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2020 MSBA Courses
First Term: September-October
This course develops fundamental knowledge and skills for applying statistics and analytics methods to make data-driven decisions in business and communicate insights. Topics include Descriptive Statistics, Probability and Probability Distributions, Interval Estimation, Hypothesis Testing, Analysis of Variance (ANOVA), and Linear Regression. This course involves the use of Microsoft Excel and introduces students to R programming.
This course uses Python as a modern programming language to introduce basic concepts and techniques of computer programming and software development. Students in this course will learn the skills necessary to build well-designed code modules and data models for business. Topics include data structures, control structures, data input/output, object-oriented programming, exception handling, and debugging.
Second Term: November-December
This course focuses on state-of-the-art data presentation techniques to create and communicate effective visualizations for structured and unstructured data that businesses face today. They will learn how to develop compelling narratives to effectively communicate business data to various audiences. Topics include using data visualization software packages (e.g. Tableau) to present data dynamically, visualization of multidimensional query results, dashboards, animation, personalization, and actionable alerts.
This course focuses on database management systems and procedures with an emphasis on the design and development of efficient business information systems. Students will be trained to effectively manage data through database tools and techniques. Topics include relational database structures, database queries and reports, and database management issues such as concurrency control, data security and integrity. Structured Query Language (SQL) and a structured database software package will be used in the course.
Third Term: January-February
Students in this course will learn advanced techniques used in data analytics including association rules, clustering and other modern classification techniques. This course builds on the Applied Data Analysis course and covers Regression Analysis, Logistic Regression, Non-linear Regression, Time Series Analysis, Nonparametric Methods, Bayesian Probability Updating, and Decision Analysis. The emphasis is on large complex data sets and web mining. Students will continue learning R in this course.
Machine learning is the process of developing, testing, and applying predictive algorithms to predict future outcomes. This course will teach students how to use machine learning to achieve business goals. Students will be trained to formulate ML solutions to real-world problems, carry a project through various ML phases such as training, evaluation, and deployment, and perform AI responsibly to avoid reinforcing existing bias. They will measure, monitor and predict the evolution of key enterprise variables and performance indicators and present them in the form of usable information for business decision-making. Topics include supervised and unsupervised learning, data mining, text mining and big data strategies.
Fourth Term: March-April
In this course, students will learn how cloud computing works as an IT delivery service to enhance business efficiency and agility. Students will learn about cloud computing concepts, cloud applications, and cloud networking. Topics include cloud architectures such as Amazon Web Services, cloud programming, cloud transport using Docker Containers, mobile cloud applications for Internet of Things (IoT), social network analysis using cloud services, cloud performance, and cloud security.
This course teaches students how to build successful financial models using analytics. It employs financial econometrics and predictive modeling using computer software to provide insights into internal and external financial information. Topics include cross-sectional risk and return models such as the Capital Asset Pricing Model (CAPM), price-earnings relationship, portfolio optimization, and market performance forecasting.
Fifth Term: May - June
This course teaches students how to take advantage of the latest developments in data analytics, digital advertising, and predictive modeling to increase an organization’s marketing reach and optimize their return on investment (ROI). Students will learn to develop a digital marketing optimization strategy using key analytics tools. Topics include applications of analytics in key marketing areas including measuring customer preferences, market segmentation and targeting, new product and service design, brand positioning, revenue optimization, customer relationship management (CRM), pricing and yield management, and distribution decisions. Students will utilize commonly used software such as MS Excel and SAS.
This course examines optimization through a Business Analytics lens. Students will learn how to translate business scenarios into mathematical models and use linear and nonlinear optimization applications such as revenue management and portfolio optimization, supply chain design and facility location problems, inventory control models with probabilistic demands, and waiting line models. Topics include Process Analysis, Linear Programming, Integer Linear Programming, Queuing Models, Inventory Models, and Simulation. A mathematical programming language (AMPL) will be introduced in this course.
Sixth Term: July-August
This course teaches students the various risks involved in data management and how to protect digital data. Students will learn about the legal, policy and ethical issues required to become an ethical business data analyst. The course focuses on the dominant ethical standards, laws and codes of conduct regarding data management including data security, data privacy, data accuracy, and data property rights, and unanticipated actions such as data breach, cyber-attacks, data fraud and negligence.
In this capstone course, students will work in teams to apply the knowledge and skills they have learned throughout the program to tackle real life business analytics projects. They will interact with industry professionals, and present and defend their findings and recommendations to faculty and analytics experts.