Data Analytics in Project Management

List of contents

Introduction to Data Analytics (DA). Why Data Analytics in Project Management (PM)? The Importance of DA in PM. The Key role of Data Analytics in Business Analysis. Business Analysis in Managing Projects. Earned Value Method. IT solutions of DA as Applied to PM. How to manage Big Data issues in Projects’ Environment. Data Mining and the Project Management Office. Project Portfolio Management. Future Directions.

About the author

Seweryn Spalek is an academic professor and head of the CS&E department at the Faculty of Organisation and Management, Silesian University of Technology. Since 1994, he has managed several projects in industrial companies and healthcare organisations, in multicultural and multinational environments. He is the author and co-author of several publications in project management. He participated as a speaker in several conferences related to project management and company management. He carried out research related to key success factors in project management and project management maturity.

Summary

This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects.

Product details

• Authors: Seweryn Spalek
• Assisted by: Seweryn Spalek (Editor)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 24.10.2018
• EAN: 9781138307285
• ISBN: 978-1-138-30728-5
• No. of pages: 222
• Series: Data Analytics Applications
• Subjects: Social sciences, law, business > Business > Advertising, marketing; BUSINESS & ECONOMICS / Project Management, BUSINESS & ECONOMICS / Finance / General, MATHEMATICS / Probability & Statistics / General, COMPUTERS / Information Technology, BUSINESS & ECONOMICS / Development / Economic Development, TECHNOLOGY & ENGINEERING / Project Management, Economic statistics, Project management, Databases, COMPUTERS / Data Science / Data Analytics, Econometrics and economic statistics