Data-driven business major

This major is made up of the three common core courses and three Data-Driven Business major courses, then culminates in an interdisciplinary practicum that will connect students to the real-world. All the core and major courses will be three (3) credits each and will be offered by the College of Engineering and the Mike Ilitch School of Business. The program will culminate with a six (6) credit Interdisciplinary practicum which will pull together a variety of subject material covered in the core courses and give it a real-world application. Elective courses (6 credits) can come from other majors (assuming the student has the right pre-requisite knowledge) and outside.

Module 1: Core courses (9 credits)

Course DSB 6000: Data Science Strategy & Leadership (3 credits) - Provides an understanding of how organizations can leverage data science and analytics to gain competitive advantage and how to use the data to align with a company's mission and goals. Students will learn how organizations derive business value/impact, and return on investment, and the importance of interpreting and communicating the business case.

Learning objectives:

Understand how organizations can use data to align with their mission and goals
Understand the role of data science in organizational strategy and how organizations can leverage information to gain competitive advantage.
Understand the challenges of data-driven businesses and how organizations can start to use their data to deliver actionable business insight.
Gain an introductory knowledge of data science tools and new technologies tools that are useful in extracting intelligence and value from data needed to solve next generation data challenges
Identify the application of data science tools to reveal business opportunities and threats.
Identify the challenges posed by the ability to scale and the constraints of today's computing platforms and algorithms

Course DSA 6000: Data Science & Analytics (3 credits) - Basic data science and analytics concepts covered through case studies, success stories, and a semester project that cuts across all course modules.

Learning objectives:

Discuss the elements of a data science and analytics project life-cycle, starting with business need to solution deployment and sustainment.
Apply core data science and analytics techniques, tools and technologies
Apply statistical and machine learning tools and techniques to tackle various aspects of big data analytics projects
Make analytics actionable for business effectiveness and effectively engage business users and communicate findings.

Course DSE 6000: Computing Platforms for Data Science (3 credits) - Covers an overview of various computing platforms for developing, deploying, configuring a wide range of data science applications for different domains. The programming models, characteristics of supported workload, and management of performance, cost and scalability will be compared side by side.

Learning objectives:

Develop the skills necessary for creating and deploying efficient data science and analytics applications.
Analyze major distributed and parallel computing frameworks, such as MapReduce, Spark, and traditional high performance computing (HPC) systems
Compare programming models for batch, interactive, and streaming applications
Use and manage performance, cost, and scalability of hosted data platforms and the cloud based solutions
Apply criteria for choosing and configuring data science computing systems for specific applications.

Module 2: Major courses (9 credits)

Course DSB 6100: Marketing Analytics (3 credits) - Application and synthesis of marketing methods and modeling approaches to design, analyze, and optimize digital marketing campaigns and to understand customer segments, customer life cycles, and lifetime values.

Learning objectives:

Explain and illustrate core components and key concepts associated with marketing methods/analytics from a variety of perspectives.
Evaluate tools and strategies for successfully integrating traditional, multi-channel, and digital marketing data and marketing campaigns into business practices.
Understand core metrics, integration systems, data providers and benchmarks associated with effective marketing analytics.
Understand customer segments, customer life cycles, and lifetime values
Interpret and understand how to integrate core search engine optimization/marketing analytics into effective decision making procedures.
Understanding modeling approaches for executing effective marketing strategy
Identify appropriate database technologies to meet a set of requirements and to recommend possible solutions.
Create predictive models using statistical, data mining and machine learning techniques, and evaluate and interpret such models to support fact-based decision making.
Work both independently and in a team to solve large data analysis projects.
Clearly communicate and present complex analytics results to business clients, using practical and simple business terms that can be understood by a general non-technical audience.
Identify and evaluate appropriate data analytics techniques to be used depending on the specific information needs of the project
Use data visualization tools to communicate data mining results in an effective way.

Course DSB 6200: Manufacturing & Supply Chain Analytics (3 credits) - Discussion of the strategic and tactical issues surrounding the design and operation of supply chains through effective information collection, sharing, and collaboration, an understanding of applied analytical tools and methods that can be used to make better supply chain decisions and practical application of supply chain advanced planning and optimization solutions.

Learning objectives:

Identify the strategic and tactical issues surrounding the design and operation of supply chains through effective information collection, sharing, and collaboration
Understand the analytical tools and methods that can be used to make better supply chain decisions (Strategic, Tactical, and Operational)
Understand advanced planning and optimization.
Gain working knowledge of supply chain advanced planning and optimization solutions from today's leading vendors

Choose one course from the list below:

Course CSC 5800: Intelligent Systems: Algorithms and Tools (3 credits) - Introduction to basic algorithms and software tools for intelligent data representation and analysis, including: data pre-processing, data exploration and visualization, model evaluation, predictive modeling, classification methods, association analysis, clustering, anomaly detection, representing extracted patterns as expertise, tools for data mining and intelligent systems such as WEKA, CLIPS, and MATLAB.

Course CSC 5825: Introduction to Machine Learning and Applications (3 credits) - Through algorithmic investigation, brainstorming, and case analysis, students develop the skills and strategies that are necessary for effective learning from data, including Big Data emerging from science and engineering. Prerequisite: CSC 3110 or Graduate Standing.

Course ISE 7860: Intelligent Analytics (Neural Networks & Deep Learning) (3 credits) - Computational intelligence and machine learning methods (primarily neural networks and deep learning) used to solve complex analytics problems and develop decision support systems. Project-centric approach with the goal of developing several analytics solutions for real-world problems.

Module 3: Electives (6 credits)

Elective courses can come from other tracks of the Data Science & Business Analytics program or from outside the program. See elective courses page for approved course list.

Module 4: Applied analytics practicum (6 credits)

Course DSB 7500: Practicum (6 credits) - Application of theoretical knowledge acquired during the Data Science and Business Analytics program to a project involving actual business problems/opportunities and data in a realistic setting. Engages the entire process of solving a real-world data science and business analytics project including: setting the project scope, collecting and processing data, applying analytic methods and presenting the developed solution platform. Both the problem statements for the project assignments and the datasets originate from real-world domains.

Learning objectives:

Manage business opportunities from problem/opportunity recognition through delivery and deployment of effective solutions
Scope the project, collect and process real-world data, design best methods to solve the problem, implement a solution, and quantify the robustness and accuracy of proposed models
Present proposals on how to approach the problem and findings from solutions
Work in project teams to develop successful solutions
Write project reports for evaluation and gaining support