ASU Sync classes for Fall 2020

Classes for on-campus students will be taught across three environments for the fall semester: in person, via Zoom, and 100% online.
See how ASU Sync will support your learning.

Business analytics area of emphasis

To enact rapid business transformation and create greater competitive advantage, organizations are transforming massive amounts of data into business insights. The business analytics area of emphasis equips you with the quantitative and analytical skills to succeed in this new data-driven economy.

Enhance your knowledge in applied analytics, developing effective problem-framing and problem-solving skills to strategically evaluate and apply descriptive, predictive, and prescriptive models and methods for business decision-making. The business analytics area of emphasis offers flexible, domain-specific business analytics electives in information management, supply chain management, and marketing.

Required courses

You must complete the following required courses plus at least one course from the list of electives in order to complete the area of emphasis in business analytics.

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

Charts 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 numeric data.

Focuses on mastering quantitative modeling and optimization techniques for contextual business decision-making. Applies linear, nonlinear, integer programming, and network models to a wide range of business scenarios, including marketing, investment strategy, financial planning, production, and transportation, and also serves as a foundation for stochastic optimization.

Addresses the skills and knowledge necessary to model situations where uncertainty is an important factor. Covers models including decision trees, simulation, and stochastic optimization, along with application for solving a wide variety of common business problems. Requires implementation of these models using Excel and stand-alone software.


Explores how to support informed decision-making and extract predictive analytics and patterns from nonnumeric data by leveraging tools and techniques to analyze unstructured data.

Provides a broad survey of information security and controls, utilizing the COBIT framework to illustrate how information security and controls contribute to effective IT governance. Develops an understanding of the issues associated with information security and effective IT governance, assesses effectiveness of information security alternatives, and designs an organizational information security program.

Investigates major categories of enterprise systems, factors driving software adoption, and keys for successful implementation. Special attention is paid to evaluating the potential impact of emerging technologies on business environments.

Focuses on key aspects of commoditization of hardware, software, and business processes. Introduces the IT product development and service delivery processes with sound management principles for on-budget and on-time projects that meet end-user needs.

Explores decision models and frameworks applied to assess, evaluate, and implement new technologies. Provides context for applying the decision models and frameworks, including artificial intelligence, Big Data, 3D printing, Internet of Things (IoT), mobile platforms and devices, semantic web, collaboration technologies, and other emerging technologies.

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.

Examines operations planning at an aggregate level, yield management, and service capacity management using waiting line - models, discrete event simulation, and statistical quality control.

Emphasizes the fundamental needs of scheduling, team dynamics, risk analysis, and control within projects found in any business context or discipline.

Focuses on developing analytical methods and applying statistical and mathematical tools to predict consumer behavior. Introduces formal models to analyze how and when customers make product purchase decisions, configure new products, develop market segments, forecast market share, and determine optimal pricing strategies.

Career paths

  • Business analyst
  • Consultant
  • General management
  • Decision analyst
  • Leadership development and rotational programs
  • Marketing and analytics consultant
  • Operations analyst

Notable employers

  • Inc.
  • JPMorgan Chase and Co.
  • Intel Corp.
  • General Motors Co.
  • A.T. Kearney