Find your dream job with this handy and informative reference guide, packed with accessible advice on over 300 positions, including details on entry routes, qualifications, salary expectations and useful contacts.

Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data. Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed. This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected. What You Will Learn Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data Who This Book Is For Researchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.

Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks. The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS Studio.You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics. What You Will Learn Become familiar with SAS Studio IDE Understand essential visualizations Know the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Write programs in SAS Get introduced to SAS-Viya, which is more potent than SAS studio Who This Book Is For A general audience of people who are new to data science, students, and data analysts and scientists who are experiencedbut new to SAS. No programming or in-depth statistics knowledge is needed.

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don’t. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to: Develop complex, testable theories for understanding individual and social behavior in web products Think like a social scientist and contextualize individual behavior in today’s social environments Build more effective metrics and KPIs for any web product or system Conduct more informative and actionable A/B tests Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation Alter user behavior in a complex web product Understand how relevant human behaviors develop, and the prerequisites for changing them Choose the right statistical techniques for common tasks such as multistate and uplift modeling Use advanced statistical techniques to model multidimensional systems Do all of this in R (with sample code available in a separate code manual)

Metabase Up and Running is your go-to guide for mastering Metabase, the open-source business intelligence tool. You'll progress from the basics of installation and setup to connecting data sources and creating insightful visualizations and dashboards. By the end, you'll be confident in implementing Metabase in your organization for impactful decision-making. What this Book will help me do Understand how to securely deploy and configure Metabase on Amazon Web Services. Master the creation of dashboards, reports, and visual visualizations using Metabase's tools. Gain expertise in user and permissions management within Metabase. Learn to use Metabase's SQL console for advanced database interactions. Acquire skills to embed Metabase within applications and automate reports via email or Slack. Author(s) None Abraham, an experienced tool specialist, is passionate about teaching others how to leverage data tools effectively. With a background in business analytics, Abraham has guided companies of all sizes. Their approachable writing style ensures a learning journey that is both informative and engaging. Who is it for? This book is ideal for business analysts and data professionals looking to amplify their business intelligence capabilities using Metabase. Readers should have some understanding of data analytics principles. Whether you're starting in analytics or seeking advanced automation, this book offers valuable guidance to meet your goals.

O ffers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

For self-service data preparation, Tableau Prep is relatively easy to use—as long as you know how to clean and organize your datasets. Carl Allchin, from The Information Lab in London, gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets. Based on Allchin’s popular blog, Preppin’ Data, this practical guide takes you step-by-step through Tableau Prep’s fundamentals. Self-service data preparation reduces the time it takes to complete data projects and improves the quality of your analyses. Discover how Tableau Prep helps you access your data and turn it into valuable information. Know what to look for when you prepare data Learn which Tableau Prep functions to use when working with data fields Analyze the shape and profile of your dataset Output data for analysis and learn how Tableau Prep automates your workflow Learn how to clean your dataset using Tableau Prep functions Explore ways to use Tableau Prep techniques in real-world scenarios Make your data available to others by managing and documenting the output

This comprehensive guide dives into the intricacies of designing enterprise-grade solutions using Microsoft Power Platform. You'll learn how to leverage the tools in the suite, such as Power Apps, Power Automate, and Power BI, alongside Microsoft 365 and Azure services. By exploring practical examples and methodologies, you will be equipped to handle complex requirements and business challenges. What this Book will help me do Master the integration of Microsoft 365 and Azure components to enhance Power Platform applications. Learn advanced security implementations to safeguard enterprise architecture solutions. Gain proficiency in data migration strategies for robust and seamless data handling across platforms. Understand application lifecycle management and best practices for enterprise-grade Power Platform solutions. Extend and customize Power Platform tools for tailored business applications. Author(s) Robert Rybaric is an experienced enterprise architect specializing in Microsoft technologies. With years of experience crafting complex architecture solutions for dynamic business environments, Robert brings insights that simplify and clarify the challenges of enterprise architecture. His hands-on experience and practical approach make him a trusted voice in the IT solutions space. Who is it for? This book is ideal for enterprise architects and IT decision-makers seeking to use Microsoft Power Platform to address complex business needs. It caters to professionals with a foundational understanding of the platform, aiming to enhance their ability to implement scalable, efficient solutions in competitive business environments.

Before the onset of COVID-19, the healthcare community was already moving to meet the challenges of a growing global population. By collecting record amounts of clinical data electronically and making significant progress on neural network-based AI approaches, the industry now has the potential to build powerful predictive analytics systems. The focus will accelerate the shift from a one-size-fits-all approach to individualized medicine. But several questions remain. What are the plausible outcomes for the world of predictive analytics in both the short and long term? What does the care pathway look like if everything is predicted? And with patient populations and healthcare needs increasing exponentially, how can the industry deliver care in a sustainable and cost-effective way? This comprehensive report, written by Jaquie Finn and Dr. Gavin Troughton with Cambridge Consultants, explores the possibilities. You’ll learn: How predictive analytics plays a part across all stages of the care pathway The foundational enablers for predictive analytics How healthcare economics figure into the equation Predictive analytics and today’s healthcare system The future of predictive analytics in healthcare

Discover how Microsoft Power Automate can transform business processes by enabling you to automate workflows with minimal coding. This book introduces the core concepts and practical applications of workflow automation using Power Automate, making it an essential guide for enhancing productivity and efficiency in digital processes. What this Book will help me do Gain foundational knowledge of Microsoft Power Automate and how its components work together. Build automation flows that integrate with popular Microsoft 365 and third-party applications. Create efficient workflows like automated email processes and file management systems. Learn to enhance workflows with features like approvals, conditions, and triggers. Understand the basics of robotic process automation and artificial intelligence applications. Author(s) Aaron Guilmette leverages his extensive experience with Microsoft 365 to provide practical, user-friendly guides to the platform's tools. His expertise in workflow optimization ensures that readers can efficiently implement the concepts discussed. Aaron's teaching style emphasizes clarity and accessibility, making complex topics understandable for readers of all levels. Who is it for? This book is for technologists, system administrators, and power users eager to learn Microsoft Power Automate from scratch or enhance their workflow capabilities. It is ideal for those familiar with Microsoft 365 looking to streamline business processes. The book aims to help readers achieve automation goals, from simple workflows to more complex solutions.

Inform your own analyses by seeing how one of the best data analysts in the world approaches analytics problems Analytics Stories: How to Make Good Things Happen is a thoughtful, incisive, and entertaining exploration of the application of analytics to real-world problems and situations. Covering fields as diverse as sports, finance, politics, healthcare, and business, Analytics Stories bridges the gap between the oft inscrutable world of data analytics and the concrete problems it solves. Distinguished professor and author Wayne L. Winston answers questions like: Was Liverpool over Barcelona the greatest upset in sports history? Was Derek Jeter a great infielder What's wrong with the NFL QB rating? How did Madoff keep his fund going? Does a mutual fund’s past performance predict future performance? What caused the Crash of 2008? Can we predict where crimes are likely to occur? Is the lot of the American worker improving? How can analytics save the US Republic? The birth of evidence-based medicine: How did James Lind know citrus fruits cured scurvy? How can I objectively compare hospitals? How can we predict heart attacks in real time? How does a retail store know if you're pregnant? How can I use A/B testing to improve sales from my website? How can analytics help me write a hit song? Perfect for anyone with the word “analyst” in their job title, Analytics Stories illuminates the process of applying analytic principles to practical problems and highlights the potential pitfalls that await careless analysts.

Apply statistics in business to achieve performance improvement Statistical Thinking: Improving Business Performance, 3rd Edition helps managers understand the role of statistics in implementing business improvements. It guides professionals who are learning statistics in order to improve performance in business and industry. It also helps graduate and undergraduate students understand the strategic value of data and statistics in arriving at real business solutions. Instruction in the book is based on principles of effective learning, established by educational and behavioral research. The authors cover both practical examples and underlying theory, both the big picture and necessary details. Readers gain a conceptual understanding and the ability to perform actionable analyses. They are introduced to data skills to improve business processes, including collecting the appropriate data, identifying existing data limitations, and analyzing data graphically. The authors also provide an in-depth look at JMP software, including its purpose, capabilities, and techniques for use. Updates to this edition include: A new chapter on data, assessing data pedigree (quality), and acquisition tools Discussion of the relationship between statistical thinking and data science Explanation of the proper role and interpretation of p-values (understanding of the dangers of “p-hacking”) Differentiation between practical and statistical significance Introduction of the emerging discipline of statistical engineering Explanation of the proper role of subject matter theory in order to identify causal relationships A holistic framework for variation that includes outliers, in addition to systematic and random variation Revised chapters based on significant teaching experience Content enhancements based on student input This book helps readers understand the role of statistics in business before they embark on learning statistical techniques.

Get a quick start to learn, understand, and apply GNU Octave using a math- and programming-friendly approach. This book focuses on an end-to-end track to teach mathematical programming, data science, signal processing, and image processing with GNU Octave. GNU Octave by Example starts with an introduction to GNU Octave, a free and open-source alternative to MATLAB. Next, it explains the processes to install GNU Octave on popular operating systems such as Windows, Ubuntu, Raspberry Pi, and other platforms. Further, it covers hands-on exercises with GNU Octave exploring the basic functionality and command line in interactive mode. This is followed by covering matrices and various operations including how to read and analyze data from various sources. Moving forward, it introduces commonly used programming constructs in data visualization. It explains 2D and 3D data visualization along with data analysis. It also demonstrates the concepts related to geometry and its application with GNU Octave. It concludes with coverage of signal processing followed by image, video, and audio processing techniques. After reading this book, you will be able to write your own programs for scientific and numerical applications. What You Will Learn ● Understand the practical aspects of GNU Octave with math and programming-friendly abstractions ● Install GNU Octave on multiple platforms including Windows, Raspberry Pi, and Ubuntu ● Work with GNU Octave using the GUI, the command line, and Jupyter notebooks ● Implement 2D and 3D data visualization and analysis with GNU Octave Who This Book Is For Software engineers, data engineers, data science enthusiasts, and computer vision professionals.

This book explains how you can enrich the data you have loaded into Power BI Desktop by accessing a suite of Artificial Intelligence (AI) features. These AI features are built into Power BI Desktop and help you to gain new insights from existing data. Some of the features are automated and are available to you at the click of a button or through writing Data Analysis Expressions (DAX). Other features are available through writing code in either the R, Python, or M languages. This book opens up the entire suite of AI features to you with clear examples showing when they are best applied and how to invoke them on your own datasets. No matter if you are a business user, analyst, or data scientist – Power BI has AI capabilities tailored to you. This book helps you learn what types of insights Power BI is capable of delivering automatically. You will learn how to integrate and leverage the use of the R and Python languages for statistics, how to integrate with Cognitive Services andAzure Machine Learning Services when loading data, how to explore your data by asking questions in plain English ... and more! There are AI features for discovering your data, characterizing unexplored datasets, and building what-if scenarios. There’s much to like and learn from this book whether you are a newcomer to Power BI or a seasoned user. Power BI Desktop is a freely available tool for visualization and analysis. This book helps you to get the most from that tool by exploiting some of its latest and most advanced features. What You Will Learn Ask questions in natural language and get answers from your data Let Power BI explain why a certain data point differs from the rest Have Power BI show key influencers over categories of data Access artificial intelligence features available in the Azure cloud Walk the same drill down path in different parts of your hierarchy Load visualizations to add smartness to your reports Simulate changes in data and immediately see the consequences Know your data, even before you build your first report Create new columns by giving examples of the data that you need Transform and visualize your data with the help of R and Python scripts Who This Book Is For For the enthusiastic Power BI user who wants to apply state-of-the-art artificial intelligence (AI) features to gain new insights from existing data. For end-users and IT professionals who are not shy of jumping into a new world of machine learning and are ready to make that step and take a deeper look into their data. For those wanting to step up their game from doing simple reporting and visualizations by making the move into diagnostic and predictive analysis.

Understand the basics of data - how to collect, analyse, present and apply it - and give your career a valuable, cutting-edge boost with this engaging and easy-to-read beginner's guide.

The fun and friendly guide to mastering IBM’s Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis. Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You’ll even dabble in programming as you expand SPSS functionality to suit your specific needs. Master the fundamental mechanics of SPSS Learn how to get data into and out of the program Graph and analyze your data more accurately and efficiently Program SPSS with Command Syntax Get ready to start handling data like a pro—with step-by-step instruction and expert advice!

"Learning Tableau 2020" is a comprehensive resource designed to strengthen your understanding of Tableau. It takes you from mastering the fundamentals to achieving proficiency in advanced visualization and data handling techniques. Through this book, you will gain the ability to create impactful data visualizations and interactive dashboards, effectively leveraging the capabilities of Tableau 2020. What this Book will help me do Effectively utilize Tableau 2020 features to develop data visualizations and dashboards. Apply advanced Tableau techniques, such as LOD and table calculations, to solve complex data analysis problems. Clean and structure data using Tableau Prep, enhancing data quality and reliability. Incorporate mapping and geospatial visualization for geographic data insights. Master storytelling with data by constructing engaging and interactive dashboards. Author(s) Joshua N. Milligan, the author of "Learning Tableau 2020," is an experienced Tableau training consultant and professional. With extensive years in the data visualization and analytics field, Joshua brings a practical perspective to the book. He excels at breaking down complex topics into accessible learning paths, making advanced Tableau concepts approachable for learners of all levels. Who is it for? This book is perfect for aspiring data analysts, IT professionals, and data enthusiasts who aim to understand and create compelling business intelligence reports. Beginners in Tableau will find the learning process straightforward due to its structured and incremental lessons. Advanced users can refine their skills with the wide range of complex examples covered. A basic familiarity with working with data is beneficial, though not required.

The Data Science Workshop provides a comprehensive introduction to building real-world data science projects. Through a hands-on approach, you will learn how to analyze data, build machine learning models, and deploy them effectively in various scenarios. This book is designed to equip you with the skills to confidently tackle data science challenges. What this Book will help me do Understand the differences between supervised and unsupervised learning to select the appropriate technique. Master data manipulation and analysis using popular Python libraries like pandas and scikit-learn. Develop skills in regression, classification, and clustering to solve diverse data science problems. Learn advanced methods to improve model accuracy, including hyperparameter tuning and feature engineering. Implement and deploy machine learning models efficiently in production workflows. Author(s) The authors of The Data Science Workshop are experienced professionals and educators in the field of data science and machine learning. They have extensive expertise in using practical methods to solve data challenges and have a passion for teaching others through engaging and clear instructional material. Who is it for? This book is ideal for aspiring data analysts, data scientists, and business analysts who wish to build foundational skills in data science. It caters to those new to the field and professionals transitioning to a data-centric role, providing practical knowledge without requiring an advanced mathematical background. Familiarity with Python is recommended.

Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands-On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers

In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg

What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Gain Powerful Insights with SAS Viya! Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.

How the data revolution is transforming biotech and health care, especially in the wake of COVID-19—and why you can’t afford to let it pass you by We are living through a time when the digitization of health and medicine is becoming a reality, with new abilities to improve outcomes for patients as well as the efficiency and success of the organizations that serve them. In The Patient Equation, Glen de Vries presents the history and current state of life sciences and health care as well as crucial insights and strategies to help scientists, physicians, executives, and patients survive and thrive, with an eye toward how COVID-19 has accelerated the need for change. One of the biggest challenges facing biotech, pharma, and medical device companies today is how to integrate new knowledge, new data, and new technologies to get the right treatments to the right patients at precisely the right times—made even more profound in the midst of a pandemic and in the years to come. Drawing on the fascinating stories of businesses and individuals that are already making inroads—from a fertility-tracking bracelet changing the game for couples looking to get pregnant, to an entrepreneur reinventing the treatment of diabetes, to Medidata's own work bringing clinical trials into the 21st century—de Vries shares the breakthroughs, approaches, and practical business techniques that will allow companies to stay ahead of the curve and deliver solutions faster, cheaper, and more successfully—while still upholding the principles of traditional therapeutic medicine and reflecting the current environment. How new approaches to cancer and rare diseases are leading the way toward precision medicine What data and digital technologies enable in the building of robust, effective disease management platforms Why value-based reimbursement is changing the business of life sciences How the right alignment of incentives will improve outcomes at every stage of the patient journey Whether you're a scientist, physician, or executive, you can't afford to let the moment pass: understand the landscape with this must-read roadmap for success—and see how you can change health care for the better.

The Data Analysis Workshop teaches you how to analyze and interpret data to solve real-world business problems effectively. By working through practical examples and datasets, you'll gain actionable insights into modern analytic techniques and build your confidence as a data analyst. What this Book will help me do Understand and apply fundamental data analysis concepts and techniques to tackle diverse datasets. Perform rigorous hypothesis testing and analyze group differences within data sets. Create informative data visualizations using Python libraries like Matplotlib and Seaborn. Understand and use correlation metrics to identify relationships between variables. Leverage advanced data manipulation techniques to uncover hidden patterns in complex datasets. Author(s) The authors, Gururajan Govindan, Shubhangi Hora, and Konstantin Palagachev, are experts in data science and analytics with years of experience in industry and academia. Their background includes performing business-critical analysis for companies and teaching students how to approach data-driven decision-making. They bring their depth of knowledge and engaging teaching styles together in this approachable guide. Who is it for? This book is intended for programmers with proficiency in Python who want to apply their skills to the field of data analysis. Readers who have a foundational understanding of coding and are eager to implement hands-on data science techniques will gain the most value. The content is also suitable for anyone pursuing a data-driven problem-solving mindset. This is an excellent resource to help transition from basic coding proficiency to applying Python in real-world data science.

The Data Wrangling Workshop is your beginner's guide to the essential techniques and practices of data manipulation using Python. Throughout the book, you will progressively build your skills, learning key concepts such as extracting, cleaning, and transforming data into actionable insights. By the end, you'll be confident in handling various data wrangling tasks efficiently. What this Book will help me do Understand and apply the fundamentals of data wrangling using Python. Combine and aggregate data from diverse sources like web data, SQL databases, and spreadsheets. Use descriptive statistics and plotting to examine dataset properties. Handle missing or incorrect data effectively to maintain data quality. Gain hands-on experience with Python's powerful data science libraries like Pandas, NumPy, and Matplotlib. Author(s) Brian Lipp, None Roychowdhury, and Dr. Tirthajyoti Sarkar are experienced educators and professionals in the fields of data science and engineering. Their collective expertise spans years of teaching and working with data technologies. They aim to make data wrangling accessible and comprehensible, focusing on practical examples to equip learners with real-world skills. Who is it for? The Data Wrangling Workshop is ideal for developers, data analysts, and business analysts aiming to become data scientists or analytics experts. If you're just getting started with Python, you will find this book guiding you step-by-step. A basic understanding of Python programming, as well as relational databases and SQL, is recommended for smooth learning.