Data Analytics in Project Management, Chess, Paul M.
Автор: Guo, Xin , Lai, Tze Leung , Shek, Howard , Wong Название: Quantitative Trading ISBN: 0367871815 ISBN-13(EAN): 9780367871819 Издательство: Taylor&Francis Рейтинг: Цена: 10104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part cove
Автор: Kloppenborg, Timothy Название: Contemporary project management ISBN: 1337406457 ISBN-13(EAN): 9781337406451 Издательство: Cengage Learning Рейтинг: Цена: 17536.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn to master the most proven methods in project management as well as exciting new techniques emerging from current industry and today's most recent research with Kloppenborg's CONTEMPORARY PROJECT MANAGEMENT, 4E. This edition introduces time-tested manual techniques and progressive automated techniques, all consistent with the latest PMBOK (R) Guide and standards and integrated with Microsoft (R) Project 2016. The book's focused approach is ideal for building strong portfolios that showcase project management skills for future interviews.
All content is consistent with the knowledge areas and processes of the 6th edition of the PMBOK (R) Guide to give you an advantage as you prepare to become a Certified Associate in Project Management (CAPM (R)) or Certified Project Management Professional (PMP (R)), if desired.
Автор: Burke Rory Название: Project Management ISBN: 1118561252 ISBN-13(EAN): 9781118561256 Издательство: Wiley Рейтинг: Цена: 8070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: * Reflects changes in the new versions of the bodies of knowledge of the PMI (American Project Management Institute) and the APM (British Association for Project Management) *7 additional chapters including expanded coverage of project methodology, project management process and knowledge area trade-offs.
Описание: There are many different project management programs out there that you can choose to work with. But none are going to be as efficient as the Lean methodology.
Описание: This text provides a comprehensive overview of Data Science. With the continued advancement in storage and computing technologies, data science has emerged as one of the most desired fields in driving business decisions. Data science employs techniques and methods from many other fields, such as statistics, mathematics, computer science, and information science. Besides the methods and theories drawn from several fields, data science uses visualization techniques using specially designed big data software and statistical programming language, such as R programming, and Python. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book is divided into four different areas divided into different chapters. These chapters explain the core of Data Science. Part I of the book introduces the field of Data Science, different disciplines it comprises of, and the scope with future outlook and career prospects. This section also explains analytics, business analytics, and business intelligence and their similarities and differences with Data Science. Since the data is at the core of Data science, Part II is devoted to explaining the data, big data, and other features of data. One full chapter is devoted to Data Analysis, creating visuals, pivot table, and other applications using Excel with office 365. Part III explains the statistics behind Data Science. It uses several chapters to explain the statistics and its importance, numerical and data visualization tools and methods, probability, and probability distribution applications in Data Science. Other chapters in the Part III are Sampling, Estimation, and Hypothesis Testing. All these are integral part of Data Science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical software. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI) and R-statistical software is widely used by data science professionals. The book also outlines a brief history, the body of knowledge, skills and education requirements for Data Scientist and data science professionals. Some statistics on job growth and prospects are also summarized. A career in data science is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.[29]
Chapter 1: Introduction to Risk and Uncertainty. This chapter provides: a) general discussion on the types of uncertainties in projects including the examples; we cover theoretical, frequentist, belief-based epistemic, as well as agnostic viewpoints on the uncertainty; we show these viewpoints in context of typical project uncertainties and contrast them against representations of uncertainty in other engineering disciplines; b) summary on the role of knowledge and assumptions in characterizing the uncertainty; we link the discussion on uncertainty to knowledge about the underlying phenomena, the embedded assumptions, and their validity over the course of the project; c) overview on the approaches that relate the risk to the underlying uncertainty; we discuss approaches to the risk-uncertainty relationship in different disciplines, and finally d) discussion on the organizational attitude and viewpoints toward the risk and uncertainty; we cover topics such as value of u
ncertainty (is it always bad?), organizational responsibility towards risk (who should be taking risk, when, and how much?), and the contrast between the decision-theoretic vs. managerial viewpoint on the uncertainty showing the differences that govern the choice of analysis and the methods.
Chapter 2.Project Risk Management Framework. This chapter provides: a) overview of the project systems, their complexity, life-cycle and risk-based decision-making; we define project as a complex system, and its life-cycle in the context of phase-gate process where decisions are evaluated under different objectives and criteria; we emphasize the points where the uncertainty is introduced and when it is reflected in project outcomes; we particularly stress the design and construction/installation i.e. execution phases of a project as this is the key focus of this text; b) outline of the high-level guidelines in conducting risk assessment and management (such as
ISO and PMI approach), the use of "risk language" and common terms in communicating risk (such as SRA glossary of terms), and more detailed description of each step; we particularly emphasize risk identification and assessment as they are the key focus of this text; c) formal definition of risk in projects distinguishing between variability of operations, event driven risk factors, and the combination of the two; also, we discuss risks in context of low probability - high impact and low impact - high probability; we emphasize the role of assumptions and knowledge in formally developing risk statement; and finally d) classifications methods for project risks as they relate to project objectives, their inception and resolution period, relationship to project structure i.e. internal-external, technical-no technical, and other key project parameters. The chapter includes homework examples.
Chapter 3: Project Data. This chapter provides a comprehensive summary on the type and sources of project data, and the methods for data acquisition. The key underpinning of this text is that risk analysis should be driven by data in a mathematically rigorous way; so where can one find such data? This chapter covers project data as they relate to planning and execution phase of the project; more specifically, we discuss data in terms of: a) project phase and system of interest; we contrast available data during planning and estimation vs. data during monitoring and control phase of the project, as well as whether data relates to internal project system (logistics, operations, etc.) or environmental systems (weather, market trends, etc), we define data collection objectives for each of the phase and the system type; b) observed vs. judgement/simulated data, or in other words, whether data is generated by the system and recorded by the participants, or assessed by individuals using their experience, judgements, models, or just gut f
Название: Data analytics in project management ISBN: 1138307289 ISBN-13(EAN): 9781138307285 Издательство: Taylor&Francis Рейтинг: Цена: 19140.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Damnjanovic Ivan Название: Data Analytics for Engineering and Construction Project Ris ISBN: 3030142507 ISBN-13(EAN): 9783030142506 Издательство: Springer Рейтинг: Цена: 9756.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chapter 1: Introduction to Risk and Uncertainty. This chapter provides: a) general discussion on the types of uncertainties in projects including the examples; we cover theoretical, frequentist, belief-based epistemic, as well as agnostic viewpoints on the uncertainty; we show these viewpoints in context of typical project uncertainties and contrast them against representations of uncertainty in other engineering disciplines; b) summary on the role of knowledge and assumptions in characterizing the uncertainty; we link the discussion on uncertainty to knowledge about the underlying phenomena, the embedded assumptions, and their validity over the course of the project; c) overview on the approaches that relate the risk to the underlying uncertainty; we discuss approaches to the risk-uncertainty relationship in different disciplines, and finally d) discussion on the organizational attitude and viewpoints toward the risk and uncertainty; we cover topics such as value of u
ncertainty (is it always bad?), organizational responsibility towards risk (who should be taking risk, when, and how much?), and the contrast between the decision-theoretic vs. managerial viewpoint on the uncertainty showing the differences that govern the choice of analysis and the methods.
Chapter 2.Project Risk Management Framework. This chapter provides: a) overview of the project systems, their complexity, life-cycle and risk-based decision-making; we define project as a complex system, and its life-cycle in the context of phase-gate process where decisions are evaluated under different objectives and criteria; we emphasize the points where the uncertainty is introduced and when it is reflected in project outcomes; we particularly stress the design and construction/installation i.e. execution phases of a project as this is the key focus of this text; b) outline of the high-level guidelines in conducting risk assessment and management (such as
ISO and PMI approach), the use of "risk language" and common terms in communicating risk (such as SRA glossary of terms), and more detailed description of each step; we particularly emphasize risk identification and assessment as they are the key focus of this text; c) formal definition of risk in projects distinguishing between variability of operations, event driven risk factors, and the combination of the two; also, we discuss risks in context of low probability - high impact and low impact - high probability; we emphasize the role of assumptions and knowledge in formally developing risk statement; and finally d) classifications methods for project risks as they relate to project objectives, their inception and resolution period, relationship to project structure i.e. internal-external, technical-no technical, and other key project parameters. The chapter includes homework examples.
Chapter 3: Project Data. This chapter provides a comprehensive summary on the type and sources of project data, and the methods for data acquisition. The key underpinning of this text is that risk analysis should be driven by data in a mathematically rigorous way; so where can one find such data? This chapter covers project data as they relate to planning and execution phase of the project; more specifically, we discuss data in terms of: a) project phase and system of interest; we contrast available data during planning and estimation vs. data during monitoring and control phase of the project, as well as whether data relates to internal project system (logistics, operations, etc.) or environmental systems (weather, market trends, etc), we define data collection objectives for each of the phase and the system type; b) observed vs. judgement/simulated data, or in other words, whether data is generated by the system and recorded by the participants, or assessed by individuals using their experience, judgements, models, or just gut f
Автор: Badiru, Adedeji Bodunde Название: Project management essentials ISBN: 0367431181 ISBN-13(EAN): 9780367431181 Издательство: Taylor&Francis Рейтинг: Цена: 8420.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This Focus book presents the basic principles and practice of project management and simple analytics for project control. The overriding theme of the book is that every pursuit can be organized as a project.
Описание: Every business wishes to provide an exceptional product to their customers. They want to be able to do this and meet their customers` needs, while also reducing costs to ensure profits stay as high as possible. But in many companies, there is a level of waste and inefficiency that shouldn`t be there.
Автор: Kenneth David Strang, Maximiliano E. Korstanje, Narasimha Vajjhala Название: Research, Practices, and Innovations in Global Risk and Contingency Management ISBN: 1522547541 ISBN-13(EAN): 9781522547549 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35759.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Risk management is a vital concern in any organization. In order to succeed in the competitive modern business environment, the decision-making process must be effectively governed and managed. Research, Practices, and Innovations in Global Risk and Contingency Management is a critical scholarly resource that provides an all-encompassing holistic discussion of risk management and perception, while giving readers innovations on empirical risk-contingency management research and case studies. Featuring coverage on a broad range of topics, such as contingency planning, project management, and risk mitigation, this book is geared towards academicians, practitioners, and researchers seeking current research on risk and contingency management issues.
Автор: Brian McBreen, Denise Bedford, John Silson Название: Organizational Intelligence and Knowledge Analytics ISBN: 1802621784 ISBN-13(EAN): 9781802621785 Издательство: Emerald Рейтинг: Цена: 14024.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Organizational Intelligence and Knowledge Analytics expands the traditional intelligence life cycle to a new framework - Design-Analyze-Automate-Accelerate - and clearly lays out the alignments between knowledge capital and intelligence strategies.
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