Utilize organizational data and analytics to make better decisions about managing the learning and development of your workforce.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. About the Technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the Book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's Inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the Reader For readers who want to begin or advance a data science career. About the Authors Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Quotes Full of useful advice, real-case scenarios, and contributions from professionals in the industry. - Sebastián Palma Mardones, ArchDaily The perfect companion for someone who wants to be a successful data scientist! - Gustavo Gomes, Brightcove Insightful overview of all aspects of a data science career. - Krzysztof Jędrzejewski, Pearson Highly recommended. - Hagai Luger, Clarizen

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system

Real-life examples of how to apply intelligence in the healthcare industry through innovative analytics Healthcare analytics offers intelligence for making better healthcare decisions. Identifying patterns and correlations contained in complex health data, analytics has applications in hospital management, patient records, diagnosis, operating and treatment costs, and more. Helping healthcare managers operate more efficiently and effectively. Transforming Healthcare Analytics: The Quest for Healthy Intelligence shares real-world use cases of a healthcare company that leverages people, process, and advanced analytics technology to deliver exemplary results. This book illustrates how healthcare professionals can transform the healthcare industry through analytics. Practical examples of modern techniques and technology show how unified analytics with data management can deliver insight-driven decisions. The authors—a data management and analytics specialist and a healthcare finance executive—share their unique perspectives on modernizing data and analytics platforms to alleviate the complexity of the healthcare, distributing capabilities and analytics to key stakeholders, equipping healthcare organizations with intelligence to prepare for the future, and more. This book: Explores innovative technologies to overcome data complexity in healthcare Highlights how analytics can help with healthcare market analysis to gain competitive advantage Provides strategies for building a strong foundation for healthcare intelligence Examines managing data and analytics from end-to-end, from diagnosis, to treatment, to provider payment Discusses the future of technology and focus areas in the healthcare industry Transforming Healthcare Analytics: The Quest for Healthy Intelligence is an important source of information for CFO’s, CIO, CTO, healthcare managers, data scientists, statisticians, and financial analysts at healthcare institutions.

This book presents fundamentals in MATLAB programming, including data and statement structures, control structures, function writing and bugging in MATLAB programming, followed by the presentations of algebraic computation, transcendental function evaluations and data processing. Advanced topics such as MATLAB interfacing, object-oriented programming and graphical user interface design are also addressed.

"DAX Cookbook: Over 120 recipes to enhance your business with analytics, reporting, and business intelligence" is the ultimate guidebook for mastering DAX (Data Analysis Expressions) in business intelligence, Power BI, and SQL Server Analysis Services. With hands-on examples and extensive recipes, it enables professionals to solve real-world data challenges effectively. What this Book will help me do Understand how to create tailored calculations for dates, time, and duration to enhance data insights. Develop key performance indicators (KPIs) and advanced business metrics for strategic decision-making. Master text and numerical data transformations to construct dynamic dashboards and reports. Optimize data models and DAX queries for improved performance and analytics accuracy. Learn to handle and debug calculations, and implement complex statistical and mathematical measures. Author(s) Greg Deckler is a seasoned business intelligence professional with extensive experience in using DAX and Power BI to provide actionable insights. As a recognized expert in the field, Greg brings practical knowledge of developing scalable BI solutions. His teaching approach is rooted in clarity and real-world application, making complex topics accessible to learners of all levels. Who is it for? This book is perfect for business professionals, BI developers, and data analysts with basic knowledge of the DAX language and associated tools. If you are looking to enhance your DAX skills and solve tough analytical challenges, this book is tailored for you. It's highly relevant for those aiming to optimize business intelligence workflows and improve data-driven decisions.

Grasp the essentials of JMP to generate rapid results. JMP Essentials: An Illustrated Guide for New Users, Third Edition, is designed for new or novice JMP users who need to generate meaningful analysis quickly. The book focuses on the most commonly used platforms and typical workflow of the user, from data importing, exploring, and visualizing to modeling and sharing results with others. Throughout the book, the authors emphasize results over theory, providing just the essential steps with corresponding screenshots. In most cases, each section completes a JMP task, which maximizes the book’s utility as a reference. This edition has new instructions and screenshots reflecting the features added to the latest release of JMP software, including updated sections on JMP Dashboard Builder, Query Builder, the Fit Model platform, JMP Public and JMP Live, and a more detailed look at the JMP website. Each chapter contains a family of features that are carefully crafted to first introduce you to basic features and then move on to more advanced topics. JMP Essentials: An Illustrated Guide for New Users, Third Edition, is the quickest and most accessible reference book available.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

Explore powerful SAS analytics and the Internet of Things! The world that we live in is more connected than ever before. The Internet of Things (IoT) consists of mechanical and electronic devices connected to one another and to software through the internet. Businesses can use the IoT to quickly make intelligent decisions based on massive amounts of data gathered in real time from these connected devices. IoT increases productivity, lowers operating costs, and provides insights into how businesses can serve existing markets and expand into new ones. Intelligence at the Edge: Using SAS with the Internet of Things is for anyone who wants to learn more about the rapidly changing field of IoT. Current practitioners explain how to apply SAS software and analytics to derive business value from the Internet of Things. The cornerstone of this endeavor is SAS Event Stream Processing, which enables you to process and analyze continuously flowing events in real time. With step-by-step guidance and real-world scenarios, you will learn how to apply analytics to streaming data. Each chapter explores a different aspect of IoT, including the analytics life cycle, monitoring, deployment, geofencing, machine learning, artificial intelligence, condition-based maintenance, computer vision, and edge devices.

Learn to create stunning animated and interactive charts using Highcharts and Angular. Use and build on your existing knowledge of HTML, CSS, and JavaScript to develop impressive dashboards that will work in all modern browsers. You will learn how to use Highcharts, call backend services for data, and easily construct real-time data dashboards. You'll also learn how you can club your code with jQuery and Angular. This book provides the best solutions for real-time challenges and covers a wide range of charts including line, area, maps, plot, different types of pie chart, Gauge, heat map, Histogram, stacked bar, scatter plot and 3d charts. After reading this book, you'll be able to export your charts in different formats for project-based learning. Highcharts is one the most useful products worldwide for develop charting on the web, and Angular is well known for speed. Using Highcharts with Angular, developers can build fast, interactive dashboards. Get up to speed using this book today. What You’ll Learn How to develop interactive, animated dashboards How you can implement Highcharts using Angular How to develop a real-time application with the use of WebAPI, Angular, and Highcharts How to create interactive styling themes and colors for a dashboard Who This Book Is For This book is aimed at developers, dev leads, software architects, students or enthusiasts who are already familiar with HTML, CSS, and JavaScript.

The 'Pandas 1.x Cookbook' offers a recipe-based guide for mastering the powerful Python library, pandas. You will gain practical knowledge for handling and manipulating data efficiently, from the fundamentals to advanced techniques. The book is an essential resource for exploring and analyzing datasets with pandas. What this Book will help me do Understand and apply data exploration techniques in pandas. Use pandas to manipulate, aggregate, and clean datasets to extract meaningful insights. Combine pandas with Matplotlib and Seaborn to create effective visualizations. Perform time series analysis and transform datasets for machine learning. Implement workflows for handling large-scale data that exceeds your computer's memory. Author(s) Matthew Harrison and Theodore Petrou are highly experienced educators and practitioners in data science and Python programming. With their extensive expertise in using pandas, they provide insights through practical exercises and approachable narratives. Their aim is to make complex concepts accessible to learners of varying skill levels. Who is it for? This book is ideal for Python programmers, analysts, and data scientists seeking to expand their data handling and analysis capabilities. It caters to both beginners who are new to pandas and those looking to deepen their understanding of its advanced features. If your goal is to explore, clean, and analyze complex datasets efficiently, this book is tailored for you.

Analyze company data quickly and easily using Microsoft’s powerful data tools. Learn to build scalable and robust data models, clean and combine different data sources effectively, and create compelling and professional visuals. Beginning Power BI is a hands-on, activity-based guide that takes you through the process of analyzing your data using the tools that that encompass the core of Microsoft’s self-service BI offering. Starting with Power Query, you will learn how to get data from a variety of sources, and see just how easy it is to clean and shape the data prior to importing it into a data model. Using Power BI tabular and the Data Analysis Expressions (DAX), you will learn to create robust scalable data models which will serve as the foundation of your data analysis. From there you will enter the world of compelling interactive visualizations to analyze and gain insight into your data. You will wrap up your Power BI journey by learning how to package and share your reports and dashboards with your colleagues. Author Dan Clark takes you through each topic using step-by-step activities and plenty of screen shots to help familiarize you with the tools. This third edition covers the new and evolving features in the Power BI platform and new chapters on data flows and composite models. This book is your hands-on guide to quick, reliable, and valuable data insight. What You Will Learn Simplify data discovery, association, and cleansing Build solid analytical data models Create robust interactive data presentations Combine analytical and geographic data in map-based visualizations Publish and share dashboards and reports Who This Book Is For Business analysts, database administrators, developers, and other professionals looking to better understand and communicate with data

Don't fly blind. See how the power of experiments works for you. When it comes to improving customer experiences, trying out new business models, or developing new products, even the most experienced managers often get it wrong. They discover that intuition, experience, and big data alone don't work. What does? Running disciplined business experiments. And what if companies roll out new products or introduce new customer experiences without running these experiments? They fly blind. That's what Harvard Business School professor Stefan Thomke shows in this rigorously researched and eye-opening book. It guides you through best practices in business experimentation, illustrates how these practices work at leading companies, and answers some fundamental questions: What makes a good experiment? How do you test in online and brick-and-mortar businesses? In B2B and B2C? How do you build an experimentation culture? Also, best practice means running many experiments. Indeed, some hugely successful companies, such as Amazon, Booking.com, and Microsoft, run tens of thousands of controlled experiments annually, engaging millions of users. Thomke shows us how these and many other organizations prove that experimentation provides significant competitive advantage. How can managers create this capability at their own companies? Essential is developing an experimentation organization that prizes the science of testing and puts the discipline of experimentation at the center of its innovation process. While it once took companies years to develop the tools for such large-scale experiments, advances in technology have put these tools at the fingertips of almost any business professional. By combining the power of software and the rigor of controlled experiments, today's managers can make better decisions, create magical customer experiences, and generate big financial returns. Experimentation Works is your guidebook to a truly new way of thinking and innovating.

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Learn how to easily transform your data into engaging, interactive visual reports! Data is no longer the sole domain of tech professionals and scientists. Whether in our personal, business, or community lives, data is rapidly increasing in both importance and sheer volume. The ability to visualize all kinds of data is now within reach for anyone with a computer and an internet connection. Google Data Studio, quickly becoming the most popular free tool in data visualization, offers users a flexible, powerful way to transform private and public data into interactive knowledge that can be easily shared and understood. Hands On With Google Data Studio teaches you how to visualize your data today and produce professional quality results quickly and easily. No previous experience is required to get started right away—all you need is this guide, a Gmail account, and a little curiosity to access and visualize data just like large businesses and organizations. Clear, step-by-step instructions help you identify business trends, turn budget data into a report, assess how your websites or business listings are performing, analyze public data, and much more. Practical examples and expert tips are found throughout the text to help you fully understand and apply your new knowledge to a wide array of real-world scenarios. This engaging, reader-friendly guide will enable you to: Use Google Data Studio to access various types of data, from your own personal data to public sources Build your first data set, navigate the Data Studio interface, customize reports, and share your work Learn the fundamentals of data visualization, personal data accessibility, and open data API's Harness the power of publicly accessible data services including Google’s recently released Data Set Search Add banners, logos, custom graphics, and color palettes Hands On With Google Data Studio: A Data Citizens Survival Guide is a must-have resource for anyone starting their data visualization journey, from individuals, consultants, and small business owners to large business and organization managers and leaders.

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining—including big data, classification, machine learning, and visualization—is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book’s companion website. Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems. Features two new chapters—one on Data Mining and another on Cluster Analysis Now contains R exhibits including code, graphical display, and some results MINITAB and JMP have been updated to their latest versions Emphasizes the p-value approach and includes related practical interpretations Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts Supplemented with an Instructor's-only solutions manual on a book’s companion website Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences.

Hone your SAS skills with Exercises and Projects for The Little SAS Book, Sixth Edition! Now in its sixth edition, the best-selling The Little SAS Book just keeps getting better. Readers worldwide study this easy-to-follow book to help them learn the basics of SAS programming. Rebecca Ottesen has once again teamed up with the authors of The Little SAS Book, Lora Delwiche and Susan Slaughter, to provide a way to challenge and improve your SAS skills through thought-provoking questions, exercises, and projects. Each chapter has been updated to match The Little SAS Book, Sixth Edition. The book contains a mixture of multiple-choice questions, open-ended discussion topics, and programming exercises with selected answers and hints. It also includes comprehensive programming projects that are designed to encourage self-study and to test the skills developed by The Little SAS Book. Exercises and Projects for The Little SAS Book, Sixth Edition is a hands-on workbook that is designed to improve your SAS skills whether you are a student or a professional.

The Data Science Workshop is designed for beginners looking to step into the rigorous yet rewarding world of data science. By leveraging a hands-on approach, this book demystifies key concepts and guides you gently into creating practical machine learning models with Python. What this Book will help me do Understand supervised and unsupervised learning and their applications. Gain hands-on experience with Python libraries like scikit-learn and pandas for data manipulation. Learn practical use cases of machine learning techniques such as regression and clustering. Discover techniques to ensure robustness in machine learning with hyperparameter tuning and ensembling. Develop efficiency in feature engineering with automated tools to accelerate workflows. Author(s) Anthony So None, Thomas Joseph, Robert Thas John, and Andrew Worsley are seasoned experts in data science and Python programming. Along with Dr. Samuel Asare None, they bring decades of experience and practical knowledge to this book, delivering an engaging and approachable learning experience. Who is it for? This book is targeted toward individuals who are beginners in data science and are eager to acquire foundational knowledge and practical skills. It appeals to those who prefer a structured, hands-on approach to learning, possibly having some prior programming experience or interest in Python. Professionals aspiring to pivot into data-oriented roles or students aiming to strengthen their understanding of data science concepts will find this book particularly valuable. If you're looking to gain confidence in implementing data science projects and solving real-world problems, this text is for you.

Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition is also an excellent text for students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful.

Provides practical examples of circuit design and analysis using PSpice, MATLAB, and the Smith Chart This book presents the three technologies used to deal with electronic circuits: MATLAB, PSpice, and Smith chart. It gives students, researchers, and practicing engineers the necessary design and modelling tools for validating electronic design concepts involving bipolar junction transistors (BJTs), field-effect transistors (FET), OP Amp circuits, and analog filters. Electronic Circuits with MATLAB®, PSpice®, and Smith Chart presents analytical solutions with the results of MATLAB analysis and PSpice simulation. This gives the reader information about the state of the art and confidence in the legitimacy of the solution, as long as the solutions obtained by using the two software tools agree with each other. For representative examples of impedance matching and filter design, the solution using MATLAB and Smith chart (Smith V4.1) are presented for comparison and crosscheck. This approach is expected to give the reader confidence in, and a deeper understanding of, the solution. In addition, this text: Increases the reader's understanding of the underlying processes and related equations for the design and analysis of circuits Provides a stepping stone to RF (radio frequency) circuit design by demonstrating how MATLAB can be used for the design and implementation of microstrip filters Features two chapters dedicated to the application of Smith charts and two-port network theory Electronic Circuits with MATLAB®, PSpice®, and Smith Chart will be of great benefit to practicing engineers and graduate students interested in circuit theory and RF circuits.

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!