Why ENCE 603

Project managers and professionals in engineering and software development need to perform data analytics on a daily basis. This course is an introduction to identifying, analyzing, assessing, and managing data inherent to engineering projects.

It includes: Bayesian statistical principles, concepts, and models; decision science aspects of data systems; measurement theory; subjective aspects of statistics and data appraisal. Examples are drawn from software development, systems integration, and large infrastructure engineering projects. Covers statistical basics, Bayesian data analysis, simulation, predictive analytics.

Learning Objectives:
• Identify salient aspects of data development systems in the project environment
• Interpret project performance data using descriptive analytics
• Analyze project data using Bayesian analytics and predictive analysis
• Relate decision engineering to prescriptive analysis in project management
• Organize data analytics programs for unique projects, programs, and portfolios

Gregory Baecher, PhD Credits 3 Offered^* Fall On-Campus, Spring Online

Topics

Data development systems

Identify salient aspects of data development systems in the project environment

Descriptive analytics

Interpret project performance data using descriptive analytics

Bayesian analytics and predictive analysis

Analyze project data using Bayesian analytics and predictive analysis

Decision engineering

Relate decision engineering to prescriptive analysis in project management, bidding, and other operational tasks

Projects, programs, and portfolios

Organize project data analytics programs for unique projects, programs, and portfolios

Student Feedback

“The topics discussed throughout the course where mostly new to me, so overall it was an interesting course.” Fall 2020
“I really liked the instructors' passion, extensive knowledge, and experience.” Fall 2020
“I really enjoyed the breadth of knowledge brought to this course, particularly the background and history of statistics. More importantly, I got some of the theory and reasoning behind the processes and statistical tests. I think previous classes focused on the implementation rather than the "why," and so it was nice to understand why we are doing certain things.” Fall 2020
“Very good and clear explanations made by the instructor.” Fall 2020
“The virtual library and all the readings that the professor provided were extremely useful.” Fall 2020
“Excellent reading materials and resources.” Fall 2020
“Very informative class.” Fall 2020

Books^*

Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) 3rd Edition By Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin

Introducing Monte Carlo Methods with R (Use R!) 2010th Edition By Christian P. Robert and George Casella

The Signal and the Noise: Why So Many Predictions Fail–But Some Don’t 1st Edition By Nate Silver

Schedule

Offered: Fall On-Campus, Spring Online^*

Week 1Data and statistics fundamentals I

Measurement theory
Probability sampling

Week 2Data and statistics fundamentals II

Data visualization
Data mining

Week 3Project performance data and descriptive analytics I

Key performance indicators
Earned value management
Expert opinion elicitation
Expert panels and voting

Week 4Project performance data and descriptive analytics II

Measuring intangibles
Government data and analysis

Week 5Bayesian methods and predictive analytics I

Bayesian theory
Bayes factors and the weight of evidence

Week 6Bayesian methods and predictive analytics II

Markov Chain Monte Carlo
Predictive modeling
Textural analysis and natural language processing

Week 7Prescriptive analytics I

Decision engineering
Bidding theory

Week 8Prescriptive analytics II

Machine learning

^*All course content, including schedule, topics, and books are subject to change semester-to-semester. Course costs are dependent on students' individual situations, including but not limited to, online vs on-campus enrollment, in-state vs out-of-state enrollment, and scholarships. Not all books on this page may be required readings and additional readings may be assigned. Please check the UMD Schedule of Classes for most up to date semester offerings. Instructors give students specific semester details once they are enrolled.

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Why ENCE 603

Topics

Student Feedback

Books*

Schedule

Books^*