MSBA | Core Courses



Overview

The MSBA Core Curriculum is divided as follows:

Category Credit Hours
Descriptive 7.5
Predictive 10.5
Prescriptive 4.5
Applications & Electives 7.5
Workshops & Practicum 3.0
Total Credits: 33.0

Descriptive Analytics • 7.5 Credit Hours

Analyzing data to determine what has happened in the past or is happening now.

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DNSC 6201 | Introduction to Business Analytics | 1.5 Credits

DNSC 6201 | Introduction to Business Analytics | 1.5 Credits


The advancement in computing and data management technology has created the opportunity for businesses to store, organize and analyze vast amounts of information (i.e. “Big Data”). This course provides an introduction to business analytic concepts, methods and tools with concrete examples from industry applications.

The semester is organized into two components:

  1. Theoretical & Communication Skill Development: Based on teacher-led dialogue, assigned reading, and videos to represent a variety of thought-leadership viewpoints, students will be introduced to a variety of perspectives related to Business Analytics, Data Science, and Data-Analytic Thinking.
  2. Technical Skill Development: Utilizing the analytic software package SAS JMP Pro 12 for data access, integration/transformation, visualization, and predictive modeling, students will learn how to use the technology in the context of business applications. Students will learn by doing: that is, guided by the instructor and using software, they will prepare and analyze authentic data sets to learn how to develop strategic recommendations for managerial actions.


ISTM 6211 | Programming for Analytics | 3.0 Credits

ISTM 6211 | Programming for Analytics | 3.0 Credits


The ability to handle data that differ in variety, volume and velocity is central to data science and especially to business analytics. This course prepares students to be able to programmatically access, prepare, handle, and process data in all its variety.

The ability to handle and process data is a core capability in the context of any analytics position in the industry. Specifically, students will learn how to prepare data for analytics. Students taking this course will develop a theoretical grounding in emerging paradigms like schema-less data.

This is an application-oriented course and students will be required to complete projects using different technologies in addition to traditional exams associated with classroom lectures. The programming environments that will be typically employed include Python and R.



ISTM 6212 | Data Management for Analytics | 3.0 Credits

ISTM 6212 | Data Management for Analytics | 3.0 Credits


This course provides a practical grounding in relational databases with a focus on data warehousing and dimensional modeling, along with hands-on experience in these tools and other traditional and contemporary methods for managing and analyzing data at scale, such as the Unix command line and Apache Spark.

The course focuses on using these tools for the middle phases of data analysis: wrangling, exploring, and modeling, with an emphasis on delivering reproducible data analyses. This course is complementary to other foundational courses in the Business Analytics program; as such, topics and techniques from Statistics, Programming, Data Mining, and Optimization may be present, but will not be a focal point for grading.




Predictive Analytics • 10.5 Credit Hours

Examining data to discover whether trends will continue in the future.

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DNSC 6203 | Statistics for Analytics I | 1.5 Credits

DNSC 6203 | Statistics for Analytics I | 1.5 Credits


This course introduces the foundations of Probability, along with the commonly used Probability models (Binomial, Normal, and Poisson) in predictive analytics.

Topics covered include:

  • Probability Laws
  • Probability Models for Modeling Dependence
  • Univariate & Bivariate Models and their Applications
  • Conditional Mean Models incl. Simple Regression and Extensions to Probit & Logit Models


DNSC 6213 | Statistics for Analytics II | 1.5 Credits

DNSC 6213 | Statistics for Analytics II | 1.5 Credits


This course extends the foundations for statistical methodologies used in business analytics to “real world” scenarios where the assumptions of the General Linear Model are unlikely to be universally met.

In the process of identifying and rectifying violations of the assumptions of linearity, homoskedasticity, normality, additivity, & uncorrelated errors, the course introduces high-level analytical techniques including hierarchical linear modeling (HLM), & mixed-effects modeling.



DNSC 6206 | Stochastic Foundations: Probability Models | 1.5 Credits

DNSC 6206 | Stochastic Foundations: Probability Models | 1.5 Credits


This course introduces the foundations of Probability, along with the commonly used Probability models | Binomial, Normal, and Poisson) in predictive analytics.

Topics covered include:

  • Probability Laws
  • Probability Models for Modeling Dependence
  • Univariate & Bivariate Models and their Applications
  • Conditional Mean Models incl. Simple Regression and Extensions to Probit & Logit Models


DNSC 6219 | Time Series Forecasting for Analytics | 3.0 Credits

DNSC 6219 | Time Series Forecasting for Analytics | 3.0 Credits


This course focuses on predictive analysis and blackbox models for time series and econometric forecasting. Emphasis will be given to identifying hidden patterns and structures in the univariate and multivariate time series data and exploiting these for forecasting.

Topics include use of smoothing methods, identification of seasonality and trends and nonstationarity, analysis of autocorrelation and partial autocorrelations and their use in identification of univariate time series models.

The second part of the course emphasizes cross correlations, transfer function analysis, matrix autocorrelation and partial autocorrelations and their use in identification multivariate time series models such as vector autoregressive processes. Students will be using SAS throughout the course to apply different forecasting models and methodologies to real life time-series data. Use of R in time-series analysis will also be introduced.



DNSC 6279 | Data Mining | 3.0 Credits

DNSC 6279 | Data Mining | 3.0 Credits


This course provides an in-depth exposure to various supervised and unsupervised data mining techniques that can be used both to discover relationships in large data sets and to build predictive models.

Techniques covered include regression models, decision trees, neural networks, clustering, and association analysis.




Prescriptive Analytics • 4.5 Credit Hours

Studying data to elevate the best course of action for the organization in the future.

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DNSC 6212 | Optimization Methods & Applications | 3.0 Credits

DNSC 6212 | Optimization Methods & Applications | 3.0 Credits


The course offers a practical and thorough introduction to the field of optimization and its versatile applications. The areas covered are linear, network, integer, and nonlinear models, along with the fundamental underlying analytic concepts and solution methods.

The overarching goal is to enable students to acquire the skills, tools, and foundational analytic knowledge to become sophisticated users of optimization technology. Intuitive understanding of solution methods and their underpinning theoretical paradigms is emphasized throughout and is deemed essential for the effective usage of optimization models.

The course also emphasizes model formulation, solving and results interpretation using powerful and popular commercial software.



DNSC 6210 | Decision and Risk Analytics | 1.5 Credits

DNSC 6210 | Decision and Risk Analytics | 1.5 Credits


This course presents essential concepts, methods, and practical tools to analyze managerial decisions involving risk and uncertainty.

Topics covered include strategic options thinking, value of information, real options valuation, risk measures, risk preferences, and risk-return analysis. The tools used for modeling include decision trees, Monte Carlo simulation, and optimization. Some attention will be paid to the role—and limitations—of human judgment, as it is always an input to the decision making process. Sensitivity and robustness analysis will also be demonstrated throughout as a means to deal with the ambiguities inevitably present in real situations.

The methods and tools covered find a wide range of applications in strategic planning, policy analysis, finance, operations, technology development, and management of innovation, among others.




Applications & Electives • 7.5 Credit Hours

Students select 7.5 credit hours from the courses below (list is non-exhaustive)

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FINA 6223 | Investment Analysis/Portfolio Management | 3.0 Credits

FINA 6223 | Investment Analysis/Portfolio Management | 3.0 Credits


This course covers:

  1. Institutional Details of the U.S. Stock Market
  2. Modern Portfolio Theory
  3. Risk Management Tools
  4. Market Efficiency & Portfolio Performance Evaluation
  5. The Valuation of Stocks & Bonds
  6. Applications of Options & their Valuation


MKTG 6290 | Marketing Metrics | 1.5 Credits

MKTG 6290 | Marketing Metrics | 1.5 Credits


Effectiveness and productivity of marketing are fundamental to stock market valuations, which often rest upon the aggressive assumptions about customer acquisitions and market growth. Despite its importance, marketing is one of the least understood, least measured functions at many companies.

As a profession, marketing must evolve beyond relying almost exclusively on conceptual content to drive decisions and actions. In today’s environment, marketing managers require tools and techniques to both quantify the strategic value of marketing initiatives, and to maximize marketing campaign performance.

This course is designed to help marketers demonstrate the return on investment (ROI) of marketing and leverage data from marketing analytics to make better decisions. Content covered is especially applicable to those pursuing careers as marketing and brand managers. Through the use of both lecture material, and case applications, students will apply their knowledge and experience in these areas to solve marketing problems.



DNSC 6290 | Supply Chain Analytics | 1.5 Credits

DNSC 6290 | Supply Chain Analytics | 1.5 Credits


This course focuses on analytical models that are used in the planning, design and operation of global supply chains.

Students are exposed to concepts and models important in supply chain management including topics such as forecasting, aggregate planning, sales and operations planning, inventory management, supply chain network design and planning, and pricing and revenue management.

Emphasis will be on the increasingly important role of Analytics in improving the performance of supply chains.



DNSC 6214 | Pricing & Revenue Management | 1.5 Credits

DNSC 6214 | Pricing & Revenue Management | 1.5 Credits


Pricing and revenue management is concerned with having the right prices in place for all the products a firm sells, to all its customers, through all their channels, all the time and is a tactical decision.

The most familiar example probably comes from the airline industry, where tickets for the same flight may be sold at many different fares throughout the booking horizon depending on product restrictions as well as the remaining time until departure and the number of unsold seats.

The use of such strategies has transformed the transportation and hospitality industries, and has become increasingly important in retail, telecommunications, entertainment, financial services, on- line advertising and manufacturing. Moreover, pricing and revenue management is a growing practice in management consulting services and in software and IT development.

Through a combination of lecture notes, case studies, problem solving, games and a lecture by a guest speaker, the course will review the main methodologies that are used in many of these areas. Most of the topics covered in the course are either directly or indirectly related to pricing issues faced by firms that have some degree of market power.

Within the broader area of pricing theory, the course places particular emphasis on tactical optimization of pricing and capacity allocation decisions, tackled using quantitative models of consumer behavior and constrained optimization.



DNSC 6215 | Social Network Analytics | 1.5 Credits

DNSC 6215 | Social Network Analytics | 1.5 Credits


This course introduces concepts, methods, and applications of network science and prepares students to develop a working knowledge of network analysis.

While the term social networks has become far more popular after social media applications like Facebook and Twitter, the field of network science has been around for much longer but has developed amazingly fast over the last decade. Network science applications can be found in genomics, social media, sales and marketing, organizational communication, recommendation systems, crime analysis and many more areas.

Upon taking this course students will be able to describe and analyze real networks | project teams, emails, the internet, social media networks, etc.) as well as relevant phenomena such as disease propagation, organizational analysis, social power, and fraud detection. In addition, students will learn about specific topics such as recommendation systems.



DNSC 6225 | Business Process Simulation | 1.5 Credits

DNSC 6225 | Business Process Simulation | 1.5 Credits


Every firm needs to manage a variety of processes that generally encompass a number of departments, and consist of several different functions and activities with various process owners. A process has to be efficient and cost effective while serving its overall goal. Non- value added activities, or those that do not directly support the organization’s products, services or customers need to be eliminated or modified.

This course examines the key methods used to analyze, develop and improve processes in a given organization. The objective is to develop an understanding of the trade-offs and limitations involved in process design, as well as to develop an understanding of many of the basic tools used to analyze and improve processes. In addition, students will learn how to test the performance of existing and proposed processes by building simulation models using a powerful discrete-event simulation tool used frequently in industry.

The course is intended to be hands-on and application oriented, and will help students acquire the requisite skills for adopting a process-oriented approach when undertaking major projects.



DNSC 6290 | Visualization for Analytics | 1.5 Credits

DNSC 6290 | Visualization for Analytics | 1.5 Credits


Organizations of all sizes find data visualization platforms to be essential tools that enable them to monitor business processes, discover patterns, and take data-driven action to defend against threats, and obtain opportunities. Many corporations have successfully used — and will continue to utilize — traditional business graphics (i.e. bar and pie charts). However, modern technologies have innovated the usage of more dynamic and interactive graphic experiences.

Now, through new technological capabilities presently existent in data visualization, potential exists for nontraditional and more visually rich approaches, especially in regard to more complex (i.e. thousands of dimensions or attributes) or larger (i.e. billions of rows) data sets, to reveal insights not possible through conventional means.

From pure data exploration to the delivery of valuable data-driven insights, visualization is an important application in the business analytics discipline. Students will learn how to use data visualization software technology in the context of their exploratory and reporting capabilities. Although our focus will be the use of SAS Visual Analytics / Statistics, the course will cover other methodologies.

Using SAS Visual Analytics / Statistics, students will get hands-on experience in class each week analyzing real-world data, focusing on data discovery and communicating insights. Based on in-class demonstrations, in- class case exercises using software, and assigned readings/videos; students will prepare and visualize data using a variety of graphical approaches.



DNSC 6290 | Sports Analytics | 1.5 Credits

DNSC 6290 | Sports Analytics | 1.5 Credits


The primary goal of this course is to introduce you to important contributions that analysts such as yourself can fulfill within the sports industry. This goal will be accomplished by splitting the course time in half between player analytics and sports business analytics.

As a student, succeeding in this course will require the same attributes as are required within the sports industry: 1) Maintain an increasing appetite for information – the challenge in sports is to stay on top of and manage the data; and 2) Share insights with your team members, in this case, your fellow classmates. In most industries, information = power.

As with any industry in which significant revenue is at stake and costs increasingly escalate, sports is ever more dependent on access to information and the leverage of it throughout an organization (i.e. Analytics). Although gaining a competitive advantage via analytics on the field of play has garnered much attention via strategies such as Moneyball (Billy Beane – Oakland A’s), it is just as vital within the business operations that manage the key revenue drivers. As such, this course focuses on both player analysis and competitive business analyses.




Workshops & Practicum • 3.0 Credit Hours

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DNSC 6216 | Business Analytics Skills Workshop | 1.5 Credits

DNSC 6216 | Business Analytics Skills Workshop | 1.5 Credits


This course is structured as a series of workshops designed to expose students to the following:

  • Project Management Techniques for Analytics Projects
  • Team Dynamics Skills
  • Communication Skills
  • Ethics, Security, and Privacy Policies in Analytics


DNSC 6217 | Business Analytics Practicum | 1.5 Credits

DNSC 6217 | Business Analytics Practicum | 1.5 Credits


The practicum constitutes a major final milestone that students are required to fulfill in order to earn their Master of Science in Business Analytics (MSBA) degree. Working in teams of two or three, students are expected to apply their analytics skills to real-life projects, sponsored by public or private institutions, and to produce implementable solutions.

Typically, students will choose from several pre-approved topics that have already been identified by MSBA faculty members in conjunction with several companies and agencies, including those on the MSBA Advisory Board. Alternatively, the practicum topic may be obtained from the students’ current employers, or from other organizations, as long as the topic is approved by the MSBA faculty member assigned to administer the course.

Each team will be advised by at least one faculty member. In addition, each practicum sponsor is required to designate one or more “mentors” to provide guidance to the student team during the duration of the project. At the conclusion of the practicum, each team is required to submit a final report that covers the following items:

  • Problem Context
  • Data Analyzed
  • Analytic Technique

  • Conclusions & Recommendations
  • Appendices | Including All Code Developed for the Project




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