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The material motivates why adopting a repeatable methodology improves consistency of outcomes, provides shared assumptions and vocabulary, reduces coordination overhead through clarity of process, and enables continuous self-improvement through codified practices. It defines what projects are, outlines PMI criteria for project success (time, budget, performance/specification, and customer acceptance), and introduces core economic concepts like payback period, break-even points, and ROI. 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Leidner, M.A. M. Phil. Ph. D.  \nUniversity of She􀀎eld / Re􀀌nitiv  \nIEEE DSAA 2018 􀀁 3 October 2018  \nJochen L. Leidner Project Management for Data Science  \nCopyright © 2018 by Jochen L. Leidner 􀀁 All rights reserved.  \nJochen L. Leidner Project Management for Data Science  \nObjectives  \nIn this tutorial, I aim to:  \n introduce you to some basic concepts and realities of project management as practiced in commercial and governmental organizations  \n describe the Data-to-Value methodology for project management of data science projects  \n(especially for those using NLP & ML)  \n convey some best practices for data-centric projects  \nJochen L. Leidner Project Management for Data Science  \n Methodology { Motivation (1/2)   \nWhen I started teaching big data and data science, I discovered  \n there were no papers on methodology for data science (unlike software project management methodology)  \nWhen I had to mentor new team members in industry, I discovered  \n there were no papers on methodology for data science (unlike software project management methodology)  \n) Need to try and 􀀌x that!  \nJochen L. Leidner Project Management for Data Science  \nFundamentals of Project Management  \nJochen L. Leidner Project Management for Data Science  \n Methodology { Motivation (2/2)   \nWhy care about methodology?  \n (Relatively higher) consistency of outcome  \n Guidance to less experienced engineers  \n Provides a common set of assumptions, expectations & shared vocabulary in a team  \n Clarity of process reduces necessary  \ncoordination/communications (alignment)  \n Codi􀀌cation of best practices leads to a culture of continuous self-improvement  \nJochen L. Leidner Project Management for Data Science  \nProject: A De􀀌nition  \nProject  \nA project is a time-limited activity to deploy de􀀌ned resources toe􀀋ect change with a de􀀌ned scope with the aim to bene􀀌t.   \nJochen L. Leidner Project Management for Data Science  \nProject Success: 4 Criteria by the Project Management Institute (PMI)  \nA project is completed successfully if it is completed:  on time ,  \n on budget ,  \n at performance level/to speci􀀌cation, and  with customer acceptance.  \nJochen L. Leidner Project Management for Data Science  \n Payback Period, Break-Even Point & ROI:   \nPayback Period: time period until break-even point (BEP) is reached  \n Return on Investment (ROI): a measure, per period, of interest rate of return on money invested in an entity in order to decide whether to undertake an investment  \n break-even point (BEP) : the point in time for which the gain from an investment less the cost of investment to obtain that gain equals zero  \n ROI = (gain from investment 􀀀  \ncost of investment)=cost of investment  \nJochen L. Leidner  \nProject Management for Data Science  \nEstimating the Cost of Systems (Leidner, in prep.)   \nCTotal = CPM + CRes + CDev + CComp + CData + CKM  \n CPM : the cost of project management, i.e. the cost of planning, initiating, executing/controlling and closing the project  \n CRes: the cost of research activities required to develop the system (prior art, evaluative comparison of existing systems, determining features, regular ongoing quantitative evaluation)  \n CDev: the cost of developing and qualitative testing of the software and rules (e.g. \\lingware\") that constitute the system  \n CComp: the cost of licensing in existing components to develop the system  \n CData: the cost of licensing in existing data plus the cost of curating new data and/or meta-data (annotation layers, tags)  \n CKM : the cost of knowledge management (internal and externally facing: authoring customer documentation, authoring internal maintenance documentation, API documentation, training  \nmaterials)        \n Jochen L. Leidner  Project Management for Data Science","cbCail4tpSQlOuTm","https://ap.wps.com/l/cbCail4tpSQlOuTm","pdf",1052434,62,"English","# Objectives\n# Methodology: Motivation\n## Motivation (1/2)\n## Motivation (2/2)\n# Fundamentals of Project Management\n## Project: A Definition\n## Project Success: 4 Criteria (PMI)\n# Payback Period, Break-Even Point & ROI\n# Estimating the Cost of Systems","[{\"question\":\"What does the tutorial aim to teach about project management for data science?\",\"answer\":\"It covers basic concepts and realities of project management, the Data-to-Value methodology for data science projects, and best practices for data-centric work, especially for NLP and ML teams.\"},{\"question\":\"Why does the tutorial emphasize using a methodology in project management?\",\"answer\":\"It improves consistency of outcomes, guides less experienced engineers, establishes shared vocabulary and assumptions, reduces coordination needs by clarifying process alignment, and supports continuous self-improvement through codified best practices.\"},{\"question\":\"What criteria determine whether a project is successful according to PMI?\",\"answer\":\"A project is successful when it is completed on time, on budget, at the required performance level/specification, and with customer acceptance.\"}]","Project Management for Data Science - DSAA 2018 Tutorial | PDF",1791215803,156]