Advanced Analytics track
This track is made up of three core courses and three Advanced Analytics track courses, then culminates in an interdisciplinary practicum that will connect students to the real world. All core and track courses are three (3) credits each and will be offered by the College of Engineering and the Mike Ilitch School of Business. The program culminates with a six-credit interdisciplinary practicum that will pull together a variety of subject material covered in the core courses and give it a real-world application. Elective courses (six credits) can come from other tracks (assuming the student meets prerequisites) or from outside the program.
Module 1: Core courses (9 credits)
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.
- 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 –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.
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.
- 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.
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.
- 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: Advanced Analytics track courses (9 credits)
Choose three courses from the list below.
DSA 6100: Statistical Methods for Data Science & Analytics (3 credits) – Statistical methods and techniques required for data science and analytics applications covered through case studies, success stories, and a semester project that cuts across all course modules.
DSA 6200: Operations Research (3 credits) – Mathematical optimization models that come into play in data science and analytics applications covered through case studies and a semester project. Heuristic solution approaches will also be addressed, along with sensitivity analysis techniques.
DSA 6300: Decision Analysis & Simulation (3 credits) – Coherent approach to decision making, developing rules of thought to transform complex decisions into simpler decision situations covered through case studies, success stories, and a semester project that cuts across all course modules. Discusses the role of discrete-event simulation for improving decision support.
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.
CSC 7810: Data Mining: Algorithms and Applications (3 credits) - Application of various basic/advanced data mining techniques to real-world problems. Prerequisite: CSC 5800 with a minimum grade of C.
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 needed)
Elective courses can come from other tracks of the MSDSBA program or from outside the program. See the elective courses page for an approved course list.
Module 4: Practicum (6 credits)
DSA 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.
- Manage business opportunities from problem/opportunity recognition through delivery and deployment of practical 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.