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M.S. in Business Analytics Online

Cameron School of Business
M.S. in Business Analytics - University of North Carolina Wilmington Online

Earning your Master of Science in Business Analytics online from the University of North Carolina Wilmington gives you the skills modern organizations need to remain competitive in a rapidly changing marketplace.

Overview

UNCW's online Master of Science Business Analytics program prepares you for in-demand career opportunities by teaching you the advanced analysis skills needed to inform advanced business strategies, recommendations and decisions. Taught by experienced faculty who are committed to your success, this rigorous program gives you a world-class education with an emphasis on real-world application through case studies, simulations and data-intensive projects.

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$15,211


N.C. Resident
Tuition & Fees

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as few as 12 months


Program Duration

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30


Program
Credit Hours

The U.S. Bureau of Labor Statistics projects a 39% increase in business analytics jobs through 2024. Whether you're seeking professional advancement or to transition to a new career, this program will give you the skills you need to get ahead. Core coursework surveys descriptive, prescriptive and predictive analytics and gives you a solid foundation in programming and application development. You will also learn how to generate statistical reports and create informative data graphics that "tell a story" appropriately tailored to your target audience. Choose from a variety of electives—including an optional internship—to hone your focus on specific industries such as marketing, healthcare, supply chain, transportation and cyber security.

This highly affordable, AACSB-accredited program is delivered in a convenient 100% online format designed for working professionals and can be completed in as few as 12 months. Plus, we don't require a GMAT or GRE, so you can get started right away.

In this online program, you will:

  • Learn tools and techniques to manage analytics on projects in a variety of business environments
  • Understand and apply methods of collecting and transforming data into appropriate formats for analysis
  • Learn prescriptive analytics modeling techniques for optimization, simulation and decision making
  • Study model selection, parameter estimation and validation for predictive analytics using algorithms from statistical and machine learning disciplines
  • Gain a foundation in programming, including application development and integrating applications into business operations
  • Use common statistical forecasting methods—including decision trees, neural networks and stepwise regression—to analyze data, generate statistical reports and create informative data graphics
UNCW Cameron School of Business
AACSB Accredited

UNCW's Cameron School of Business is accredited by The Association to Advance Collegiate Schools of Business (AACSB International)

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Begin your application today, or call 855-306-4734 with questions.

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Tuition

Tuition for the online M.S. Business Analytics degree program is highly affordable. Financial aid is available for those who qualify.

  Per Credit Hour Fees Per Credit Hour Per Course Fees Per Course Total Tuition & Fees
North Carolina Residents: $481.32 $25.70 $1,443.96 $77.10 $15,210.60
Non-North Carolina Residents: $1,159.22 $25.70 $3,477.66 $77.10 $35,547.60
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Calendar

The M.S. Business Analytics is delivered 100% online, featuring accelerated 7-week courses and two start dates a year. Choose the start date that is best for you.

Session Program Start Date Application Deadline Document Deadline Registration Deadline Payment Deadline Last Class Day
Fall 1 8/27/18 6/15/18 8/4/18 8/29/18 8/30/18 10/14/18
Fall 2* 10/22/18 9/22/18 9/29/18 10/24/18 10/25/18 12/9/18
Spring 1** 1/21/19 12/15/18 12/22/18 1/23/19 1/24/19 3/10/19

*Fall 2: Only students who need to complete program prerequisites (Statistics or BUS 500) in order to begin the program in the Spring 1 term should apply for the Fall 2 Session. Please note: Application fees are not-refundable and not transferable to future sessions. Students who have already met the prerequisites will not be admitted for Fall 2 and should only apply to Spring 1.

**Spring 1: admission is only available to applicants that have met the BUS 500 and Statistics pre-requisite course requirements.

Ready to Get Started?

Begin your application today, or call 855-306-4734 with questions.

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Admissions

To apply for the online M.S. Business Analytics program, you must hold a bachelor's degree and submit transcripts verifying a minimum cumulative GPA of 3.0.

M.S. Business Analytics Online Admission Requirements

  • An earned bachelor's degree from an accredited college or university
  • Completion of one course in statistics with a grade of at least a B
  • Minimum 3.0 GPA on undergraduate coursework
  • Three letters of recommendation
  • Resume with cover letter (serves as statement of interest)

Official transcripts from all colleges/universities are required. Electronic transcripts are preferred and can be emailed to gradtranscripts@uncw.edu. Alternatively, they can be mailed directly from the institution(s) to UNCW at this address:

UNCW Graduate School
601 S. College Road
Wilmington, NC 28403-5955

Have a question? Call us at 855-306-4734.

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Courses

The M.S. Business Analytics online curriculum is comprised of 10 courses (30 credit hours), which includes eight core courses (24 credit hours) and two electives (6 credit hours). Students with an undergraduate degree other than business must take an additional course - BUS 500 Survey of Business.

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Students in the M.S. Business Analytics online program must take the following courses.

Duration: 7 weeks   |   Credit Hours: 3

This course covers statistical inference as applied to management decision making and focuses on building linear statistical models and developing skills for implementing statistical analysis in real situations. Applications require the use of statistical analysis programs on the computer.

Duration: 7 weeks   |   Credit Hours: 3

This course introduces students to the field of prescriptive analytics. Students will learn how to develop and use modeling techniques used extensively in the business world. Both mathematical and spreadsheet skills in MS Excel are utilized for performing optimization, simulation, and decision analysis techniques.

Duration: 7 weeks   |   Credit Hours: 3

This course explores computer-intensive methods for model selection, parameter estimation, and validation for predictive analytics. The course focuses on techniques and algorithms from the statistical and machine learning disciplines and has a strong programming component. Example topics in this course include ordinary least squares regression, logistic regression, multinomial logistic regression, classification and regression trees, neural networks, support vector machines, naïve Bayes, principal components analysis, cluster analysis, and regularization. Each technique is accompanied with a focus on application and problem-solving.

Duration: 7 weeks   |   Credit Hours: 3

This course consists of case studies on using statistical methods for prediction in some business settings. The statistical methods used in the cases follow the recent advances in forecasting with big data and include methods such as decision trees and other classification models, neural networks and stepwise regressions. The JMP Pro software, which is available at UNCW, will be utilized in all cases. The course will also include a data intensive project, which will ask students to use the approaches introduced in the cases to forecast the US economy or the stock market.

Duration: 7 weeks   |   Credit Hours: 3

This course requires students to apply theoretical and practical knowledge acquired during the Business Analytics program at a comprehensive capstone project. Students will develop a solution to a real-world problem that will include collecting and cleaning data, building an appropriate model, and using appropriate analytic methods to solve the problem. The course instructor will provide a problem statement and data from either a real-world domain or a close approximation. Students will work individually or in small teams to develop a project plan, appropriate models, and a final recommendation. It may be possible for students to propose their problem statement and data collection, however, all such projects must be instructor approved.

Duration: 7 weeks   |   Credit Hours: 3

This course introduces the essential general programming concepts and techniques to a data analytics audience with limited or no prior programming experience. Students will learn programming foundations, application development, and how to integrate applications with business operations in this class. The course covers hands-on issues in programming for analytics, which includes accessing data, manipulate data objects, analyze data using common statistical methods, generate reproducible statistical reports, and creating informative data graphics. The course introduces software techniques to write functions, debug, and organize and comment code.

Duration: 7 weeks   |   Credit Hours: 3

This course focuses on the conceptual foundations of relational databases and data management, interpreting database structure for relevant data, data queries and reporting, and searching for data anomalies [sometimes referred to as "data cleansing" of errors and inconsistencies]. Students will become familiar with database modeling and logical design. Proficiency in developing complex queries and report generation is stressed.

Duration: 7 weeks   |   Credit Hours: 3

This course provides experience in design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. Use of popular and powerful data visualization and dashboarding software tools is emphasized.

Students must also choose two of the following courses.

Duration: 7 weeks   |   Credit Hours: 3

This course addresses the unstructured data management skills needed for modern data analysis including those salient to big data and real-time data environments. The focus is on unstructured data and its environment. Unstructured data includes web data (blogs, text), user generated content, social media, location-aware data, and digital media among others. Topics covered include extraction methods for real time audio and video data, data capture, cleaning, representation, storage, queries, manipulation, and real-time data management. Also included as they apply to unstructured data environment are data security, governance, and visualization. Students will learn natural language processing and geospatial analytical tools.

Duration: 7 weeks   |   Credit Hours: 3

The focus of this course is to familiarize students with the principles and strategic concepts of marketing analytics, a high growth area that uses computer-based analytical techniques and quantitative modeling to enhance decision-making capabilities of marketing managers. The effective use of marketing analytics offers insights into customer preferences and trends and allows for the detection of patterns, the making of new associations, and the acquisition of a deeper understanding of customers.

Duration: 7 weeks   |   Credit Hours: 3

This course equips students with health analytics skills to select, prepare, analyze, interpret, evaluate, and present clinical and operational data to improve healthcare outcomes. Theoretical and practical coverage of topics is presented, such as data mining, predictive modeling, association analysis, clustering, and visualization.

Duration: 7 weeks   |   Credit Hours: 3

This course explores various statistical analysis techniques used in transportation systems. Various practical transportation topics will be covered, including model estimation, data analysis, traffic forecasting, incident prediction, traffic flow theory, and safety. Techniques to be covered include statistics, data mining, hypothesis testing, experimental design, and optimization, such as regression, time series modeling, classification, and clustering. Popular statistical modeling software will be used to assess transportation solutions.

Duration: 7 weeks   |   Credit Hours: 3

This course introduces the application of data analytics in various aspects of supply chain management, including forecasting and inventory management, sales and operations planning, transportation, logistics and distribution, purchasing, and supply chain risk management. Software packages include Excel with Solver and R.

Duration: 7 weeks   |   Credit Hours: 3

This course introduces students to a current area of application or a current development of an advanced topic in business analytics. Students will learn how Business Analytics solutions can impact a firm's ability to create and maintain competitive advantages. Topics may include applications or developments in descriptive, predictive, or prescriptive analytics.

Duration: 7 weeks   |   Credit Hours: 3

Prerequisite: Overall GPA of at least 3.0. Academic training and practical experience through work in a private company or public agency. Faculty supervision and evaluation of all study and on-site activity. Students much secure permission of the graduate coordinator.

Students with an undergraduate degree other than business must take the following course.

Duration: 7 weeks   |   Credit Hours: 3

Provides non-business students with a broad overview of the complex and dynamic contemporary world of business. Topics include Accounting, Finance, Management, Marketing and Operations. Required for Non-Business Majors.

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