A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Author Ryan Sleeper, one of the worldâ??s most qualified Tableau consultants, complements his web posts and instructional videos with this guide to give you a firm understanding of how to use Tableau to find valuable insights in data. Over five sections, Sleeperâ??recognized as a Tableau Zen Master, Tableau Public Visualization of the Year author, and Tableau Iron Viz Championâ??provides visualization tips, tutorials, and strategies to help you avoid the pitfalls and take your Tableau knowledge to the next level. Practical Tableau sections include: Fundamentals: get started with Tableau from the beginning Chart types: use step-by-step tutorials to build a variety of charts in Tableau Tips and tricks: learn innovative uses of parameters, color theory, how to make your Tableau workbooks run efficiently, and more Framework: explore the INSIGHT framework, a proprietary process for building Tableau dashboards Storytelling: learn tangible tactics for storytelling with data, including specific and actionable tips you can implement immediately

This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set. Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. What You'll Learn Leverage well-established best practices and commonly-used Python packages Handle today's web, including JavaScript, cookies, and common web scraping mitigation techniques Understand the managerial and legal concerns regarding web scraping Who This Book is For A data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well.

The data-driven revolution is finally hitting the media and entertainment industry. For decades, broadcast television and print media relied on traditional delivery channels for solvency and growth, but those channels fragmented as cable, streaming, and digital devices stole the show. In this ebook, you’ll learn about the trends, challenges, and opportunities facing players in this industry as they tackle big data, advanced analytics, and DataOps. You’ll explore best practices and lessons learned from three real-world media companies—Sling TV, Turner Broadcasting, and Comcast—as they proceed on their data-driven journeys. Along the way, authors Ashish Thusoo and Joydeep Sen Sarma explain how DataOps breaks down silos and connects everyone who handles data, including engineers, data scientists, analysts, and business users. Big-data-as-a-service provider Qubole provides a five-step maturity model that outlines the phases that a company typically goes through when it first encounters big data. Case studies include: Sling TV: this live streaming content platform delivers live TV and on-demand entertainment instantly to a variety of smart televisions, tablets, game consoles, computers, smartphones, and streaming devices Turner Broadcasting System: this Time Warner division recently created the Turner Data Cloud to support direct-to-consumer services, including FilmStruck, Boom (for kids), and NBA League Pass Comcast: the largest broadcasting and cable TV company is building a single integrated big data platform to deliver internet, TV, and voice to more than 28 million customers

Thanks to approaches such as continuous integration and continuous delivery, companies that once introduced new products every six months are now shipping software several times a day. Reaching the market quickly is vital today, but rapid updates are impractical unless they provide genuine customer value. With this ebook, you’ll learn how online controlled experiments can help you gain customer feedback quickly so you can maintain a speedy release cycle. Using examples from Google, LinkedIn, and other organizations, Adil Aijaz, Trevor Stuart, and Henry Jewkes from Split Software explain basic concepts and show you how to build a scalable experimentation platform for conducting full-stack, comprehensive, and continuous tests. You’ll learn practical tips on best practices and common pitfalls you’re likely to face along the way. This ebook is ideal for engineers, data scientists, and product managers. Build an experimentation platform that includes a robust targeting engine, a telemetry system, a statistics engine, and a management console Dive deep into types of metrics, as well as metric frameworks, including Google’s HEART framework and LinkedIn’s 3-tiered framework Learn best practices for an building experimentation platform, such as A/A testing, power measuring, and an optimal ramp strategy Understand common pitfalls: how users are assigned across variants and control, how data is interpreted, and how metrics impact is understood

An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more stream­lined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are intro­duced through examples, and the text is designed to be accessible to readers with a variety of backgrounds. To enhance learning, most of the mathematical formulae and notation appears in technical appendices at the end of each chapter. This important resource: • Offers a comprehensive guide to understanding business statistics targeting business and economics students and professionals • Introduces the concepts and techniques through concise and intuitive examples • Focuses on understanding by moving distracting formulae and mathematical notation to appendices • Offers intuition, insights, humor, and practical advice for students of business statistics • Features coverage of sampling techniques, descriptive statistics, probability, sampling distributions, confidence intervals, hypothesis tests, and regression Written for undergraduate business students, business and economics majors, teachers, and practitioners, A Guide to Business Statistics offers an accessible guide to the key concepts and fundamental principles in statistics. DAVID M. McEVOY, PhD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years. DAVID M. M c EVOY, P h D, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The book maintains the level of rigor of a more conventional textbook in business statistics but uses a more streamlined and intuitive approach. In short, A Guide to Business Statistics provides clarity to the typical statistics textbook cluttered with notation and formulae. The author—an expert in the field—offers concise and straightforward explanations to the core principles and techniques in business statistics. The concepts are introduced through examples, and the text is designed to be accessible

Go beyond technique to master the difficult judgement calls of forecasting A variety of software can be used effectively to achieve accurate forecasting, but no software can replace the essential human component. You may be new to forecasting, or you may have mastered the statistical theory behind the software’s predictions, and even more advanced “power user” techniques for the software itself—but your forecasts will never reach peak accuracy unless you master the complex judgement calls that the software cannot make. Profit From Your Forecasting Software addresses the issues that arise regularly, and shows you how to make the correct decisions to get the most out of your software. Taking a non-mathematical approach to the various forecasting models, the discussion covers common everyday decisions such as model choice, forecast adjustment, product hierarchies, safety stock levels, model fit, testing, and much more. Clear explanations help you better understand seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared, and an exploration of psychological biases provides insight into the decision to override the software’s forecast. With a focus on choice, interpretation, and judgement, this book goes beyond the technical manuals to help you truly grasp the more intangible skills that lead to better accuracy. Explore the advantages and disadvantages of alternative forecasting methods in different situations Master the interpretation and evaluation of your software’s output Learn the subconscious biases that could affect your judgement toward intervention Find expert guidance on testing, planning, and configuration to help you get the most out of your software Relevant to sales forecasters, demand planners, and analysts across industries, Profit From Your Forecasting Software is the much sought-after “missing piece” in forecasting reference.

Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as S&OP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

Dive right into the powerful world of Microsoft Power BI with this comprehensive guide. This book takes you through every step of mastering Power BI, from data modeling to creating actionable visualizations. You'll find clear explanations and practical steps to improve your data analytics and enhance business decision-making. What this Book will help me do Learn to connect and transform data using Power Query M Language to create clean, structured datasets. Understand how to design scalable and performance-optimized Power BI Data Models for effective analytics. Develop professional, visually appealing and interactive reports and dashboards to convey insights confidently. Implement best practices for managing Power BI solutions, including deployment, version control, and monitoring. Gain practical knowledge to administer Power BI across organizational structures, ensuring security and efficiency. Author(s) None Powell is a seasoned expert in business intelligence and a passionate educator in the field of data analytics. With extensive hands-on experience in Microsoft Power BI, None has supported many organizations in unlocking the potential of their data. The approachable writing style reflects a real-world yet proficient understanding of Power BI's capabilities. Who is it for? This book is ideal for business intelligence professionals looking to deepen their expertise in Microsoft Power BI. Readers already familiar with basic BI concepts and Power BI will gain significant technical depth. It suits professionals keen to enhance their data modeling, visualization, and analytics skills. If you're aiming to create impactful dashboards and benefit from advanced insights, this book is for you.

Learn advanced techniques and improve your audio visualization skills with Thinking Machine Audio Dreams (ThMAD). With this book, you can concentrate on advanced examples and usage patterns, including using shaders in a more profound way, and how to incorporate ThMAD into a tool chain using the professional sound server JACK. Advanced Audio Visualization Using ThMAD provides advanced techniques for generating graphics, improving performance, and providing readers with the skills needed to create more interesting visualizations. You will also learn professional setups with highly developed visual and aural art tool chains. What You'll Learn Use the ThMAD software for advanced setups in their personal and professional projects Gain a pragmatic introduction to using shaders Use JACK sound servers with ThMAD Control the timing ThMAD Work withadvanced configurations Who This Book Is For Artists and developers already familiar with ThMAD and looking to enhance their projects. In addition, readers primarily interested in using shaders or the Jack audio server for graphics generation can benefit from the book as well.

"Data Analysis with R, Second Edition" is your ultimate guide to mastering data analysis in R, encompassing everything from foundational concepts to advanced techniques. You will learn to manipulate, analyze, and visualize data effectively, applying cutting-edge R packages like ggplot2 and dplyr. Through rich examples, this comprehensive book thoroughly prepares you to tackle real-world analytical challenges. What this Book will help me do Understand foundational statistical reasoning and sampling methods. Perform hypothesis testing and apply Bayesian methods to data analysis. Build and evaluate regression, classification, and time series models. Handle messy and missing data using advanced R techniques and methods. Optimize performance through parallel processing, Rcpp, and efficient data manipulation. Author(s) Chris Burnett is an experienced data analyst with over 15 years of expertise harnessing R for insights. A passionate advocate for accessible computing, Chris integrates practical exercises and rich examples to demystify complex analytical techniques. Their experience and dedication shine in this approachable yet detailed guide. Who is it for? This book is ideal for budding and professional data analysts or data scientists who wish to deepen their expertise in R. It suits learners with a basic understanding of R who aim to extend their proficiency in applied data analysis. The guide provides significant value for professionals aiming to implement effective analytical models. Readers seeking to grow within the analytics sector will find this resource indispensable.

Enhance your SAS data-wrangling skills with high-precision and parallel data manipulation using the DS2 programming language. Now in its second edition, this book addresses the DS2 programming language from SAS, which combines the precise procedural power and control of the Base SAS DATA step language with the simplicity and flexibility of SQL. DS2 provides simple, safe syntax for performing complex data transformations in parallel and enables manipulation of native database data types at full precision. It also covers PROC FEDSQL, a modernized SQL language that blends perfectly with DS2. You will learn to harness the power of parallel processing to speed up CPU-intensive computing processes in Base SAS and how to achieve even more speed by processing DS2 programs on massively parallel database systems. Techniques for leveraging internet APIs to acquire data, avoiding large data movements when working with data from disparate sources, and leveraging DS2's new data types for full-precision numeric calculations are presented, with examples of why these techniques are essential for the modern data wrangler. Here's what's new in this edition: how to significantly improve performance by using the new SAS Viya architecture with its SAS Cloud Analytic Services (CAS) how to declare private variables and methods in a package the new PROC DSTODS2 the PCRXFIND and PCRXREPLACE packages While working though the code samples provided with this book, you will build a library of custom, reusable, and easily shareable DS2 program modules, execute parallelized DATA step programs to speed up a CPU-intensive process, and conduct advanced data transformations using hash objects and matrix math operations. This book is part of the SAS Press Series.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions. Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs. Classroom-tested with exercises included Practice-oriented (taken from day-to-day statistical work of the authors) Includes further studies including design of experiments and sample sizing Presents and uses IBM SPSS Statistics 24 for practical calculations of data Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.

Relational Research and Organisation Studies does not only present and discuss guidelines for practice at a onto-epistemological level but also presents and discusses concrete cases of research projects building on relational constructionist ideas. Furthermore, excerpts of data are presented and analyzed in order to explain the co-constructed processes of the inquiries more in detail.

Mastering Qlik Sense is a comprehensive guide designed to empower you to utilize Qlik Sense for advanced data analytics and dynamic visualizations. This book provides detailed insights into creating seamless Business Intelligence solutions tailored to your needs. Whether you're building dashboards, optimizing data models, or exploring Qlik Cloud functionalities, this book has you covered. What this Book will help me do Build interactive and insightful dashboards using Qlik Sense's intuitive tools. Learn to model data efficiently and apply best practices for optimized performance. Master the Qlik Sense APIs and create advanced custom extensions. Understand enterprise security measures including role-based access controls. Gain expertise in migrating from QlikView to Qlik Sense effectively Author(s) Juan Ignacio Vitantonio is an experienced expert in Business Intelligence solutions and data analytics. With a profound understanding of Qlik technologies, Juan has developed and implemented impactful BI solutions across various industries. His writing reflects his practical knowledge and passion for empowering users with actionable insights into data. Who is it for? This book is perfect for BI professionals, data analysts, and organizations aiming to leverage Qlik Sense for advanced analytics. Ideal for those with a foundational grasp of Qlik Sense, it also provides comprehensive guidance for QlikView users transitioning to Qlik Sense. If you want to improve your BI solutions and data-driven decision-making skills, this book is for you.

Generate reports with style! The SAS Programmer's PROC REPORT Handbook: ODS Companion explains how to use style elements within a style template to customize reports generated by PROC REPORT, leading to more appealing and effective business reports. Many programmers are faced with generating reports that are easy to read and comprehend for a wide variety of audiences, which is where the ODS destinations and style changes come into play. This book teaches you how to use style elements in PROC REPORT, a versatile reporting procedure, to customize your output. Mastering style elements allows you to change visual aspects of reports, such as borders, column widths, fonts, backgrounds, and more. This companion to The SAS Programmer’s PROC REPORT Handbook: Basic to Advanced Reporting Techniques explores how the style elements within a style template affect the output generated by PROC REPORT. It provides examples of altering the style elements and the effect on the main ODS destinations, while also discussing common pitfalls that programmers can avoid while working with tables, Microsoft Excel, Microsoft Power Point, and PDF output.

Business applications are evolving and user expectations for quality, easy-to-use software is at an all-time high. The consumerization of applications is making the role of product managers more difficult than ever. How do you build products or services that meet demands for both power and simplicity? Companies are now gaining competitive advantage by providing intuitive application experiences that help users achieve goals. The best applications—the ones that stick—are those that empower users to realize the full value of their data. In this book, we explore how treating data as a feature can help product managers create indisposable applications that help users solve their most critical goals. Understand your users’ goals, the data they’ll need to achieve them, where the data comes from, and how to visualize data effectively Use personas to help you keep users in mind when making critical development and design decisions Provide an interwoven data experience by immersing reports, dashboards, and visualizations into your applications Make your data “over-the-counter” so that you and your users can accurately and easily interpret it Learn how to manage your data roadmap and handle requests for additional features

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes

Comprising six thematic sections, The Routledge Companion to Trust provides a comprehensive survey of trust research. With contributions from international experts, this volume examines the major topics and emerging areas within the field, including essays on the foundations, levels and theories of trust.

This book makes readers aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship.

This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment. R has become one of the most widely used tools for reproducible quantitative analysis, and analysts fluent in this language are in high demand. The R language, traditionally used for statistical analysis, provides a more explicit, flexible, and extensible environment than spreadsheets for conducting business case analysis. The main tutorial follows the case in which a chemical manufacturing company considers constructing a chemical reactor and production facility to bring a new compound to market. There are numerous uncertainties and risks involved, including the possibility that a competitor brings a similar product online. The company must determine the value of making the decision to move forward and where they might prioritize their attention to make a more informed and robust decision. While the example used is a chemical company, the analysis structure it presents can be applied to just about any business decision, from IT projects to new product development to commercial real estate. The supporting tutorials include the perspective of the founder of a professional service firm who wants to grow his business and a member of a strategic planning group in a biomedical device company who wants to know how much to budget in order to refine the quality of information about critical uncertainties that might affect the value of a chosen product development pathway. What You’ll Learn Set upa business case abstraction in an influence diagram to communicate the essence of the problem to other stakeholders Model the inherent uncertainties in the problem with Monte Carlo simulation using the R language Communicate the results graphically Draw appropriate insights from the results Develop creative decision strategies for thorough opportunity cost analysis Calculate the value of information on critical uncertainties between competing decision strategies to set the budget for deeper data analysis Construct appropriate information to satisfy the parameters for the Monte Carlo simulation when little or no empirical data are available Who This Book Is For Financial analysts, data practitioners, and risk/business professionals; also appropriate for graduate level finance, business, or data science students

Learn how to leverage SQL Server 2017 Machine Learning Services and the R programming language to create robust, efficient data analysis and machine learning solutions. This book provides actionable insights and practical examples to help you implement and manage database-oriented analytics and predictive modeling. What this Book will help me do Understand and use SQL Server 2017 Machine Learning Services integrated with R. Gain experience in installing, configuring, and maintaining R services in SQL Server. Create and operationalize predictive models using RevoScaleR and other R packages. Improve database solutions by incorporating advanced analytics techniques. Monitor and manage R-based services effectively for reliable production solutions. Author(s) Tomaž Kaštrun and None Koesmarno bring a wealth of expertise as practitioners and educators in data science and SQL Server technologies. They share their experience innovatively, making intricate subjects approachable. Their unified teaching method ensures readers can directly benefit from practical examples and real-world applications. Who is it for? This book is tailored for database administrators, data analysts, and data scientists eager to integrate R with SQL Server. It caters to professionals with varying levels of R experience who are looking to enhance their proficiency in database-oriented analytics. Readers will benefit most if they are motivated to design effective, data-driven solutions in SQL Server environments.

A Practical Guide for Informationists: Supporting Research and Clinical Practice guides new informationists to a successful career, giving them a pathway to this savvier, more technically advanced, domain-focused role in modern day information centers and libraries. The book's broad scope serves as an invaluable toolkit for healthcare professionals, researchers and graduate students in information management, library and information science, data management, informatics, etc. Furthermore, it is also ideal as a textbook for courses in medical reference services/medical informatics in MLIS programs. Offer examples (e.g. case studies) of ways of delivering information services to end users Includes recommendations, evidence and worksheets/take-aways/templates to be repurposed and adapted by the reader Aimed at the broad area of healthcare and research libraries