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Бумага Data Copy A5 80g/m2 500 листов A+

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Greg Harvey Excel 2010 For Dummies


Crunch numbers, create spreadsheets, and get up to speed on Excel 2010! This friendly book gets you started with the basics of Excel 2010, such as creating a spreadsheet from scratch, selecting commands from the Ribbon, customizing the Quick Access toolbar, creating simple formulas, moving and copying data with drag and drop, using the AutoCorrect and AutoFill features, and more. Navigate effectively – see how the Ribbon interface and the Backstage View give you access to all the tools you need for every task Be a mover and a shaker – move and copy data with cut, copy, and paste or drag and drop Summing up – total columns and rows of numbers with the AutoSum button Making it pretty – format data tables, adjust column widths and row heights, hide columns and rows, and format with Styles or the Format Painter Safety net – save your work and recover spreadsheets after a computer crash Open the book and find: Whats new in Excel 2010 How to surf an Excel 2010 worksheet and workbook Instructions for entering data in a worksheet Tips for keeping your data secure How to enter and edit formulas with built-in functions Tips for undoing or deleting data entry mistakes Learn to: Get comfortable with Excel 2010 and Backstage View Create and edit spreadsheets Format cells, create formulas, and fix data entry errors Copy, cut, move, paste, or delete data

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Iafrate From Big Data To Smart


Книга "From Big Data To Smart Data".

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Holger Walther Integration externer Informationen in ein Data Warehouse zur Unterstutzung des Managements


Inhaltsangabe:Inhaltsverzeichnis:Inhaltsverzeichnis:AutorenreferatInhaltsverzeichnisAbkürzungsverzeichnisAbbildungsverzeichnisTabellenverzeichnisAnlagenverzeichnis1.Einleitung1.1Ausgangssituation1.2Zielstellung der Arbeit1.3Aufbau der Arbeit2.Grundlagen des Managementcomputing2.1Daten als Basis unternehmerischer Entscheidungen2.2Produktions- und Wettbewerbsfaktor Information2.3Management Support Systeme3.Data-Warehouse-Konzept3.1Ausgangspunkt3.2Charakteristik des Data Warehouse3.3Data-Warehouse-Architektur3.3.1Datenbasis3.3.2Extraktions- und Transformationswerkzeuge3.3.3Metadatenbanksystem3.3.4Auswertungs- und Analysewerkzeuge3.3.5Archivierungssystem3.3.6Hard- und Softwarevoraussetzungen3.4Organisationsformen eines Data Warehouse3.4.1Virtuelles Data Warehouse3.4.2Zentrales Data Warehouse3.4.3Dezentrales Data Warehouse / Data Mart3.5Vorgehensmodell zum Aufbau eines Data Warehouse3.6Evaluierung des Data-Warehouse-Konzeptes4.Quellen externer Informationen4.1Online-Datenbanken4.2Online-Dienste4.3Internet / World Wide Web4.4Sonstige Quellen externer Informationen4.5Beurteilung externer Informationsquellen5.Integration externer Informationen in ein Data Warehouse5.1Betriebswirtschaftliche Problemstellung5.2Fachliches Integrationskonzept5.2.1Informationsauswahl5.2.1.1Informationsbedarfsanalyse5.2.1.2Informationsangebotsanalyse5.2.2Informationsbeschaffung5.2.3Informationssynthese5.2.3.1Konsolidierung5.2.3.2Aggregation5.2.3.3Integration5.2.4Informationsspeicherung5.2.4.1Datenmodellier...

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Thomas Hammergren C. Data Warehousing For Dummies


Data warehousing is one of the hottest business topics, and there’s more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition. Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to: Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic!

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Stephen R Massey The Ultimate GDPR Practitioner Guide. Demystifying Privacy & Data Protection


The Ultimate GDPR Practitioner Guide provides those tasked with implementing Data Protection processes, useful information on how to achieve compliance with GDPR. The book is crammed with advice, guidance and templates and also includes a copy of the full regulation text and the supporting recitals. Topics include:• The Data Protection Officer• Data Protection Policy• Data Protection / Privacy Notices• Data Protection Impact Assessments (DPIA)• Data Protection / Privacy by Design• Outsourcing• Subject Access Requests• And Much Much More!“We’re all going to have to change how we think about data protection.” Elizabeth Denham, UK Information CommissionerWhen Elizabeth Denham, the UK Information Commissioner, delivered the above quote at a lecture for the Institute of Chartered Accountants in England and Wales in London on 17 January 2017, she was highlighting the requirement for organisations to be accountable for the Personal Data they hold and process. Under the EU General Data Protection Regulation (GDPR) we all need to up our game!GDPR is a transformative piece of regulation that applies from 25 May 2018. GDPR enhances current rights and freedoms afforded to EU citizens under the 1995 EU Data Protection Directive (95/46/EC). GDPR gives Supervisory Authorities strengthened powers to take enforcement action on those organisations who fail in their duty to uphold those rights and freedoms. GDPR is a game-changer!

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Zacharias Voulgaris Data Science. Mindset, Methodologies, and Misconceptions


Master the concepts and strategies underlying success and progress in data science.From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist’s toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework.The following chapters cover these four foundational areas: Chapter 1 - What Is Data Science? Chapter 2 - The Data Science Pipeline Chapter 3 - Data Science Methodologies Chapter 4 - The Data Scientist’s Toolbox Chapter 5 - Questions to Ask and the Hypotheses They Are Based On Chapter 6 - Data Science Experiments and Evaluation of Their Results Chapter 7 - Sensitivity Analysis of Experiment Conclusions Chapter 8 - Programming Bugs Chapter 9 - Mistakes Through the Data Science Process Chapter 10 - Dealing with Bugs and Mistakes Effectively and Efficiently Chapter 11 - The Role of Heuristics in Data Science Chapter 12 - The Role of AI in Data Science Chapter 13 - Data Science Ethics Chapter 14 - Future Trends and How to Remain RelevantTargeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and ob...

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Книга "Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX. Special Issue on Big Data and Open Data".

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Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Data


The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

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Peter Zadrozny, Raghu Kodali Big Data Analytics Using Splunk. Deriving Operational Intelligence from Social Media, Machine Data, Existing Warehouses, and Other Real-Time Stre


Книга "Big Data Analytics Using Splunk. Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Stre".

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Chunlei Tang The Data Industry. Business and Economics of Information Big


Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

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Hitesh Chhinkaniwala and Sanjay Garg Privacy Preserving Data Mining - Issues & Techniques


Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.

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Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining


The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

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Pervasive, data is a unique organizational resource, and this distinction warrants its own strategy. Data, representing your single non-depletable, non-degradable, durable strategic asset, is likely also your most poorly leveraged and underutilized organizational asset.Lack of talent, barriers in organizational thinking, and seven specific data sins prevent most organizations from benefiting fully from their data asset investments. Solving these prerequisites will allow your organization to:• Improve your organization’s data;• Improve the way your people use data; and• Improve the way your people use data to achieve your organizational strategy.This method better focuses data and thinking in direct support of strategic objectives. After eliminating necessary prerequisites, organizations can develop a disciplined and repeatable means of improving their data, literacy, standards, and controls using data governance practices. Once in place, the process (based on the theory of constraints) becomes a variant of lather, rinse, and repeat. Several complementary concepts covered include:• An overview of data strategy prerequisites;• A repeatable process for identifying and removing data constraints;• Why data strategy is necessary for effective data governance;• Balancing operational results with capability development;• An objective definition of data-centric thinking; and• Ways to monetize these efforts.

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Книга "Data Lake Development with Big Data".

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Amar Sahay Data Visualization, Volume I. Recent Trends and Applications Using Conventional Big


Data visualization involves graphical and visual tools used in data analysis and decision making. The emphasis in this book is on recent trends and applications of visualization tools using conventional and big data. These tools are widely used in data visualization and quality improvement to analyze, enhance, and improve the quality of products and services. Data visualization is an easy way to obtain a first look at the data visually. The book provides a collection of visual and graphical tools widely used to gain an insight into the data before applying more complex analysis. The focus is on the key application areas of these tools including business process improvement, business data analysis, health care, finance, manufacturing, engineering, process improvement, and Lean Six Sigma. The key areas of application include data and data analysis concepts, recent trends in data visualization and ÒBig Data,Ó widely used charts and graphs and their applications, analysis of the relationships between two or more variables graphically using scatterplots, bubble graphs, matrix plots, etc., data visualization with big data, computer applications and implementation of widely used graphical and visual tools, and computer instructions to create the graphics presented along with the data files.

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Bill Inmon Data Lake Architecture. Designing the and Avoiding Garbage Dump


Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps.Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.

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Harish Gulati, Charles Joseph Data Analyst. Careers in Analysis


Data is constantly increasing; everything from app usage, to sales, to customer surveys generate data in an average business. Out on the streets data is everywhere too, from speed and security cameras, weather monitoring and measuring footfall to name just a few examples. Against this backdrop, data analysts are in higher demand than ever.This book is an essential guide to the role of data analyst. Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to. Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.

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Penny Jeffrey and Gillian Garbus Mining New Gold-Managing Your Business Data. Data Management for Owners


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Rajesh Jugulum Competing with High Quality Data. Concepts, Tools, and Techniques for Building a Successful Approach to Data


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.

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Mark Gardener Managing Data Using Excel


Microsoft Excel is a powerful tool that can transform the way you use data. Thisbook explains in comprehensive and user-friendly detail how to manage, make sense of, explore and share data, giving scientists at all levels the skills they need to maximize the usefulness of their data.Readers will learn how to use Excel to:* Build a dataset - how to handle variables and notes, rearrangements and edits to data.* Check datasets - dealing with typographic errors, data validation and numericalerrors.* Make sense of data - including datasets for regression and correlation; summarizing data with averages and variability; and visualizing data with graphs, pivot charts and sparklines.* Explore regression data - finding, highlighting and visualizing correlations.* Explore time-related data - using pivot tables, sparklines and line plots.* Explore association data - creating and visualizing contingency tables.* Explore differences - pivot tables and data visualizations including box-whisker plots.* Share data - methods for exporting and sharing your datasets, summaries andgraphs.Alongside the text, Have a Go exercises, Tips and Notes give readers practicalexperience and highlight important points, and helpful self-assessment exercises and summary tables can be found at the end of each chapter. Supplementary material can also be downloaded on the companion website.Managing Data Using Excel is an essential book for all scientists and students who use data and are seeking to manage data mor...

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Matthew Gillingham SAS Programming with Medicare Administrative Data


SAS Programming with Medicare Administrative Data is the most comprehensive resource available for using Medicare data with SAS. This book teaches you how to access Medicare data and, more importantly, how to apply this data to your research. Matthew Gillingham has created a book that is both a foundation for programmers new to Medicare data and a comprehensive reference for experienced programmers.

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Harvey Goldstein Methodological Developments in Data Linkage


A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.

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Zach Gemignani Data Fluency. Empowering Your Organization with Effective Communication


A dream come true for those looking to improve their data fluency Analytical data is a powerful tool for growing companies, but what good is it if it hides in the shadows? Bring your data to the forefront with effective visualization and communication approaches, and let Data Fluency: Empowering Your Organization with Effective Communication show you the best tools and strategies for getting the job done right. Learn the best practices of data presentation and the ways that reporting and dashboards can help organizations effectively gauge performance, identify areas for improvement, and communicate results. Topics covered in the book include data reporting and communication, audience and user needs, data presentation tools, layout and styling, and common design failures. Those responsible for analytics, reporting, or BI implementation will find a refreshing take on data and visualization in this resource, as will report, data visualization, and dashboard designers. Conquer the challenge of making valuable data approachable and easy to understand Develop unique skills required to shape data to the needs of different audiences Full color book links to bonus content at juiceanalytics.com Written by well-known and highly esteemed authors in the data presentation community Data Fluency: Empowering Your Organization with Effective Communication focuses on user experience, making reports approachable, and presenting data in a compelling, inspiring way. The book helps to dissolve the disconnect between your data and those who might use it and can help make an impact on the people who are most affected by data. Use Data Fluency today to develop the skills necessary to turn data into effective displays for decision-making.

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Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Analysis and Mining


Praise for the First Edition “…a well-written book on data analysis and data mining that provides an excellent foundation…” —CHOICE “This is a must-read book for learning practical statistics and data analysis…” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

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