BAN 4550 - Analytics Programming
This course provides a general introduction to computer programming for analytics. Python will be used as the primary language. Specific topics include the programming environment, programming language elements, basic data types, conditionals, functions, and reading and writing files. Upon completing this course, students are expected to have a good understanding of programming and will be able to design and develop programs for scientific computing and basic analytics. While this course doesn’t require a background in programming, students are asked to complete an online course in Python before the start of the class.
STAT 4600 - Intermediary Statistical Modeling for Analytics
Intermediary Statistical Modeling for Analytics emphasizes understanding descriptive, diagnostic, and predictive analytics to identify critical aspects of a business question, from data collection to formulating and testing hypotheses. As such, this course is a data science course emphasizing statistical methodologies. At the same time, the course emphasizes the practical aspects of Business Analytics by embedding the methodologies in applications and underlining the general objective of improving the speed, reliability, and quality of decisions. Topics covered include sampling, inferential statistics, and linear regression. The course uses real-life datasets as illustrations and R to build answers to business questions.
BAN 5501 - Database Management and SQL for Analytics
This course serves as an introduction and overview of the Database Management Body of Knowledge (DMBOK) from a managerial perspective. The learning objectives will be to acquire a comprehensive understanding of 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 both 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
Machine Learning and Intermediary Statistical Modeling for Analytics provide an overview of techniques drawn from machine learning, data mining, and statistics. These two courses aim to prepare students with an intellectual framework for problem-solving.
This course emphasizes using mathematical modeling and scenario optimization to reach optimal business decisions. As such, this course is a data science course with an emphasis on statistical methodologies. At the same time, the course emphasizes the practical aspects of Business Analytics by embedding the methods in applications and by underlining the general objective of improving the speed, reliability, and quality of decisions. Topics include logistic regression, cross-validation, model selection, nonlinear methods, decision trees, dimension reduction, and clustering. If time allows, support vector machines and time series forecasting are also discussed. The course uses real-life data sets as illustrations, and R and Python to build answers to business questions.
BAN 5573 - Visual Analytics and Business Intelligence
By leveraging enterprise information assets, business intelligence tools and technologies can help businesses become more efficient and effective in their operations. Business Intelligence utilizes technology, expertise, knowledge, statistics, and creative thinking to identify problems and provide solutions to them. The focus of this class is to learn about enterprise approaches to business intelligence through case studies, decision support systems (DSS), development methodologies, and enabling technologies. This course will provide students with the experience to conduct an analytic project from gathering the data to interpretation in the business intelligence technologies such as Tableau, KNIME, frontline solver, LINGO, and others.
The analytics content is divided into three parts: descriptive, predictive, and prescriptive analytics. The first four to five weeks will be spent learning descriptive analytics and will be performed in Tableau. The next four to five weeks will be spent learning predictive analytics methods using KNIME software. The remaining weeks will be utilized to learn linear programming and other prescriptive analytics methods using Excel Frontline Software, and LINGO. This course also involves a project in the form of a creative component. The details are given later in the syllabus.
By the end of the course students will develop an understanding of the role of computer-based information systems in direct support of managerial decision making."
BAN 5650 Applied Business Analytics
The goal of this course is to cultivate students’ capability to apply data analytics and decision support modeling to industry decision problems characterized by complex market and regulatory environments and competing demands for resources. Students will learn a framework for quantitative decision making and effective resource allocation under uncertainty that is applied today in many Business Analytics and decision making contexts. The course will focus on (1) identification and collection of relevant data for analysis; (2) identification and application of the appropriate models and techniques (e.g., capital budgeting, cost benefit analysis, optimization, and Monte-Carlo simulation); and (3) structuring the decision problem in terms of strategic alignment, feasibility, cost effectiveness, and risk. By the end of the semester, students will understand how to assess the business context and apply Business Analytics skills to the managerial decision problem; structure and implement a complex decision analysis; select appropriate data and analytical methods and build spreadsheet models; apply project management principles and tools to the completion of complex analysis; and present and defend an analysis and recommended investment program
BAN 5600 - Advanced Big Data Computing and Programming
The astounding growth of data in all aspects of life in the form of emails, weblogs, tweets, sensors, videos, and text has necessitated the use of Big Data and advanced analytics techniques to support large-scale data analytics. The goal of this course is to enable students to design and build Big Data applications through highly scalable systems capable of collecting, processing, storing, and analyzing large volumes of structured and unstructured data.
By extending the Cross-Industry Standard Process for Data Mining (CRISP-DM) to build Big Data applications using distributed and parallel computing architecture, this course brings together key Big Data tools on Hadoop Ecosystem (such as Pig, Hive, Flume, Sqoop, and Spark). Students will learn how to efficiently manage and analyze data with three main characteristics: high volume, high velocity, and high variety.
Topics include the Hadoop Ecosystem platforms such as Hortonworks Sandbox, Amazon AWS, and Databricks; and advanced analytics techniques such as Visualization, Natural Language Processing, and streaming analytics.