2018-2019 Graduate Bulletin

Business Information and Analytics

Office: Daniels College of Business, Room 580
Mail Code: Daniels College of Business, Room 580, 2101 S. University Blvd., Denver, CO 80208
Phone: 303-871-3695
Web Site: https://daniels.du.edu/business-information-analytics/

Master of Science in Business Analytics

The University of Denver’s Daniels College of Business Master of Science in Business Analytics program balances the three pillars of business intelligence: data management, analytics, and business decisions. Graduates will be able to inform through evidence-driven decision making. During the program, students work with companies on actual problems by leveraging data to produce real outcomes for real implementation. Through partnerships with IBM/SPSS, Tableau, Microsoft and other leading technology vendors, Daniels is able to provide the most relevant tools in analytics in our classrooms. This gives students an edge in solving complex statistical problems and keeps them ahead of the curve. This is a STEM designated degree and is a 12–36-month, full or part-time, 58-credit program with two components: Business Analytics Core (54 credits) and Electives (4 credits). 

Daniels has been continuously accredited by the Association to Advance Collegiate Schools of Business International (AACSB) since 1923.

Master of Science in Business Analytics

Application Deadlines

  • Fall 2018 Priority 1 Deadline: November 1, 2017
  • Fall 2018 Priority 2 Deadline: January 15, 2018
  • Fall 2018 Priority 3 Deadline: March 15, 2018
  • Fall 2018 Priority 4 Deadline: May 1, 2018
  • Fall 2018 Final Submission Deadline: August 1, 2018
  • Spring 2019 Priority 1 Deadline: October 1, 2018
  • Spring 2019 Priority 2 Deadline: December 1, 2018
  • Spring 2019 Priority 3 Deadline: January 15, 2019
  • Spring 2019 Final Submission Deadline: February 15, 2019

Admission Requirements

  • Online admission application
  • $100 Application Fee
  • University Minimum Degree and GPA Requirements
  • Transcripts: One official transcript from each post-secondary institution.
  • GRE: The GMAT or GRE is required. Scores must be received directly from the appropriate testing agency by the deadline. The GMAT code number for the Business Analytics program is MZR-GT-47. The GRE code number is 4842. The admissions committee will consider GMAT or GRE waiver requests from candidates who meet one of the following standards (on a case-by-case basis):
    • Received an accredited master's degree in a related field.
    • More than 84 months of related professional experience.
    • DU students that meet the provisions for the Masters Accelerated Admissions Process.
  • Essay: Two required and one optional essay. Essays are assessed for clarity, organization, conciseness and grammar. The essays should communicate what the candidate hopes to achieve at Daniels and in the future and how he/she will contribute to the Daniels community.
    • Required: Briefly discuss your career objectives post-graduate school. How will your professional experience, combined with our degree program, help you to achieve these goals? What are your alternative career paths? (350 words)
    • Required: Respond to one of the following prompts (250 words):
      • My favorite memory is...
      • I’m most afraid of…
      • My greatest challenge has been…
      • I’m most proud of…
    • Optional: Is there anything else that we should know as we evaluate your application? If you feel your credentials and essays represent you fairly, please don’t feel obligated to submit another essay. (250 words).
  • Résumé: Submit a professional résumé that demonstrates the scope of professional experience and educational achievements. Utilize our résumé guidelines for assistance.
  • Other Requirements: Applicants may be contacted by a Daniels representative to schedule the admissions interview, which will be conducted on campus or via webcam.

Additional Standards for Non-Native English Speakers

Official scores from the Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) or Cambridge English: Advanced (CAE) are required of all graduate applicants, regardless of citizenship status, whose native language is not English or who have been educated in countries where English is not the native language. The minimum TOEFL/IELTS/CAE test score requirements for the degree program are:

  • Minimum TOEFL Score (paper-based test): 575
  • Minimum TOEFL Score (internet-based test): 94 (No less than a 20 on any section)
  • Minimum IELTS Score: 7.0 (No less than a 6.0 on any section) 
  • Minimum CAE Score: 185 (No less than a 170 on any section)
  • English Conditional Admission Offered: No, this program does not offer English Conditional Admission.
  •  

Read the English Language Proficiency policy for more details.

Read the English Conditional Admission (ECA) policy for more details.

Read the Required Tests for GTA Eligibility policy for more details.

Additional Standards for International Applicants

Per Student & Exchange Visitor Program (SEVP) regulation, international applicants must meet all standards for admission before an I-20 or DS-2019 is issued, [per U.S. Federal Register: 8 CFR § 214.3(k)] or is academically eligible for admission and is admitted [per 22 C.F.R. §62]. Read the Additional Standards For International Applicants policy for more details.

Financial Aid

There are many different options available to finance your education. Most University of Denver graduate students are granted some type of financial support. Our Office of Financial Aid is committed to helping you explore your options.

Master of Science in Business Analytics

Degree Requirements

Core coursework requirements
INFO 4000Foundations of Business 14
INFO 4100Survey of Business Analytics4
INFO 4120Python for Business Analytics4
INFO 4140Business Databases4
STAT 4610Business Statistics4
INFO 4200Business Analytics Capstone Planning2
INFO 4240Data Warehousing4
INFO 4281Project Management2
INFO 4300Predictive Analytics4
INFO 4340Data Mining and Visualization4
INFO 4360Complex Data Analytics4
INFO 4381Decision Processes2
INFO 4390Advanced Predictive Modeling with R4
INFO 4400Business Analytics Capstone4
INFO 4590Optimization4
Elective requirements
4 credits in electives required in 4000 level graduate courses. 24
Total Credits58

Minimum Number of Credits Required: 58

Courses

INFO 4000 Foundations of Business (4 Credits)

The Introduction to Business course is an introduction to provides an overview of the business arena, how a business operates, and the supporting functions that are needed in any business enterprise. Students will identify forms of ownership and the processes used in operations, marketing, accounting, finance, personnel, information technology and general management. Moreover, students will learn about social responsibility and business ethics in concurrence with the Daniels College legacy.

INFO 4100 Survey of Business Analytics (4 Credits)

This course provides an overview of business analytics: how business data are collected, processed, and analyzed to support decision making. It will address both how to assess and use data that is readily available as well as how to start with corporate strategy and determine what data is needed, how to generate and process it. The course will also explore how corporate culture, ethics, and globalization can affect data management and analytic decision-making.

INFO 4120 Python Programming (4 Credits)

Python is a popular general purpose programming language which is well suited to a wide range of problems. With the right set of add-ons, it is comparable to domain-specific languages such as R and MATLAB. Python is a scripting language. The following topics will be covered: Importing data, Reading and writing files, Cleaning and Managing Data, Merging and joining DataFrame objects, Plotting and Visualization, Statistical Analysis, Fitting data to probability distributions and Linear models. Packages: Pandas, NumPy, matplotlib, statsmodels, Scikit-learn, and IPython. Principal Content Elements: 1. Introduction to Programming Logic and Design Using Python 2. Data Management 3. Statistical Analysis 4. Advanced Data Management and Statistical Analysis Prerequisites: STAT 4610.

INFO 4140 Business Databases (4 Credits)

This is an introductory database course which covers enterprise database design, modeling and implementation.

INFO 4200 Business Analytics Capstone Planning (2 Credits)

This course prepares the student for the Capstone course by identifying a faculty advisor, company, data, and a business issue to be addressed in the Capstone course in the final quarter. (Must be taken two quarters prior to INFO4400, with the exception of off-cycle students, who will take it the quarter prior to INFO4400.) This course may be taken by MSBA students only.

INFO 4240 Data Warehousing (4 Credits)

This course introduces students to the main components of a data warehouse for business intelligence applications. Students will learn how a data warehouse fits into the overall strategy of a complex enterprise, how to develop data models useful for business intelligence, and how to combine data from disparate sources into a single database that comprises the core of a data warehouse. Students will also explore how to define and specify useful management reports from warehouse data. Prerequisites: INFO 4100, INFO 4140.

INFO 4250 Business Data and Analytics (4 Credits)

Businesses make decisions and improve processes using their own and external data with a variety of data-driven and analytic techniques. This course introduces students to the business data landscape, data management in commercial organizations, and the data-driven decision-making process. Students explore the fundamental concepts behind how data and analytics can improve business performance, using their individual roles and companies as subject matter. Principal Content Elements: 1. Data-driven decision making and performance improvement. 2. Data management in organizations. 3. Organizational transformation based on data-driven insights.

INFO 4280 Project Management (4 Credits)

In this course students examine the science, practice the art, and discuss the folklore or project management to enable them to contribute to and manage projects as well as to judge when to apply this discipline. The course also covers the use of MS Project Professional as a management tool and Crystal Ball as a Monte Carlo simulator for project exercises. Students also learn the fundamentals of process and project simulation for business decision-making. Prerequisite: INFO 4100.

INFO 4281 Project Management (2 Credits)

“Cheaper, better, faster” is the mantra of modern business. Innovation, providing new products and services or using improved business processes, has become a prerequisite for businesses to thrive and flourish. Project Management is a discipline which supports innovation by examining how to facilitate one time events such a constructing a building, installing a software system, taking a product to market, reengineering a marketing process, or merging an acquired company. In this course, we examine the science, practice the art, and discuss the folklore of project management to enable students to contribute to and manage projects as well as to judge when to apply this discipline.

INFO 4300 Predictive Analytics (4 Credits)

This course is designed to prepare students for managerial data analysis and data mining, predictive modeling, model assessment and implementation using large data sets. The course addresses the how, when, why and where of data mining. The emphasis is on understanding the application of a wide range of modern techniques to specific decision-making situations, rather than on mastering the theoretical underpinnings of the techniques. The course covers methods that are aimed at prediction, forecasting, classification, clustering and association. Students gain hands-on experience in using computer software to mine business data sets. Prerequisite: STAT 4610.

INFO 4340 Data Mining and Visualization (4 Credits)

In this course, students create business intelligence tools such as balanced scorecards, data visualization and dashboards to inform business decisions. The course will focus on the identification of metrics, measures, and key performance indicators for a variety of business operations, and will introduce numerous analytic methodologies to support the decisions made with regard to these metrics. The focus will be on the advantages and disadvantages of various modeling methodologies and implementations moving towards performance improvement and business understanding. Prerequisite: STAT 4610.

INFO 4360 Complex Data Analytics (4 Credits)

This course addresses the rapidly-growing demands on businesses created by the prevalence of big and unstructured data. These include management of big data, big-data analytics, analysis of unstructured data (to include text mining), and management and analysis of real-time (streaming) data. The focus will be on enhancing business decision-making in the presence of big data, and on how to create the greatest ROI with large data sets.

INFO 4380 Decision Processes (4 Credits)

This course addresses the process of decision making in the enterprise: who makes what decisions based on what information and for what purpose. Business Intelligence is premised on the HP motto: "in God we trust. All others bring data." But what is the cost of collecting and analyzing the data and presenting the results, and what decisions justify that cost? Is the transformation from data to decision always rational, and what are the common pitfalls for human decision makers? We examine the results of recent experiments from behavior economics and their relevance to making business decisions. Prerequisite: INFO 4100.

INFO 4381 Decision Processes (2 Credits)

The competency we want to begin to develop in this course is the ability to make sound business decisions. A quick Google search can reassure you that there is no lack of information about how to make good decisions. And much of that information is confusing, if not downright contradictory. Since you will be making the decisions which impact your business and your career, you will need to decide what constitutes a good decision as well as a good decision process. In this course, we will explore some of the voluminous material available, use it to make decisions, practice with useful tools, identify traps and pitfalls, assess results, and extract guidelines for a decision process. Then we will iterate to update and refine the process.

INFO 4390 Advanced Predictive Modeling with R (4 Credits)

This course serves as an introduction to advanced predictive modeling and statistical learning using the R statistical software. Specific topics include linear, non-linear, and logistic regression, classification, resampling methods, and non-linear regression, tree-based methods, and support vector machines. The students will learn how to communicate their results (business reports, dashboards, etc.) of the varioius modeling exercises and projects using RStudio and the RMarkdown suite of tools. Enforced Prerequisites and Restrictions: INFO 4300.

INFO 4400 Business Analytics Capstone (4 Credits)

This course gives students an opportunity to apply the knowledge and skills learned in this program to a real-world problem submitted by a partner business. Students take a business problem from model construction and data collection through an analysis and presentation of results to recommendations for specific business decisions. Prerequisite: INFO 4200.

INFO 4401 Quantitative Methods (2 Credits)

Businesses can never have perfect information; therefore, they must employ statistical techniques to improve the decision-making process. This course introduces students to managerial decision-making using probability and other statistical techniques to support and validate the chosen decision. A student project will focus on data collection (primary research), data analysis, decision analysis, written/oral presentation skills, and the development of an infographic.

INFO 4590 Optimization (4 Credits)

This course introduces students to the basic optimization modeling techniques and tools as practiced by business analysts to help their enterprises make better-informed decisions. Applications will include mix, selection, assignment, distribution, transportation, financial management, planning, scheduling, and management implementations in a variety of business settings. The course will focus on problem definitions, problem configuration, spreadsheet solutions, LP Software (LINGO) solutions, and interpreting and implementing results.

INFO 4591 Optimization (2 Credits)

This is a two-credit version of INFO4590, intended for dual-undergraduate/graduate students only. Students have the option of taking the first ten lessons (spreadsheet modeling) or the second ten lessons (solver programming) and completing the deliverables associated with their track only. The students taking the spreadsheet track will focus on LOs 1, 2, and 3. The students taking the solver track will focus on LOs 1, 2, 4, and 5. All students will take the common INFO4590 final. The course is only offered in conjunction with INFO4590 during the Winter quarter.

INFO 4700 Topics in Business Analytics (0-10 Credits)

Exploration of current trends and topics in business analytics. Prerequisite: INFO 4100.

INFO 4991 Independent Study (1-10 Credits)

INFO 4992 Directed Study (1-4 Credits)

Faculty

Andrew Urbaczewski, Associate Professor and Department Chair, PhD, Indiana University

Paige Baltzan, Teaching Assistant Professor, MBA, University of Denver

Valerie Bartelt, Assistant Professor, PhD, Indiana University

Gisella Bassani, Teaching Assistant Professor, MBA, University of Denver

Paul Bauer, Clinical Professor, Emeritus, PhD, University of Kansas

Philip Beaver, Professor of the Practice of BIA, PhD, Naval Postgraduate School

Tianjie Deng, Assistant Professor, PhD, Georgia State University

Ryan Elmore, Assistant Professor, PhD, Pennsylvania State University

Ronald Farina, Associate Professor, Emeritus, PhD, University of Colorado

Stephen Haag, Professor of the Practice of BIA, PhD, Univ of Texas at Arlington

Tamara Hannaway, Teaching Assistant Professor, PhD, University of Colorado-Denver

Anthony Hayter, Professor, PhD, Cornell University

Kellie Keeling, Associate Professor, PhD, University of North Texas

John Kuark, Professor, Emeritus, PhD, University of Minnesota

Young Jin Lee, Associate Professor, PhD, University of Washington

Don McCubbrey, Clinical Professor, Emeritus, PhD, University of Maribor

Zlatana Nenova, Assistant Professor, PhD, University of Pittsburgh

Thomas Obremski, Associate Professor, Emeritus, PhD, Michigan State University

David Paul, Associate Professor, PhD, University of Texas at Austin

Amy Phillips, Teaching Professor, Med, Plymouth State College

Richard Scudder, Associate Professor, Emeritus, PhD, University of Colorado-Boulder

Scott Toney, Teaching Assistant Professor, MS, University of Texas at Dallas

Nathan "Dave" Yates, Assistant Professor, PhD, University of Southern California

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