Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling.
The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python, and provides pseudo-algorithms to port the code to any other language.
Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like:
Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis
A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity
Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages
An exploration of algorithms, including how to write one and how to perform an asymptotic analysis
A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 9720.00 р. Наличие на складе: Поставка под заказ.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Автор: Wei Emma Zhang; Quan Z. Sheng Название: Managing Data From Knowledge Bases: Querying and Extraction ISBN: 3030069400 ISBN-13(EAN): 9783030069407 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual’s historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries’ structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system.To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use clustering technique to separate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraint in the optimization task and achieves fast and accurate performance.For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.
Автор: Lopes Pedro, Lahoud Pam Название: Learn T-SQL Querying ISBN: 1789348811 ISBN-13(EAN): 9781789348811 Издательство: Неизвестно Рейтинг: Цена: 11033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: T-SQL is an extension of the SQL language which allows you to tackle advanced querying and query-tuning challenges in SQL Server and Azure SQL Database. This book will be a perfect reference for you to write more efficient T-SQL code to perform simple-to-advanced tasks for data management and data analysis.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: Burattin, Andrea Название: Process mining techniques in business environments ISBN: 3319174819 ISBN-13(EAN): 9783319174815 Издательство: Springer Рейтинг: Цена: 6708.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.
Описание: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution.
Автор: Paolo Ceravolo; Rafael Accorsi; Philippe Cudre-Mau Название: Data-Driven Process Discovery and Analysis ISBN: 3662464357 ISBN-13(EAN): 9783662464359 Издательство: Springer Рейтинг: Цена: 7267.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed proceedings of the Third International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Garda, Italy, in August 2013.The six revised full papers were carefully selected from 18 submissions.
Описание: Focuses on database query processing methods, technologies, and applications. Aimed at providing an all-inclusive reference source of technologies and practices in advanced database query systems, this book investigates various techniques, including database and XML queries, spatiotemporal data queries, big data queries, metadata queries, and applications of database query systems.
If you are a student or a professional looking for more technical skills, or if you are simply curious about the most up-to-date data analysis techniques and their powerful applications, then this is definitely the book for you.
Learning all of the required skills to master data science and machine learning could certainly be challenging, but in this book, Jason Callaway has condensed all of the knowledge you need into a clear and beginner-friendly introduction, with practical examples, detailed explanations, and tips and tricks from his experience.
Through his revolutionary and systematic approach, you can learn techniques to manipulate and process datasets, the principles of Python programming, and their real-world applications, regardless of your previous experience.
Here's just a tiny fraction of what you will discover:
What data science is, and why it has become fundamental in hundreds of business and technological applications
The basics of Python programming
Essential Python libraries such as NumPy, Pandas, and Matplotlib
All of the most effective computational methods for data analysis
Data visualization tools and techniques
How to build statistical and machine learning models (even if you are brand new to programming)
The future of Artificial Intelligence
How to build neural networks with Python
Step-by-step exercises, practical examples, and tips and tricks
Are you ready to develop a successful career in the growing industry of data science?
Автор: Seyed-Mehdi-Reza Beheshti; Boualem Benatallah; She Название: Process Analytics ISBN: 3319250361 ISBN-13(EAN): 9783319250366 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data.
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