Curriculum

Foundational principles making computers learn, plan, and solve problems autonomously; and driving modern intelligent agents on real-world applications for contemporary problems, such as deep learning, data flows, and autonomous driving.

This course introduces fundamentals about data and the standards, technologies and methods for organizing, managing, curating, preserving, and using data. The course will teach students the use of software such as Python for data manipulation, analysis and visualization. The course also incorparates broader issues surrounding data, including technologies, behaviors, organizations, policies, and society. Special attention will be given to ethical issues surrounding data, soical and historical perspectives on data with ethics and policies to help students develop a workable understanding of current ethical issues in data science. Finally, the ethical issues will be addressed that arises throughout the lifecycle of data - from collection to storage to analysis and application.

To compete in a data-driven world, data analytic skills and database skills are key. Before data is analyzed, correct data first needs to be chosen and pulled from a database within your organization or your client's organization. While the term big data is influenced by the rise of unstructured data (no-SQL database), structured data (SQL/relational database) remains a large and important component because structured data is driven by business processes and workflows. This course mainly focuses on process-driven/structured data and a relational database. This course is not designed to develop database building skills. A large focus of this course is placed on an understanding of database schema (or how business data is collected in relation to other business data) and SQL coding techniques for selecting the right data for the purpose of further analysis.

This course introduces students to basic mathematical and statistical methods and models, as well as their software applications for solving business problems and/or in making decisions. Included topics are linear regression, analysis of variance, introductory time series analysis & forecasting and several advanced applications of the general linear model. This course uses numerous case studies and examples from economics, finance, marketing, operations and other areas of business to illustrate the realistic use of statistical methods.
Prerequisite: Take BUAN-651

Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. This course is an introduction to the principles and techniques for data visualization. In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from data sets across categories, space, and time.
Prerequisite: Take BUAN-651

Data mining involves decision making by detecting patterns, and cluster analysis. This course introduces data mining techniques, real-world applications and its challenges. A number of well-defined data mining tasks such as classification, estimation, prediction, affinity grouping and clustering, and data visualization will be discussed. The course will provide students with a sound understanding of how to utilize data mining to enhance business productivity in a variety of business applications.
Prerequisite: Take BUAN-660

The course utilizes an integrative team project that gives students the opportunity to demonstrate an understanding of the core competencies taught throughout the program and apply them to real business concerns.
Prerequisite: Take BUAN-651

In this course, students learn the concepts and development of analytical model building as used in global supply chain decisions. Topics include forecasting and inventory management, sales and operations planning, transportation, logistics and fulfillment, purchasing and supply management, supply chain risk management, etc. in manufacturing, trade and service industries. Students learn to define the right data set, ask the right questions to drive supply chain efficiency and business value and use the right models and tools to develop data-driven decisions. Software packages such as Python will be utilized.

Pricing and revenue analytics is a set of practices and tools that firms use to optimize product & service choices, pricing, and promotion strategies. Students will be able to identify and develop opportunities for revenue optimization in different business contexts including the retail, telecommunications, entertainment, financial services, health care, manufacturing, among others. Adoption of these modeling techniques in the on-line advertising, online retailing, and online markets will also be discussed.
Prerequisite: Take BUAN-651

This course explores the strategic role of analytics and business intelligence in an organization. Students will learn to evaluate the strategic environment of an organization, use strategic models to formulate a strategy and the implementation of that strategy. The course will then emphasize the interplay between analytics and strategic considerations in an organization. Students will learn the practicual application of analytics to formulate an organization's strategy and reversely the influence of the organization's strategy to nature of the analytics within the organization.

This course provides an understanding of machine learning techniques. It offers the concepts and the tools the students need to implement programs capable of learning from data.

This course presents a number of cloud computing tools and technologies, including virtualization, web services, data analysis, and integration.

This course will provide advanced concepts of Python script programming. Topics covered include Functions, Design with classes, Multithreading, Networks, Client/server programming, Searching, Sorting and Complexity analysis.
Prerequisite: Take CS-504

Big Data Analytics is about harnessing the power of data for new insights. The course covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry.

The objective of the course is to learn about FinTech - the technologies that are disrupting traditional financial services - and how it is changing areas such as mobile payments, trading, lending, capital markets, and asset management. The course will survey emerging issues in FinTech, enabling students to understand key transformations happening in the financial services industry and the trends that will impact the industry in the future.

Course Title - Digital Currencies: Blockchain and Cryptocurrency We will cover the mainstream blockchain-based digital currencies (e.g., Bitcoin, Ethereum, and Uniswap), stable coins (e.g., MakerDAO, USDT, etc.), as well as Non Fungible Tokens (NFT). We will also discuss the Central Bank Digital Currency (CBDC) and its interdependency with blockchain technology. We will focus on the financial aspects of digital currencies, including valuation, trading, liquidity, investment, collection, and regulation. Students will create their own digital currencies.

Course Title - Artificial Intelligence and Financial Technology in Financial Markets. We will cover a variety of applications of AI and Fintech in the financial markets, including streamlining credit and loan transactions, automating and personalizing financial services, predictive analysis for investment and risk management, fraud detection and regulatory compliance, as well as direct and cost-effective fundraising. Students will create their own crowdfunding projects through ICOs and NFTs.

The course is intended to provide an understanding of the role of modern financial theory in portfolio management and to present a framework for addressing current issues in the management of financial assets. Topics to be covered during the semester include trading, valuation, active portfolio management, asset allocation, global diversification, performance measurement, financial derivatives, and fixed income securities.

The course emphasizes modern methods of risk management. Lectures cover risk measurement and estimation, management, control, and monitoring of risk positions. The impact of risk management tools such as derivative securities will be examined. Regulatory constraints and their impact on risk management will also be assessed. This course also provides a comprehensive and in-depth treatment of valuation methods for derivative securities. Extensive use is made of continuous time stochastic processes, stochastic calculus, and martingale methods. The main topics to be addressed include A.) European option valuation, B.) exotic options, C.) stochastic interest rate, D.) stochastic volatility, E.) American options, and F.) some numerical methods such as Monte Carlo simulations. Additional topics may be covered depending on time constraints.
Prerequisite: Take MFIM-638 MFIM-636

This course analyzes the theory and practice of modern investment management. Topics include quantitative concepts, portfolio analysis, capital asset pricing theory model, performance measurement, efficient market hypothesis, portfolio management process, use of derivative securities, ethical and legal considerations, and professional standards. The course will also provide students with a concise introduction to recent results on optimal dynamic consumption-investment problems. Lectures will also cover standard mean-variance theory, dynamic asset allocation, asset-liability management, and lifecycle finance. The main focus of this course is to present a financial engineering approach to dynamic asset allocation problems of institutional investors such as pension funds, mutual funds, hedge funds, and sovereign wealth funds. Numerical methods for implementation of asset allocation models will also be presented. The course also focuses on empirical features and practical implementation of dynamic portfolio problems.
Prerequisite: MFIM-636 MFIM-638 MFIM-640

Introduction to organization, economic, culture, policy, and terminology of healthcare for non-health professionals. This also introduces the students to fundamental terminology, practices, and processes found in clinical and business operations.

This course is designed to equip students with the analytics skills to select, prepare, analyze, interpret, evaluate, and present clinical and operational data to support data-driven business decision making in healthcare for the purposes of improving outcomes (effectiveness, quality, efficiency). Students will learn and understand how to explore the use of predictive modeling and analytics, optimization, and business intelligence to support data-driven decisions pertaining to healthcare. Topics includes methods for descriptive analytics, data analysis with publicly available healthcare datasets, and an introduction to predictive analytics in healthcare.

Big Data Analytics is about harnessing the power of data for new insights. The course covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry.

Utilization and leveraging of healthcare data can drive improvements in our nation's entire healthcare system as well as in the medical and economic wellness of patients through sharing practical guidance and unbiased information on how to harness these healthcare data and facilitating problem-solving, solution sharing, and education through the collection and analyzing of healthcare data.
Prerequisite: Take HINF-501

Big Data Analytics is about harnessing the power of data for new insights. The course covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry.

This course explores the tools and techniques used by marketers to analyze customer behaviors. It examines databases, analytics, metrics, software, and techniques applied by marketers to transform data into useful formats for the strategic decision-making process. Contents focus on technology tools for segmentation, target marketing and positioning, media selection, market share and estimation, sales forecasting, and other analyses. This course explores the use of machine learning algorithms in the developing of marketing models related to consumer segmentation, market basket analysis, customer lifetime value, predictive marketing, consumer choice, and pricing.
Prerequisite: Take MK-661, MK-670

This course explores how companies assess marketing performance. It is a survey course covering a variety of return on investment metrics for marketing investments. The course introduces formulas and ratios used to gauge customer profitability, product portfolio mix, and advertising and web spending effectiveness. Attention is drawn to links between finance and marketing.

This course will present a practical approach to the process of decision-making using big datasets as a result of acquired or aggregated data.
Prerequisite: Take MK-661, MK-670