ways that provide automatic implementation – ranging from web search algorithms to intelligent agents to self-driving cars. This course will provide hands-on experience building such systems, but with the focus on understanding the implications for business. Students will gain an appreciation for the scope of potential applications, the limits of machine intelligence, ethical aspects of their use, and disruptive tendencies of these technologies. 3 credits.
MAA-550 Data Visualization (Fall)
Data/Information visualization is widely used in a number of industries, including business, engineering, and media disciplines to help people analyze and understand what the data is telling us. The industry has grown exponentially over the last few years, and as a result there are more tools available to help us quickly and efficiently create compelling visualizations. This course provides an overview of the data/information visualization discipline. Using a hands-on approach, readings and lectures will cover various visualization principles and tools. Our labs will focus on practical introductions to tools and frameworks, with plenty of time to explore & utilize additional applications. We will discuss existing visualizations (e.g. what we find in various publications and government data sources) and critique their effectiveness in conveying information. All students are expected to participate in class discussion, complete lab assignments, and create & critique many data visualization examples throughout the session. 3 credits.
MAA-560 Marketing Analytics (Spring)
Marketing remains a branch of business as well as a social science, and is often characterized by the “4 Ps” of product, place, promotion, and price, and has been extended in many contexts to include people, packaging, and positioning. Each of these Ps is a candidate for improvement through the use of analytics. In Marketing Analytics, we consider the analytics of:
- Pricing, Forecasting Sales
- Understanding Customer Demand
- Customer Value
- Market Segmentation
- Retailing
- Advertising
- Market Research Tools
- Internet and Social Marketing.
Topics include, but are not limited to, Price Bundling, Willingness to Pay, Profile Conjoint Analysis, Discrete Choice Analysis, Value Templates, Clustering and Collaborative Filtering, Bass Diffusion Models, Market Basket Analysis, Pay-per-Click Advertising, Principal Components Analysis, Measuring Nodes and Links, Network Contagion, and Viral Marketing Models. 3 credits.
MAA-571 Risk Analysis (Fall)
An important part of business planning is identification, analysis, and management of risk. This spreadsheet-based course examines a variety of models geared to addressing business and social needs. Uncertainty is explicitly analyzed through the use of scenarios, simulation, and other techniques. Emphasis is placed on understanding and communicating the important uncertainties associated with any plan, and developing ways to incorporate these into business plans. 3 credits.
MAA-581 Capstone Seminar (Summer)
The goal of this course is to have students complete a data project (generally in groups) of a complex nature. This includes obtaining and cleaning relevant data, conducting appropriate analysis and communications of findings, and planning implementation of organizational processes that utilize the results of the project. Projects may come from students’ work environment, Center for Business Analytics sponsored projects, or other timely data projects that may arise at appropriate times. 3 credits.
Applied Analytics Degree Comparison chart.pdf
