Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges. The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.
Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution
This book, written by one of the founding fathers of statistical quality control, covers the latest measurement technology for multi- variable processes.
Design for Lean Six Sigmais the only book that employs a "road-map" approach to DFSS, which allows corporate management to understand where they are in the process and to integrate DFSS methodology more fully into their overall business strategy. This is a similar approach to that used by Forrest Breyfogle in his successful book: "Implementing Six Sigma, 2E". This approach will allow corporate management to understand where they are in the process and to integrate DFSS methodology more fully into the overall business strategy. Another important aspect of this book is its coverage of DFSS implementation in a broad range of industries including service and manufacturing, plus the use of actual cases throughout.
Design for Lean Six Sigmais the only book that employs a "road-map" approach to DFSS, which allows corporate management to understand where they are in the process and to integrate DFSS methodology more fully into their overall business strategy. This is a similar approach to that used by Forrest Breyfogle in his successful book: "Implementing Six Sigma, 2E". This approach will allow corporate management to understand where they are in the process and to integrate DFSS methodology more fully into the overall business strategy. Another important aspect of this book is its coverage of DFSS implementation in a broad range of industries including service and manufacturing, plus the use of actual cases throughout.
Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, but low-quality data can actually put a company at a disadvantage. To be used effectively, data must accurately reflect the real-world scenario it represents, and it must be in a form that is usable and accessible. Quality data involves asking the right questions, targeting the correct parameters, and having an effective internal management, organization, and access system. It must be relevant, complete, and correct, while falling in line with pervasive regulatory oversight programs. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality takes a holistic approach to improving data quality, from collection to usage. Author Rajesh Jugulum is globally-recognized as a major voice in the data quality arena, with high-level backgrounds in international corporate finance. In the book, Jugulum provides a roadmap to data quality innovation, covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of a business Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effective data gathering The book also contains real-world case studies to illustrate how companies across a broad range of sectors have employed data quality systems, whether or not they succeeded, and what lessons were learned. High-quality data increases value throughout the information supply chain, and the benefits extend to the client, employee, and shareholder. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality provides the information and guidance necessary to formulate and activate an effective data quality plan today.
Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges. The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.
This book, written by one of the founding fathers of statistical quality control, covers the latest measurement technology for multi- variable processes.
Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution
Mahāvīra (or Mahaviracharya; "Mahavira the Teacher") was a 9th-century Jain mathematician from Bihar; India. He was the author of Gaṇitasārasan̄graha (or Ganita Sara Samgraha; c. 850); which revised the Brāhmasphuṭasiddhānta.He was patronised by the Rashtrakuta king Amoghavarsha. He separated astrology from mathematics. It is the earliest Indian text entirely devoted to mathematics. He expounded on the same subjects on which Aryabhata and Brahmagupta contended; but he expressed them more clearly. His work is a highly syncopated approach to algebra and the emphasis in much of his text is on developing the techniques necessary to solve algebraic problems. He is highly respected among Indian mathematicians; because of his establishment of terminology for concepts such as equilateral; and isosceles triangle; rhombus; circle and semicircle. Mahāvīra's eminence spread in all South India and his books proved inspirational to other mathematicians in Southern India. It was translated into Telugu language by Pavuluri Mallana as Saar Sangraha Ganitam. He discovered algebraic identities like a3=a(a+b)(a-b) +b2(a-b) + b3. He also found out the formula for nCr as [n(n-1)(n-2)...(n-r+1)]/r(r-1)(r-2)...2*1. He devised formula which approximated area and perimeters of ellipses and found methods to calculate the square of a number and cube roots of a number. He asserted that the square root of a negative number did not exist. Mahaviracharya by Rajesh Kumar Thakur: "Mahaviracharya" is a biography of the eminent Jain philosopher, Acharya Mahavira, written by Rajesh Kumar Thakur. The book provides a comprehensive account of Mahavira's life, teachings, and significant contributions to Jainism, a religion known for its principles of non-violence and spiritual enlightenment. Key Aspects of the Book "Mahaviracharya": Life of Acharya Mahavira: The book delves into the life and journey of Acharya Mahavira, a revered spiritual leader and the 24th Tirthankara of Jainism. Jain Philosophy and Teachings: "Mahaviracharya" explores the core tenets of Jainism, including non-violence (ahimsa), self-discipline, and the pursuit of spiritual liberation. Legacy and Influence: The book highlights the enduring impact of Acharya Mahavira's teachings on Jainism and its followers.
This revised and expanded second edition is a learning and self-assessment tool for the study of regional anesthesia. The first part deals with the basic principles of regional anesthesia and the equipment used. This is followed by sections on peripheral nerve blocks, central neuraxial blocks and pain. Pediatric regional anesthesia is discussed along with the adult blocks. There are additional MCQs in each section, and new chapters on the anatomy, physiology, assessment and monitoring of acute pain. This book is aimed at those studying for the European Society of Regional Anesthesia Diploma Examinations, regional anesthesia component of FRCA examinations, and exit examinations for regional anesthesia fellowships. It is also relevant to the regional anesthesia component of US Board examinations and the Canadian fellowships in regional anesthesia.
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