Master of Science in Business Data Analytics (MSBDA)
The Master of Science in Business Data Analytics's 12-course curriculum prepares you to be a responsible leader in the fast-growing field of business data analytics—in just one year.
The following table shows a sample course sequence for the M.S. BDA program:
Summer Quarter (Prerequisite courses)
This course uses tools of economic analysis to understand demand, supply, profits, production, competition, pricing policies, business criteria for investment, output, and marketing decisions.
Students are able to do critical managerial decisions with respect to output and pricing policies in different business and industrial environments.
The goal of this course is to provide students with an understanding of managerial finance: valuation, capital investment, financing, capital structure, and business ethics as they relate to finance.
Outcome: Students will demonstrate knowledge of financial analysis, time value of money, risk-reward, asset valuation, capital budgeting, capital structure, and working capital management.
This course develops a broad understanding of the marketing principles that undergird successful marketing strategies and marketing plans with special attention given to international and ethical considerations.
Outcome: Students use and apply marketing principles, strategic research, consumer analysis and target marketing to either a project or to case studies
The fundamentals of managerial statistics are presented. Topics may include descriptive statistics, random variables, probability distributions, estimation, hypothesis testing, regression, and correlation analysis. Statistical software is used to assist in the analysis of these problems.
Outcome: Students will be able to demonstrate understanding of statistical thinking and data analysis technique for decision-making purposes
This course uses database systems as the focus for studying concepts of data modeling and data manipulation. Procedures for creating, managing, sorting, and processing data are discussed. Concepts of relational database methods are covered as well as the issues that arise in managing information in a database and using it to support business processes.
Outcome: Understanding the development and use of business database systems.
This course examines the ethical aspects of individual and corporate decision making in business and provides resources for making ethical decisions within the context of managerial practice.
Outcome: Students will be acquainted with the concepts and principles of ethical reasoning that have been developed in ethical theory; be aware of the specific ethical issues that arise in management and of the ways in which these issues are commonly analyzed; and be able to make sound ethical and managerial decisions and to implement those decisions within the context of an organization in a competitive marketplace.
Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.
Outcome: To be able forecast business and economic variables to enhance business decisions.
Data Mining involves the search for patterns in large quantities of data. The fundamental techniques used in data mining include, but are not limited to, clustering, decision trees, neural networks, and association analysis.
Outcome: The student will be able to build models using an industry-standard package and interpret the results.
The capstone course is the last class students take in the MS Business Data Analytics program. As such, it asks students to integrate all the knowledge and skills they've developed in the foundation and elective courses, and to apply their efforts to solve a real-world business problem. The Capstone Project Coordinator will work with a set of host companies to identify projects that are suitable for students enrolled in the capstone course.
Students completing this course will:
- Gain a deep understanding of some of the tools and software that are used in modern-day analytics
- Learn how to transform large data sets into insightful and actionable information in an easy-to-understand format to assist organizational decision-making through the use of advanced analytical tools;
- Learn how to evaluate the appropriate methods and tools for data analysis in specific organizational contexts, including selecting a modeling approach, building a model using appropriate tools, validating the model, and deploying the model for prediction and analysis.
- Acquire experience tackling industry-specific problems and challenges using advanced analytics and computational methods.
Choose your five electives from the following three areas, with at least one elective from each of the areas.
This course studies the economic environment¿s impact on the firm where topics include national income accounting, factors in economic fluctuations and growth, fiscal and monetary policies, economic forecasting, the relationship of foreign trade and balance of payments on economic activities, economic indicators and measures, and problems of public policy.
Outcome: Students learn to recognize the macro environment and the business cycles in which to operate in and to make learned forecasts.
Prerequisite: ECON 420
This course studies cooperative and non-cooperative games and winning strategies and discusses prisoners dilemma, tragedies of common resources, executive compensation and auctions as applied to mergers and acquisitions.
Outcome: Students learn to think systematically to set strategy for the modern corporate firm in both cooperative and non-cooperative situations and to solve conflicts arising from principal agent problems.
Prerequisite: FINC 450
This course is an introduction to options, futures, forwards and swaps as derivative securities. After an overview of these securities, a detailed examination of the methods of valuing options will be presented. Binomial trees and a discussion of the Black-Scholes option pricing model will be emphasized, followed by insights into option contracts as useful risk management instruments. A brief introduction to stochastic calculus is also given. Stock, index, debt, commodity, foreign currency and futures options are reviewed, and option strategies are analyzed as managerial tools in financial decision-making. Skills developed in this course include analytical and decision-making, creative thinking and communication. Throughout the course the notion of risk both as potential loss and opportunity for gain and its management will be highlighted. Ethical and social dimensions of risk management and the use and abuse of derivative securities will be emphasized to help students become responsible financial managers. The recent credit crisis and its origin in subprime mortgages will be reviewed. Students are encouraged to form teams and work jointly on five sets of homework problems and to also develop trading strategies. The course integrates functional areas in finance, accounting, economics, business ethics and quantitative methods.
Prerequisite: FINC 450.
This course includes the topics of asset pricing models; risk and return analysis of stocks, bonds and cash equivalents; portfolio theory; bond pricing, the term structure of interest and immunization strategies in managing fixed income securities.
Outcome: Students will be able to demonstrate the analytical tools and finance theory necessary for making good investment decisions and for understanding the pricing of financial securities.
Prereq: FINC 452
This is a course in investment analysis and applied portfolio management. Topics will include investment policy and objectives, performance analysis and attribution, portfolio design, fixed income analysis and portfolio management, and equity analysis and portfolio management.
At the conclusion of this course the student should be able to:
1. Create an investment policy statement
2. Analyze and value fixed income securities
3. Analyze and value equity securities
4. Develop and manage a portfolio of debt and equity securities
Prerequisite: Prerequisites: FINC 450 and ISSCM 491; or FINC 335 with minimum grade of "B"
This course focuses on how to effectively use Microsoft Excel and its built-in programming language, Visual Basic for Applications (VBA) to build financial models. It has a prerequisite of Finance 450 and presumes familiarity with basic Excel operations and functions. The course will model investment, derivative, corporate finance, and risk management problems. The course is a combination of both lecture and lab.
Prerequisites: FINC 622 or FINC 346 with minimum grade of "B"
Students are introduced to a plethora of financial derivatives, including both exchange-traded and OTC products, and then learn to use these products to hedge interest rate and other risks largely through the study of cases and detailed examples emphasizing the formation and use of synthetic positions.
Outcome: Students will be able to demonstrate an understanding of a wide variety of derivative products, as well as be able to use these products to manage interest rate and other risks.
Prerequisite: FINC 622 or FINC 346 with minimum grade of "B"
We study credit risk and credit risk management. We examine a suite of financial securities which can be used to reduce (or magnify) credit risks, especially credit default swaps (CDS), asset-backed securities (ABS) and collateralized debt obligations (CDOs). We consider the role of these structured credit products in the global financial crisis.
Prerequisite: FINC 450 or FINC 342 with minimum grade of "B"
This is an advanced course in valuation where students are given a thorough grounding in traditional valuation models (DCF and relative valuation) and also introduced to real option methods and ideas; a certain emphasis is placed on the valuation of start-ups and students are introduced to the venture capital markets.
Outcome: Students will be able to demonstrate an understanding of traditional valuation models as well as real options methods and ideas.
Prerequisites: MARK 460 and ISSCM 491.
This course develops an understanding of the marketing research process and the role of survey research in it.
Outcome: Students formulate research problems and a design research study, including the development of a questionnaire, selection of an appropriate sample and data analysis.
In this course the students will study how to use data analytics to learn about customer needs and improve targeting individual consumers. The course will encourage students to apply scientific methods and models to predict and respond to customer choices. This is the key part of learning Big Data. The term Big Data is viewed in the broad sense as it relates to various aspects of the consumer behavior, which may be captured, measured, and transformed to the digital form.
Through applications of statistical models to the analysis of the real-world databases, the students will learn how firms may use customer data to serve customers better.
This course is designed with marketing managers in mind. As profession marketing is evolving, it is no longer based primarily on the conceptual content. Marketers get exposed to thousand times the volume of data she(he) saw five years ago. More data cannot lead to better decision making unless managers learn how to use that data in meaningful ways. In this course, the students will be introduced to the analytical decision models that assist modern managers in making marketing decisions related to the targeting, product design, communications, etc.
Outcomes: The objectives of this course are the following: 1. To learn analytical techniques and decision models for enhancing marketing decision making in the modern organizations 2. Improve skills to viewing marketing processes and relationships systematically and analytically 3. To learn power of decision models applied in the real managerial contexts 4. To provide students with toolkit that may be used to assess and measure return on marketing investments in organizations
This course is designed as an introductory graduate level course in analytical problem solving, another name for research methods, and design. A basic understanding of - and general familiarity with fundamentals of statistical concepts is assumed. However, where necessary, we will revisit these concepts briefly. Of course, this is not a course in statistics.
Information Systems and Supply Chain Management
The amount of data that our world generates is growing at a torrid pace. Sifting through & making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries & advancements. Data visualization plays an important role in this process.
Outcome: Students will be able to process & visualize large amounts of data in order to enable efficient & effective analysis using industry standard software.
The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses are emphasized. Oracle tools will be used to demonstrate design, implementation, and utilization issues.
Outcome: students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses.
The art and science of project management as applied to a variety of business and technical projects in commercial, public, and private sectors. Covers: project life cycle and methodology; teambuilding; project organization, stakeholders and leadership; proposals and contracts; techniques for project planning, estimating, scheduling, and control; PMO.
Outcome: Understanding of the broader role of the project manager with regard to all project stakeholders, and of methods, tools, and procedures for initiating, defining, and executing projects.
Introduction to concepts and methods for managing operations in manufacturing and service organizations. Topics typically include forecasting, capacity and aggregate planning, material requirements planning, scheduling, facility layout and location, inventory management, just-in-time, total quality management, project planning, and logistics.
Outcome: Students will understand the basic issues and role of operations management in organizations and learn tools for problem-solving in operations management.
This course examines how business partners along the supply chain can work together to gain competitive advantage in moving products and services around the world to satisfy customers.
Outcome: Understanding best practices like vendor-managed inventory and category management, and the application of information technologies for sharing information.
A study of organizational procurement processes and decision making framework. Topics include in-sourcing/out-sourcing decisions based on total cost of ownership; purchasing cycle and processes; developing material and technical specifications; supplier evaluation, selection and management; supplier quality management; purchasing capital goods and services; global sourcing and e-commerce; and purchasing tools and analytics.
Outcomes: Students will have developed an understanding of fundamental and strategic issues in material planning and procurement, with the ability to source in a global marketplace.
A study of the fundamental principles of effective management of inventory with emphasis on inventory costs, product stratification, performance measures, demand forecasting, periodic and continuous review, safety stock, material requirements planning, customer service and use of technology in inventory management. Issues related to storage and handling of inventory stock are also studied.
Outcomes: Students will have developed an understanding of the issues involved in planning, managing and control of inventories and materials in a supply chain.
A study of the design, development, and use of decision models for analysis of supply chain problems. This course provides an example-driven approach to learn about important supply chain models, problems, and solution methodologies. The objectives of this course are to develop valuable modeling skills that students can appreciate and use effectively.
Outcomes: Students will have developed an understanding of the issues involved in the use of decision support tools for analysis of supply chain problems.
* Recommended courses
Loyola's Quinlan School of Business invites you to join us at our downtown Chicago Water Tower campus for an Open House or Information Session. Please contact us with any questions about our business data analytics courses.