Data Science & Advanced Analytics

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Chair: H. Lance Stephenson, CCP FAACE
Vice-Chair: Michael A. Pink
RP Coordinator: Kaylyn Mickelsen, PSP
Secretary: Jeseka Snider
Technical Board Liaison: H. Lance Stephenson, CCP FAACE

The Data Science & Advanced Analytics (DSAA) Subcommittee will be responsible for establishing the scope and definition of data science & advanced analytics and developing the association’s recommended practices. 

The main responsibility of the subcommittee is to bring talented, competent and creative cost engineering practitioners together to learn and share new skills, discover emerging methods and related technologies. This includes evolving existing practices, skills and knowledge of project data to support the broader adoption of data science & advanced analytics, which shall include the following major topics:

  • Data science, data analytics, advanced analytics and AI (artificial intelligence).
  • Industrial internet of things or IIoT (e.g., and remote sensing and edge computing).
  • Digital twin, virtual reality, augmented reality & building information modelling (BIM).
  • Additive manufacturing or 3D printing and industrialized construction (IC).
  • Blockchain and smart contracts.
  • Robotic process automation (RPA).
  • Other emerging technologies recognized as being disruptive to the engineering and construction industry (as reported, for example, by organizations such as Gartner or McKinsey).

An important function of a subcommittee is the development of AACE recommended practices (RPs). These are peer-reviewed documents (usually < 10 pages) that define the specifics of particular methods or procedures, often derived from past papers and articles. Click here to learn more about the RP Development Guidelines and Process.

Latest Discussion Posts

  • Great video! I've been working on assigning cost accounts to schedule activities and I think this would be a good method to test out. ------------------------------ Craig Stadnyk Scheduler ------------------------------

  • Hi all Just shared a new tutorial for data scientists interested in applied project management. We show how to use Python (TF-IDF, Cosine Similarity, and Logistic Regression) to automate and standardize cost account (COA) classification for construction/infrastructure ...

    1 person likes this.
  • I would advise caution before jumping straight to P90/P50 calculations. Using advanced algorithms like Gradient Boosting, Random Forest or the one you have suggested requires that the underlying data supports the complexity of the model. Before interpreting ...

  • Unknown publisher, Thank you fir sharing this, I'm very interesting and intrigued on what the thoughts on the behind, so this model requires some patterns of cost based on data, so is the data us in one industry specific how can be use for other ...

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    Machine Learning Model for Cost Estimate

    This message was posted by a user wishing to remain anonymous Dear All, I have been developing ML for Cost Estimate especially in oil and gas projects which attributed to features important, correlation factors and cost curve. The model was developed ...