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ence 603 Data Analytics for the Project Manager Use data analytics to identify, analyze, assess, and manage engineering project data.
 
Introduction Topics Books Schedule

Why ENCE 603

Project managers and professionals in engineering and software development need to perform data analytics on a daily basis. This course introduces tools a practices for students and professionals alike.

ProfessorGregory Baecher, PhDGregory Baecher, PhDStats 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

Books*

Bayesian Data 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. Rubincarlo Introducing Monte Carlo Methods with R (Use R!) 2010th Edition By Christian P. Robert and George Casellasignal noise 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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