Computational engineering track

This track is made up of the three common core courses and three computational engineering track courses, then culminates in an interdisciplinary practicum that will connect students to the real-world. All the core and track courses are three 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-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 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.

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 –how can organizations 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.

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.

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: Computational engineering track courses (9 credits)

DSE 6100: Data Modeling & Management (3 credits) – Covers both traditional data modeling and big data modeling from conceptual design logical-to-physical mapping, to physical schema optimization. Provenance management, which concerns the lineage and history of a data product, is important for the repeatability of data analysis. The course will present various concepts of provenance and its relationships to data quality and trust.

Learning objectives:

  • Use the fundamentals of data modeling methodologies including schema design and query optimization
  • Discuss user interfaces for data, user experience, and dashboards
  • Apply Provenance management for the repeatability and reproducibility of data analysis
  • Design and implement real life data science application

DSE 6200: Modern Databases (3 credits) – Covers an overview of databases, tools, and computing platforms. One focus is basic SQL, NoSQL, and NewSQL programming skills and a comparison of their cons and pros. In particular, the students will learn the criteria to choose a database system, either SQL or NoSQL, based on the requirements of an application domain.

Learning objectives:

  • Present databases, tools, and computing systems
  • Apply working knowledge of data management solutions in today's market place, including SQL, NoSQL and NewSQL
  • Use criteria for choosing and configuring databases for specific data science applications
  • Explain the various stages of data life cycle (from collection to archival including data cleaning and integration)

DSE 6300: Data Science Applications Development (3 credits) – Focuses on the software engineering cycle of developing a comprehensive data science application. Students will have the freedom to choose a computing platform or a NoSQL database as the underlying infrastructure for developing a data science application. Students will also choose a particular domain and problem in which one needs to address one of the big data challenges: volume, velocity, or variety.

Learning objectives:

  • Explain distributed programming paradigms
  • Develop distributed computation using SPARK/SCALA
  • Develop real-time/streaming applications
  • Develop data science integration (Flume, Kafka)

Module 3: Electives (6 credits needed) 

Elective courses can come from other tracks of the MSDSBA program or from outside the program. See Elective Courses page for a sample list.

Module 4: Practicum (6 credits)

DSE 7500: Practicum (6 credits) – Application of theoretical knowledge acquired during the 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

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