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MS-GL business analytics track

The ASU MS-GL degree from the W. P. Carey School of Business offers a unique business analytics track to leverage our recognized leadership in both supply chain and analytics. In addition to preparing for a fast-growing and crucial field, you’ll learn alongside graduate students from around the world, gain insights from W. P. Carey business faculty, and work with a dedicated career services team who can help you take the next step in your career.

At a glance

16-month program

43 credit hours

Excellent career paths

Program cost

MS-GL (Business analytics)

Estimated tuition and fees

Resident

$51,912

Nonresident

$77,504

International

$80,532

Career paths and outcomes

$12.3 trillion

Global logistics market forecast by 2027

a $3 trillion increase in five years

Research and Markets, 2022

15%

job growth per year

Logistics analyst positions in the U.S. since 2004

Recruiter.com, 2022

Because the skills, tools, and knowledge used in analytics are universal, the MS-GL track in business analytics can open up opportunities around the world. Sample job titles in the field include:

  • Logistics analyst
  • Data analyst
  • Shipping analyst
  • Reverse logistics analyst
  • Logistics manager
  • Product lifecycle manager
  • Distribution and logistics program manager
  • Director of logistics technology

MS-GL business analytics courses

Courses in the MS-GL business analytics track build on and align your knowledge throughout the program, and give you the option of several electives to choose your own path.

Ensuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes.

Provides a foundation in programming fundamentals, the skills to combine and manipulate structured and unstructured data, and the ability to summarize, visualize, and draw insights from the data. This course may be waived with demonstrated Python proficiency.

Provides a solid foundation and deeper understanding of the use of quantitative modeling tools and techniques to solve problems faced in modern supply chains. Uses Excel workbooks to implement the appropriate quantitative methods, including forecasting demand, capacity planning of a manufacturing line and the line cycle time as it pertains to parts inventory management.

Addresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. Introduces modern quality management approaches including six sigma and design for six sigma. Covers the define, measure, analyze, improve and control (DMAIC) improvement cycle: the core process used to drive six sigma projects. DMAIC refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. Provides an analytics roadmap to help users work through the DMAIC problem-solving process.

Charting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from primarily numeric data.

Explains the skills and knowledge necessary for mastery of the use of quantitative modeling tools and techniques to support a variety of business decisions. Also explores deterministic optimization techniques, including linear programming, nonlinear programming, integer programming; network models and a brief introduction to metaheuristics. Covers the use of these models for a variety of common business problems. Practical application of these models uses Excel and standalone software. Also studies how to ensure that these solutions work in a wide variety of situations (what-if analysis).

Addresses the skills and knowledge necessary to model situations where uncertainty is an important factor. Covers models including decision trees, queuing theory, Monte Carlo simulation, discrete event simulation and stochastic optimization. Uses these models for a variety of common business problems and requires implementation of these models using Excel and standalone software. Studies how to ensure that these solutions work in a wide variety of situations (what-if analysis). Describes each of these methods in detail.

Involves formulating critical marketing problems, developing relevant testable hypotheses, collecting and analyzing data and, most importantly, drawing inferences and suggesting actionable implications.

Prepares students to navigate the challenging MBA world of work. Applies theories and best practices of career management and job search to help students make informed career choices, to obtain an internship between year one and year two of the program, and to obtain a full-time position upon graduation.

Deep learning applications have become an integral part of our lives over the last decade. Alexa, Amazon Go, Waymo, Apple Face ID, and Facebook's face recognition applications are all powered by deep learning networks. Applications based on deep learning models cover a wide spectrum of industries including retail, automotive, manufacturing, health care, banking, insurance, agriculture, security and surveillance. Hands-on look at the latest models, trends and challenges of deep learning applications in business.

In addition, your end-to-end project will offer challenges that may include messy data sources and undefined business value, which will develop and advance your communication skills and leadership abilities. This team-based project is intended to push the envelope of your skills in defining, addressing, and solving customer service and cost problems in all parts of the logistics supply chain.

Uses tools and techniques to analyze unstructured data that are applied to business problems to support informed decision making and the extraction of predictive analytics and patterns from primarily nonnumeric data.

Studies management of the conversion of raw materials to finished goods including scheduling, work-in-process inventory management, and postponement/customization. Students gain a deeper understanding of the integrated supply chain of plan, source, make, deliver and return.

Preparation of a supervised applied project that is a graduation requirement in some professional majors.

Logistics management track

The business analytics track offers an internship opportunity and additional coursework, while the MS-GL logistics management track delivers an intensive, leading-edge curriculum — without adding an extra semester to your degree.

Explore the logistics management track