Master of Science in Business Analytics (MSBA)

One of the most promising applications of artificial intelligence is in organizational and business decision-making. This course introduces the emerging field of decision intelligence – using AI technologies to make optimal decisions efficiently. Students will learn how AI systems use normative theory principles and machine learning algorithms to produce decision recommendations. In addition, this course covers the design of AI agents, decision analysis, decision modeling, decision-making techniques and building blocks, decision intelligence solutions and providers, decision intelligence framework, and AI ethics for decision-making. Prerequisite: MAT114 or Equivalent

Data is important for every business. After all, we base business decisions and strategic directions on data and how it is interpreted. Visualization of data helps us to understand the data better and see the detail in the data. In this class, the students will learn the basics of data visualization principles and techniques. The students will begin with identifying problems, gathering data, and using different visualization techniques; they will interpret the findings and solve problems. The students will also gain practical experience by using Tableau software to create visualizations. Prerequisite: MAT114 or Equivalent

Big Data Analytics is one of the fastest-growing areas today. In this class, the students will be exposed to big data concepts, tools, and techniques. Analyzing big data sets will provide solutions to businesses. The students will see the difference between large scales of big data as well as traditional data. They will have hands-on experience with Hadoop and other popular big data tools. Prerequisite: MAT114 or Equivalent

This course will help students learn how machine learning algorithms are used to derive value from business data. They will learn to apply supervised and unsupervised learning algorithms using Python language. The emphasis will be on predictive modeling using regression, classification, and clustering techniques. Other topics include exploratory data analysis, data normalization and standardization, logistic regression, decision trees, dimensionality reduction, and ensemble methods. Prerequisite: MAT114 or Equivalent

This course provides students the opportunity to use knowledge and skills gained through various courses in this program and build projects that solve real-world business problems. Each student will complete a practicum, a field research assignment required for graduation. In addition, they will analyze cases and provide solutions that can be applied practically. This course must be taken during the final term of your program. Business Analytics Capstone

This course introduces the fundamental concepts of data analytics and prepares students to gather, describe, and analyze data, to support important business decisions. Students will learn to use advanced statistical tools and analytics methods to synthesize, analyze, and visualize data covering different business scenarios and problems. Topics include introduction to business intelligence, data science & analytics, descriptive analytics, data visualization, predictive analytics, statistical modeling, prescriptive analytics, big data concepts and practices. Prerequisite: MAT114 or Equivalent

Most organizations apply BI technologies in order to advance the organization strategically. This course will present the students with detailed knowledge of the collection, analysis, and presentation of data to solve business problems. They will explore technologies that make up BI, technological architecture that makes up BI systems, and implementation of BI systems. With hands-on exercises and activities, they will learn to use several BI tools and technologies and their benefits for organizations of all sizes. Prerequisite: MAT114 or Equivalent

This course is designed to help students develop basic R and Python skills for data analysis. In addition to learning the functionalities of R and Python, students will also explore the similarities and differences between these two languages. Students will learn data loading and cleansing, and perform complex data analysis using a variety of statistical methods to answer real-life business questions. Among other methods, this course will focus on using R and Python for descriptive analysis, correlation, regression analysis, and time series analysis. Prerequisite: MAT114 or Equivalent

This course provides an overview of relational and non-relational database management systems (DBMS) along with techniques for the storage, manipulation, and security of data. The role of databases within analytics will be explored, as well as different data types, constraints, querying, and best practices for efficiently utilizing and designing databases. Prerequisite: MAT114 or Equivalent

This course will examine the types of information that are collected by organizations and individuals and the risks associated with storing sensitive and confidential data. This course will also introduce compliance and legal considerations within different contexts to inform the security controls that should be adopted to secure critical assets. Prerequisite: MAT114 or Equivalent