Online MSBA Curriculum at the Leavey School of Business

This course provides a practical introduction to linear algebra with a focus on its applications in data science and machine learning. Students will learn essential matrix operations, vector spaces, eigenvalues, least squares regression, and dimensionality reduction techniques such as Principal Component Analysis (PCA). The course also introduces Singular Value Decomposition (SVD) and explores how neural networks leverage matrix computations. Rather than focusing on deep theoretical proofs, this course emphasizes intuitive understanding and hands-on implementation using Python (NumPy, SciPy, and scikit-learn). By the end of the course, students will be able to apply linear algebra concepts to real-world datasets and build a strong foundation for further studies in data science, AI, and machine learning.

This course is designed to provide a comprehensive background in the mathematical topics required for learning Quantitative Finance (QF) and Business Analytics and Data Science (BADS) for the rest of the MSBA online program. The mathematical topics covered include Calculus, Linear Algebra, and Probability Theory. Applications of these topics in a variety of business contexts will be included.

Information systems often utilize a database for storage and management of system information. Vast amounts of valuable business data are stored in these databases. To develop information systems that utilize databases, or to access this information for analysis in business decision making, developing and programming database queries is required. This course presents querying of databases using the Structured Query Language (SQL). The topics include: query syntax such as clauses, operators, and aggregate functions, joins and subqueries; SQL data analytics with various functions and full-text search; web database applications; and advanced SQL topics including stored procedures, triggers, events, and concurrency control.

Data science involves the application of scientific methodologies to extract understanding from and make predictions based on data sets from a broad range of sources. Data science involves knowledge and skills from three areas: programming, math/statistics, and domain specific expertise. The objective of this course is to teach the programming skills relevant to data science. Students will learn the Python programming language, along with a complete set of open source tools for data science in Python, including the IPython Notebook, NumPy, SciPy, Pandas, matplotlib, scikit-learn, and many others. Students will learn skills that cover the various phases of exploratory data analysis: importing data (SQL, web, JSON, CSV), cleaning and transforming data, algorithmic thinking, grouping and aggregation, visualization, time series, and statistical modeling/prediction, and communication of results. The course will utilize data from a wide range of sources and will culminate with a final project and presentation.

Prepares managers to identify the competitive advantages that come from leveraged analytics, apply and implement tools, evaluate advantages and limitations, ask relevant business questions, and interpret and communicate the output from tools and models to achieve profitable business decisions.

This course introduces participants to quantitative techniques and algorithms that are based on big and small data (numerical and textual). We also analyze theoretical models of big systems for prediction and optimization that are currently being used widely in business. It introduces topics that are often qualitative but that are now amenable to quantitative treatment. The course will prepare participants for more rigorous analysis of large datasets later in other Online MSBA classes, as well as introduce machine learning models and data analytics for business intelligence.

Covers the basic conceptual foundations and tools of econometrics and applies them to case studies with real-world data. The key statistical technique used in this course is multiple linear regression and R-programming.

This course helps students understand the principles of optimization in business decisions, prepare computer-based models from problem descriptions, and determine optimal solutions using software tools. This course also prepares participants to interpret solutions to obtain insights regarding sensitivity to inputs, resource constraints, and their profitability impacts.

Practicum projects are defined by external partners who provide a dataset and the question of interest. Starting from a real-world problem and using input from the partner, students will refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, summarize the findings in a report, and present them to the partner or faculty supervisor. Practicums occur over two quarters and are worth 2 units of course credit each quarter, or 4 total units. The practicum will serve as a culmination of all of your work throughout the Online Master’s in Business Analytics program.

Capstone projects use data and examine analysis questions provided by faculty over the course of one quarter. Students refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, and summarize the findings in a final report. In certain situations, students with full- or part-time jobs or with an internship can approach their employers to find a suitable project if desired. In those instances, the employer and faculty would provide supervision.

This course is about Big Data and its role in carrying out modern business intelligence or actionable insight to address new business needs. This course is a lab led and open source software rooted course. Students will learn the fundamentals of Hadoop framework, NoSQL databases, and R Language. The class will focus on storage, process analysis, and aspects of Big Data. Students will have access to a MapR Hadoop Image. The image is enhanced by the instructor to include MOngoDB and R.

Reinforcement Learning is introduced as a way to do optimal control in cases when a system model is not available and information about the Value Function is obtained by analyzing its sample paths. RL Algorithms, Temporal Difference Learning, Q-Learning, on-policy and off-policy learning, policy exploration vs. exploitation, Deep Learning Neural Networks as function approximators for RL systems, The Deep Q Network (DQN) algorithm, Policy Gradient methods such as the REINFORCE algorithm in combination with Value Function and Policy Gradient methods are explored. Applications of these concepts in the areas of game playing systems, finance, and robotics are also discussed.

Computing is migrating to the cloud. In this course, you will understand computing-as-a-service concepts by using services from major cloud providers and learn how to deploy and manage cloud infrastructure. This course focuses on hands-on skills required to operate on prime cloud service platforms, like AWS. This course will offer an applied perspective on the core features of a cloud computing platform, such as infrastructure-as-a-service, serverless computing, cloud storage, Restful API, Lambda functions, load balancing, etc.

This course enables you to transform data into persuasive dashboards that effectively inform and guide management actions. Dashboards are persuasive if they motivate actions in an intended audience. Dashboards are effective if they offer comprehensive and reliable information. This course introduces and discusses the fundamental design principles and technology of dashboards and allows you to design, implement, and critique dashboards.

This course enables you to explore data, identify insights, and develop evidence-based arguments using data visualization techniques. Completing this course equips you with a moderate level of data literacy, the ability to interpret, construct, and convey arguments through the functional and truthful visual presentation of data. You will wrangle data, customize data visualization technologies, and programmatically develop data visualizations.

This course presents technical and managerial approaches to the analysis, design, and management of business data, databases, and database management systems. The topics include structured and unstructured data management, a comparison of relational and object-oriented databases, relational database conceptual and logical design, and database implementation and administration.

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The widespread adoption of hardware virtualization and the availability of low-cost computers and storage devices with high-capacity networks together with service-oriented architecture has led to growth in cloud computing. This course will study what technologies make cloud computing possible and how IT leverages these technologies to make the enterprise computing environment more efficient. There are three parts to this course. The first part will study how hardware virtualization is made possible through computer architecture advancement. The second part will discuss the two main solutions in the virtualization layer which are hypervisor-based virtualization and container-based virtualization. The third part of the course will study the microservices and the containers workflow orchestration. This course includes hands-on labs in virtual machine creation based on different technologies like hypervisors (VMware) and containers (Docker). We will also explore different workflow orchestration tools like Docker Swarm and/or Google Kubernetes.

This course teaches students the fundamentals of Natural Language Processing (NLP), which will be necessary for the remainder of the Online Master’s in Business Analytics program. NLP has recently found several applications in business. There is now a foundation of content that students who wish to work in this field need to know and this course is aimed at providing students with a conceptual understanding of the field and its business applications, and a technical toolkit to implement NLP models.

Introduction to the topic of Deep Learning Neural Networks (DLNs), Linear Learning models using Logistic Regression, and adding hidden layers to create Deep Feed-Forward Neural Networks. Detailed algorithms are used to train these networks using Stochastic Gradient Descent and the resulting algorithm called Backprop. Training processes of these networks are used with the Tensor Flow tool and the MNIST and CIFAR-10 image data-sets. Some specialized DLN architectures include the following: (a) Convolutional Neural Networks (ConvNets), (b) Recurrent Neural Networks (RNNs), and (c) Reinforcement Learning. Model parameter initialization, underfitting, and overfitting are discussed as well as techniques such as Regularization. Issues such as the Vanishing Gradient problem that often cause problems during training are also discussed.

This course is designed to provide a comprehensive introduction to forecasting methods used in time series analysis. The class covers a range of topics in time series forecasting. The class will provide you with a language to describe time series data and ultimately cover modeling techniques such as ARIMA, SARIMA, and GARCH to produce forecasts.

This course covers key issues in panel data analysis, with an emphasis on their applications in empirical research, especially empirical corporate finance. The course aims to introduce various econometric methods for analyzing panel data and develop core techniques to identify causal relations in the data. We will begin with the standard linear regressions, and extend to pooled, fixed effect, and random effect regression models; instrumental variables; differences-in-differences; selection models; and regression discontinuity. Students will be exposed to a broad range of applications in finance through reading academic papers and conducting their own empirical analysis.

This course will introduce economic foundations for managerial decisions. The course analyzes the economic behavior of individuals and firms and explores how their interactions in markets affect managerial decisions. Basic concepts of market, price elasticity, theory of consumer and theory of firm will be studied to incorporate economic theories in managerial decision-making. How key managerial decisions are made in different industrial structures will be discussed.