As your business tries to make sense of today’s staggering amount of structured and unstructured data, traditional analytics will take you only so far. The key to success over the next few years will depend on augmented analytics, a method that embeds machine learning and natural language processing (NLP) in the process. This report explains how augmented analytics can help you uncover hidden insights, predict results, and even prescribe solutions. Author Alice LaPlante provides best practices for deploying augmented analytics, along with real-world case studies that show you how to take full advantage of this method. IT professionals, business managers, and CFOs will learn ways to democratize data use among business users and executives, using a self-service model. The future belongs to those who can get more from their data. This report shows you how. Get a primer on the key components and learn how they work together Delve into the benefits of—and roadblocks to—adopting augmented analytics Learn how companies use this method in marketing, sales, finance, and human resources Examine case studies of companies including Accenture and Riverbed

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure that model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, Andrew Greasley provides an in-depth discussion of Business process simulation and how it can enable business analytics How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics Industrial case studies including improving service delivery while ensuring an efficient use of staff in public sector organizations such as the police service, testing the capacity of planned production facilities in manufacturing, and ensuring on-time delivery in logistics systems State-of-the-art developments in business process simulation regarding the generation of simulation analytics using process mining and modeling people’s behavior Managers and decision makers will learn how simulation provides a faster, cheaper and less risky way of observing the future performance of a real-world system. The book will also benefit personnel already involved in simulation development by providing a business perspective on managing the process of simulation, ensuring simulation results are implemented, and that performance is improved.

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling •Understand how use ML tools in real world business problems, where causation matters more that correlation •Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will Learn Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.

Hands-On Data Analysis with Pandas provides an intensive dive into mastering the pandas library for data science and analysis using Python. Through a combination of conceptual explanations and practical demonstrations, readers will learn how to manipulate, visualize, and analyze data efficiently. What this Book will help me do Understand and apply the pandas library for efficient data manipulation. Learn to perform data wrangling tasks such as cleaning and reshaping datasets. Create effective visualizations using pandas and libraries like matplotlib and seaborn. Grasp the basics of machine learning and implement solutions with scikit-learn. Develop reusable data analysis scripts and modules in Python. Author(s) Stefanie Molin is a seasoned data scientist and software engineer with extensive experience in Python and data analytics. She specializes in leveraging the latest data science techniques to solve real-world problems. Her engaging and detailed writing draws from her practical expertise, aiming to make complex concepts accessible to all. Who is it for? This book is ideal for data analysts and aspiring data scientists who are at the beginning stages of their careers or looking to enhance their toolset with pandas and Python. It caters to Python developers eager to delve into data analysis workflows. Readers should have some programming knowledge to fully benefit from the examples and exercises.

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's Inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. We interviewed Jesse as a part of our Six Questions series. Check it out here. Quotes The most comprehensive coverage of Dask to date, with real-world examples that made a difference in my daily work. - Al Krinker, United States Patent and Trademark Office An excellent alternative to PySpark for those who are not on a cloud platform. The author introduces Dask in a way that speaks directly to an analyst. - Jeremy Loscheider, Panera Bread A greatly paced introduction to Dask with real-world datasets. - George Thomas, R&D Architecture Manhattan Associates The ultimate resource to quickly get up and running with Dask and parallel processing in Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

"Associations and Correlations: Unearth the powerful insights buried in your data" is a comprehensive guide for understanding and utilizing associations and correlations in data analysis. This book walks you through methods of classifying data, selecting appropriate statistical tests, and interpreting results effectively. By the end, you'll have mastered how to reveal data insights clearly and reliably. What this Book will help me do Identify and prepare datasets suitable for analysis with confidence. Understand and apply the principles of associations and correlations in data analytics. Use statistical tests to uncover univariate and multivariate relationships. Classify and interpret data into qualitative and quantitative segments effectively. Develop visual representations of data relationships to communicate insights clearly. Author(s) Lee Baker is an experienced statistician and data scientist with a passion for education. With years of teaching and mentoring professionals in data analysis, Lee excels in breaking down complex statistical concepts into understandable insights. Lee's approachable style aims to empower learners to harness their data's full potential. Who is it for? This book is designed for budding data analysts and data scientists, targeting those starting their journey into data analytics. It serves well as an introduction to the fundamentals of associations and correlations, making it suitable for beginners. If you seek a foundational understanding or a recap of key concepts, this book is for you.

As a discipline, data science is relatively young, but the job of managing data scientists is younger still. Many people undertake this management position without the tools, mentorship, or role models they need to do it well. This report examines the steps necessary to build, manage, sustain, and retain a growing data science team. You’ll learn how data science management is similar to but distinct from other management types. Michelangelo D’Agostino, VP of Data Science and Engineering at ShopRunner, and Katie Malone, Director of Data Science at Civis Analytics, provide concrete tips for balancing and structuring a data science team. The authors provide tips for balancing and structuring a data science team, recruiting and interviewing the best candidates, and keeping them productive and happy once they're in place. In this report, you'll: Explore data scientist archetypes, such as operations and research, that fit your organization Devise a plan to recruit, interview, and hire members for your data science team Retain your hires by providing challenging work and learning opportunities Explore Agile and OKR methodology to determine how your team will work together Provide your team with a career ladder through guidance and mentorship

"Getting Started with Tableau 2019.2" is your primer to mastering the latest version of Tableau, a leading tool for data visualization and analysis. Whether you're new to Tableau or looking to upgrade your skills, this book will guide you through both foundational and advanced features, enabling you to create impactful dashboards and visual analytics. What this Book will help me do Understand and utilize the latest features introduced in Tableau 2019.2, including natural language queries in Ask Data. Learn how to connect to diverse data sources, transform data by pivoting fields, and split columns effectively. Gain skills to design intuitive data visualizations and dashboards using various Tableau mark types and properties. Develop interactive and storytelling-based dashboards to communicate insights visually and effectively. Discover methods to securely share your analyses through Tableau Server, enhancing collaboration. Author(s) Tristan Guillevin is an experienced data visualization consultant and an expert in Tableau. Having helped several organizations adopt Tableau for business intelligence, he brings a practical and results-oriented approach to teaching. Tristan's philosophy is to make data accessible and actionable for everyone, no matter their technical background. Who is it for? This book is ideal for Tableau users and data professionals looking to enhance their skills on Tableau 2019.2. If you're passionate about uncovering insights from data but need the right tools to communicate and collaborate effectively, this book is for you. It's suited for those with some prior experience in Tableau but also offers introductory content for newcomers. Whether you're a business analyst, data enthusiast, or BI professional, this guide will build solid foundations and sharpen your Tableau expertise.

Applied Supervised Learning with R equips you with the essential knowledge and practical skills to leverage machine learning techniques for solving business problems using R. With this book, you'll gain hands-on experience in implementing various supervised learning models, assessing their performance, and selecting the best-suited method for your objectives. What this Book will help me do Gain expertise in identifying and framing business problems suitable for supervised learning. Acquire skills in data wrangling and visualization using R packages like dplyr and ggplot2. Master techniques for tuning hyperparameters to optimize machine learning models. Understand methods for feature selection and dimensionality reduction to enhance model performance. Learn how to deploy machine learning models to production environments, such as AWS Lambda. Author(s) Karthik Ramasubramanian and Jojo Moolayil are both seasoned data science practitioners and educators who bring a wealth of experience in machine learning and analytics. With a deep understanding of R and its applications in real-world scenarios, they offer practical insights and actionable examples to their readers. Their teaching style focuses on clarity and practical application. Who is it for? This book is ideal for data analysts, data scientists, and data engineers at a beginner to intermediate level who aim to master supervised machine learning with R. Readers should have basic knowledge of statistics, probabilities, and R programming. It is designed for those eager to apply machine learning techniques to real-world problems and improve their decision-making capabilities.

Dive into the intricacies of time series analysis and forecasting with R in this comprehensive guide. From foundational concepts to practical implementations, this book equips you with the tools and techniques to analyze, understand, and predict time-dependent data. What this Book will help me do Develop insights by visualizing time-series data and identifying patterns. Master statistical time-series concepts including autocorrelation and moving averages. Learn and implement forecasting models like ARIMA and exponential smoothing. Apply machine learning methodologies for advanced time-series predictions. Work with key R packages for cleaning, manipulating, and analyzing time-series data. Author(s) Rami Krispin is an accomplished statistician and R programmer with extensive experience in data analysis and time-series modeling. His hands-on approach in utilizing R packages and libraries brings clarity to complex time-series concepts. With a passion for teaching and simplifying intricate topics, Rami ensures readers both grasp the theories and apply them effectively. Who is it for? This book is ideal for data analysts, statisticians, and R developers interested in mastering time-series analysis for real-world applications. Designed for readers with a basic understanding of statistics and R programming, it offers a practical approach to learning effective forecasting and data visualization techniques. Professionals aiming to expand their skillset in predictive analytics will find it particularly beneficial.

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide,developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecksto detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today’s data Understand how popular graph algorithms work and how they’re applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark

Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Second Edition, provides readers with a thorough, practical and updated guide to NodeXL, the open-source social network analysis (SNA) plug-in for use with Excel. The book analyzes social media, provides a NodeXL tutorial, and presents network analysis case studies, all of which are revised to reflect the latest developments. Sections cover history and concepts, mapping and modeling, the detailed operation of NodeXL, and case studies, including e-mail, Twitter, Facebook, Flickr and YouTube. In addition, there are descriptions of each system and types of analysis for identifying people, documents, groups and events. This book is perfect for use as a course text in social network analysis or as a guide for practicing NodeXL users. Walks users through NodeXL while also explaining the theory and development behind each step Demonstrates how visual analytics research can be applied to SNA tools for the mass market Includes updated case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and Instagram Includes downloadable companion materials and online resources at https://www.smrfoundation.org/nodexl/teaching-with-nodexl/teaching-resources/

A four-color journey through a complete Tableau visualization Tableau is a popular data visualization tool that’s easy for individual desktop use as well as enterprise. Used by financial analysts, marketers, statisticians, business and sales leadership, and many other job roles to present data visually for easy understanding, it’s no surprise that Tableau is an essential tool in our data-driven economy. Visual Analytics with Tableau is a complete journey in Tableau visualization for a non-technical business user. You can start from zero, connect your first data, and get right into creating and publishing awesome visualizations and insightful dashboards. • Learn the different types of charts you can create • Use aggregation, calculated fields, and parameters • Create insightful maps • Share interactive dashboards Geared toward beginners looking to get their feet wet with Tableau, this book makes it easy and approachable to get started right away.

Explore the possibilities of TIBCO Spotfire with this comprehensive guide. You'll start with fundamental data visualization principles and progress to creating powerful, professional-grade analytics dashboards and applications. By following this book, you'll master both basic usage and advanced features such as predictive and spatial analytics. What this Book will help me do Understand the fundamentals of TIBCO Spotfire and its various interfaces including web and desktop clients. Utilize Spotfire's range of visualization tools to effectively analyze and present data. Develop robust analytics dashboards and applications tailored for enterprise needs. Implement advanced features like predictive analytics and location-based data representations. Learn strategies for deploying and administrating Spotfire in a scalable, enterprise-oriented environment. Author(s) The authors, None Berridge and None Phillips, bring years of experience in business intelligence and data analytics. Their practical knowledge and real-world perspective shape the book into a practical resource for learning Spotfire. Their approach ensures that concepts are clearly explained with relatable examples, improving accessibility for all readers. Who is it for? This book is intended for business intelligence professionals, data analysts, and developers who aim to enhance their analytics skills using TIBCO Spotfire. It is suitable for beginners as no prior experience with Spotfire or advanced analytics is required. Readers looking to develop enterprise-grade visualization and analytical solutions will find it valuable.

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs