The information technology revolutions over the past six decades have been astonishing, from mainframes to personal computers to smart and connected economies. But those changes pale in comparison to what’s about to happen. By 2020, seven billion people and roughly 50 billion devices will be connected to the internet, leaving the world awash in data. How do we make sense of it all? In this insightful book, Raghunath Nambiar from Cisco examines the role of analytics in enabling digital transformation within the enterprise, including challenges associated with the explosion of data. It embraces the need for analytics at the edge of the network with a local context and analytics at the data center core with a global context. He also explores the differences between the four types of analytics—descriptive, diagnostic, predictive, and prescriptive—including the driving factors behind the need for each of them, as well as the analytical systems required to process them to produce actionable insight. Raghu then takes a deep dive into how the explosion in internet connections affects key industries, and how applied analytics will impact our future. Learn how analytics can make a difference in: Smart cities to manage energy, the environment, traffic, parking, structures, waste, safety, and crowds Smart energy to enable sustainable and efficient offerings that provide substantial benefits for both providers and customers Healthcare to address the aging population, growing shortage of physicians, and rising costs through connected health Manufacturing for producing higher quality products, creating new lines of business, reducing time-to-market, and increasing revenue growth Transportation to address the increasing demand through collaborative consumption, connected cars, and the potential for autonomous vehicles

'Modern R Programming Cookbook' provides an array of practical recipes designed to enhance your R programming skills for statistical, data science, and graphical applications. With this book, you will deepen your understanding of R's syntax and semantics and be able to solve real-world problems using R effectively. What this Book will help me do Understand and configure R's development environment, including IDE setup and library management. Master R's core data structures and best practices for efficient data manipulation. Design and implement custom R functions, handling errors and utilizing recursion effectively. Streamline data processing tasks using modern R packages like dplyr for structured workflows. Efficiently process text and interact with databases to tackle a variety of data science projects. Author(s) Jaynal Abedin is an experienced data scientist and R programmer with a strong background in statistical modeling and data analysis. Over his career, Jaynal has worked on numerous data-intensive projects, helping individuals and organizations transform data insights into actionable strategies. He brings a clear and hands-on approach to teaching and writes with the goal of empowering readers to tackle complex problems using R. Who is it for? This book is perfect for developers looking to enhance their R programming expertise, particularly if they already have a basic understanding of R's fundamentals. Whether you're working in data science, statistics, or software development, this book provides actionable strategies for leveraging R's capabilities. If you're exploring advanced R programming or aiming to upskill in data applications, this book is an excellent resource.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Provides conceptual information for the Base SAS language. Major topics include SAS keywords and naming conventions, SAS variables and expressions, error processing and debugging, SAS data sets and files, creating and customizing output, DATA step concepts and DATA step processing, reading raw data, and creating and managing SAS libraries.

Analyze your biostatistics data with JMP! Trevor Bihl's Biostatistics Using JMP: A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to solve biostatistical problems. Providing extensive breadth, from summary statistics to neural networks, this essential volume offers a comprehensive, step-by-step guide to using JMP to handle your data. The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis. Written for university students, professors, those who perform biological/biomedical experiments, laboratory managers, and research scientists, Biostatistics Using JMP provides a practical approach to using JMP to solve your biostatistical problems.

Discover how to unlock the secrets of time-series data with "Practical Time Series Analysis". With a focus on hands-on learning, this book takes you on a journey through time series data processing, visualization, and modeling. Gain the technical expertise and confidence to tackle real-world datasets using Python. What this Book will help me do Understand the fundamental principles of time series analysis and their application to real-world datasets. Learn to utilize Python for data preparation, visualization, and processing in the context of time series. Master the techniques of evaluating and addressing common challenges such as non-stationarity and autocorrelation. Apply statistical methods and machine learning models, including ARIMA and deep learning approaches, to forecasting tasks. Develop practical skills to implement and deploy end-to-end predictive models for time series data analysis. Author(s) PKS Prakash and Avishek Pal bring decades of combined experience in data science and analytics. Their meticulous approach toward simplifying complex concepts makes learning time series approachable and engaging. Drawing from their professional expertise, they incorporate extensive examples to merge theory with practice. Who is it for? This book is ideal for data scientists and engineers keen on enhancing their abilities to analyze temporal data. Prior knowledge in Python and basic statistics will help you gain the most from this book. Whether advancing your career or solving practical problems, you'll find invaluable insights here.

This comprehensive guide dives deep into the Power BI platform, teaching you how to create insightful data models, reports, and dashboards that drive business decisions. With hands-on recipes and real-world examples, this book is a practical resource for mastering the full range of Power BI's capabilities. What this Book will help me do Understand and apply data cleansing and transformation using Power BI tools. Create and utilize intuitive data models for business intelligence reporting. Leverage DAX and M languages for advanced data analysis and custom solutions. Build dynamic, user-specific dashboards and visualizations for impactful insights. Integrate Power BI with Microsoft Excel, SQL Server, and cloud services for extended functionality. Author(s) None Powell is an experienced data analyst and Microsoft BI solution architect with extensive expertise in Power BI. He has worked on numerous BI projects, providing practical solutions using Microsoft's data platform technologies. Through detailed, scenario-based writing, he shares his knowledge to help readers excel in their BI endeavors. Who is it for? This book is perfect for business intelligence professionals and analysts seeking to expand their skills in Power BI. Ideal readers may have foundational Power BI knowledge and look to master advanced techniques. If you aim to build impactful BI solutions and are motivated to handle complex data integrations, this book will be instrumental.

"Data Analysis with IBM SPSS Statistics" is a comprehensive guide designed to help you master IBM SPSS Statistics for performing robust statistical analyses. Through a practical approach, the book delves into critical techniques like data visualization, regression analysis, and hypothesis testing, enabling you to uncover patterns, make informed decisions, and enhance data interpretation. What this Book will help me do Set up and configure IBM SPSS Statistics for effective data analysis workflows. Perform data cleaning and preparation, including addressing missing data and restructuring datasets. Master statistical techniques such as ANOVA, regression analysis, and clustering to draw insights from data. Generate intuitive visualizations like charts and graphs to communicate findings effectively. Build predictive models and evaluate their effectiveness for decision-making purposes. Author(s) Ken Stehlik-Barry and Anthony Babinec are seasoned data analysts and IBM SPSS experts with extensive experience in statistical methodologies and data science. They have a knack for translating complex concepts into accessible lessons, making this book an ideal resource for learners aiming to build their SPSS aptitude. Their expertise ensures a well-rounded learning journey. Who is it for? This book is tailored for data analysts and researchers who need to analyze and interpret data effectively using IBM SPSS Statistics. Readers should have basic familiarity with statistical concepts, making it ideal for those with a foundational understanding of statistics. If you aim to grasp practical applications of SPSS for real-world data challenges, this book is for you.

If you have been frustrated by very technical statistical process control (SPC) training materials, then this is the book for you. This book focuses on how SPC works and why managers should consider using it in their operations. It provides you with a conceptual understanding of SPC so that appropriate decisions can be made about the benefits of incorporating SPC into the process management and quality improvement processes. Today there is little need to make the necessary calculations by hand, so the author utilizes Minitab and NWA Quality Analyst—two of the most popular statistical analysis software packages on the market. Links are provided to the home pages of these software packages where trial versions may be downloaded for evaluation and trial use. The book also addresses the question of why SPC should be considered for use, the process of implementing SPC, how to incorporate SPC into problem identification, problem solving, and the management and improvement of processes, products, and services.

R Data Analysis Cookbook, Second Edition, is your companion for mastering various data analysis techniques using R. Combining powerful R libraries like ggplot2, this book guides you through tasks such as data mining, visualization, and even advanced concepts like time series analysis. Whether you're cleaning and formatting data or generating actionable insights, this book offers hands-on recipes to upskill efficiently. What this Book will help me do Learn how to acquire, prepare, and visualize datasets using popular R libraries. Master exploratory data analysis concepts to discover insights in any data. Get introduced to machine learning algorithms in R such as regression and classification. Develop advanced skills like social network analysis, recommendation systems, and geospatial analysis. Learn to generate dynamic reports and interactive dashboards using tools like Shiny. Author(s) Kuntal Ganguly and Viswa Viswanathan bring a wealth of experience in data science, analytics, and R programming to this book. Their passion for teaching complex topics with clarity and practical insight shines throughout. The co-authors incorporate years of technical expertise to guide readers step-by-step in solving real-world data analysis challenges. Who is it for? This book is ideal for data scientists, analysts, or enthusiasts taking their first or next steps with R. It caters to professionals with a beginner or intermediate understanding of statistics and programming who are seeking practical examples and solutions to complex data tasks. If you want actionable knowledge to address real-world analytics challenges, this book is for you.

"Pentaho 8 Reporting for Java Developers" is your hands-on guide to mastering the Pentaho 8 reporting platform. Packed with practical examples and exercises, this book teaches you how to create highly functional, interactive reports for your data visualization needs. Updated for the latest version of Pentaho, it provides all the tools and techniques you need to succeed. What this Book will help me do Learn the fundamental concepts of Pentaho Reporting including setup and initial configurations. Design and customize attractive, functional reports utilizing various data sources. Integrate Pentaho reports seamlessly into Java applications with full control over their interactions and design. Explore advanced reporting features like parameterization, localization, and complex layout configurations. Incorporate Pentaho reports into the broader Pentaho suite, including the BA platform and Data Integration tools. Author(s) Jasmine Kaur and None Corti bring their extensive expertise in information technology and Java development to this comprehensive guide. With years of hands-on experience working with Pentaho Reporting tools, they have a deep understanding of the challenges and solutions in report design. Their approachable writing style and emphasis on practical examples make learning intuitive and enjoyable. Who is it for? This book is ideally suited for Information Technologists who are familiar with databases and intermediate-level Java Developers looking to integrate advanced reporting functionalities into their projects. If you are eager to build pixel-perfect, professional reports or need insights into embedding reporting tools into Java applications, this book holds the answers.

Learn to write SAS programs quickly and efficiently.

Programming in SAS is flexible, but it can also be overwhelming. Many novice and experienced programmers learn how to write programs that use the DATA step and macros, but they often don’t realize that a simpler or better way can achieve the same results. In a user-friendly tutorial style, Practical and Efficient SAS® Programming: The Insider's Guide provides general SAS programming tips that use the tools available in Base SAS, including the DATA step, the SAS macro facility, and SQL.

Drawing from the author’s 30 years of SAS programming experience, this book offers self-contained sections that describe each tip or trick and present numerous examples. It therefore serves as both an easy reference for a specific question, and a useful cover-to-cover read. As a bonus, the utility programs included in the appendixes will help you simplify your programs, as well as help you develop a sleek and efficient coding style.

With this book, you will learn how to do the following:

use the DATA step, the SAS macro facility, SQL, and other Base SAS tools more efficiently

choose the best tool for a task

use lookup tables

simulate recursion with macros

read metadata with the DATA step

create your own programming style in order to write programs that are easily maintained

Using this book, SAS programmers of all levels will discover new techniques to help them write programs quickly and efficiently.

Microsoft Excel remains the leading spreadsheet application on the market; nearly every SAS user will need to move their data and reports into Excel workbooks at some point during their career. Exchanging Data From SAS(R) to Excel: The ODS Excel Destination shows SAS users how to create Excel workbooks that are presentation ready, eliminating manual changes to the workbooks after creation.

While the original book Exchanging Data between SAS and Microsoft Excel: Tips and Techniques to Transfer and Manage Data More Efficiently touched upon many topics involved in moving data between SAS and Excel, this companion book delves into the options that are available with the ODS Excel destination. This book also has numerous examples that include syntax and graphical output.

With this book, you can learn how to:

Create native Excel files

Insert graphs and images into Excel files

Place multiple tables on multiple tabs within the file

Customize spreadsheets with workbook-level options, print features, column features, row features, and cell-level features

Exchanging Data from SAS® to Excel: The ODS Excel Destination will make sending your output and graphics to Excel a breeze, enhancing any presentation!

Infonomics is the theory, study, and discipline of asserting economic significance to information. It strives to apply economic and asset management principles to the valuation, handling, and deployment of information assets. This for the chief data officers and other leaders in their struggle to help their organizations become infosavvy. "Doug Laney masterfully weaves together a collection of great examples with a solid framework to guide readers on how to gain competitive advantage through what he labels "the unruly asset" – data. The framework is comprehensive, the advice practical and the success stories global and across industries and applications." Liz Rowe, Chief Data Officer, State of New Jersey "A must read for anybody who wants to survive in a data centric world." Shaun Adams, Head of Data Science, Betterbathrooms.com "Phenomenal! An absolute must read for data practitioners, business leaders and technology strategists. Doug's lucid style has a set a new standard in providing intelligible material in the field of information economics. His passion and knowledge on the subject exudes thru his literature and inspires individuals like me." Ruchi Rajasekhar, Principal Data Architect, MISO Energy "I highly recommend Infonomics to all aspiring analytics leaders. Doug Laney’s work gives readers a deeper understanding of how and why information should be monetized and managed as an enterprise asset. Laney’s assertion that accounting should recognize information as a capital asset is quite convincing and one I agree with. Infonomics enjoyably echoes that sentiment!" Matt Green, independent business analytics consultant, Atlanta area "If you care about the digital economy, and you should, read this book." Tanya Shuckhart, Analyst Relations Lead, IRI Worldwide

This book, 'Statistical Application Development with R and Python', is your gateway to mastering statistical analysis and applying it effectively in real-world contexts. Through integrated R and Python code, you'll learn how to utilize data processing, explore advanced statistical models like regression and CART, and develop applications that solve complex analytical challenges. What this Book will help me do Fully understand data visualization and exploratory analysis methods to uncover insights from datasets. Master techniques such as regression models, clustering, and classification to enhance your analytical toolkit. Gain proficiency in R and Python for data processing and statistical modeling tasks. Apply CART and other machine learning tools to tackle nonlinear data challenges effectively. Equip yourself with a comprehensive approach to data exploration and decision-making for impactful results. Author(s) The author(s) of this book bring extensive experience in statistical analysis, computational modeling, and the use of R and Python for data science. They are professionals and educators passionate about making statistics accessible and practical. Their engaging writing style ensures readers not only understand but also enjoy the journey of learning statistics. Who is it for? This book is perfect for aspiring data scientists or professionals wanting to deepen their understanding of statistical analysis. Whether you're new to R or Python or looking to integrate both into your workflow, this guide provides comprehensive knowledge and practical techniques. It's suitable for beginners with no prior experience as well as seasoned users seeking to enhance their data processing and modeling skills.

The New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include: Data scientists and what they do Big data and the changes it has wrought Hadoop and other open-source software for managing and analyzing data Data products—new products and services based on data and analytics Machine learning and other AI technologies The Internet of Things and its implications New computing architectures, including cloud computing Embedding analytics within operational systems Visual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data.

Learn the art of creating machine learning models and processing data efficiently with MATLAB. In this book, you will explore various techniques such as regression analysis, clustering, classification, and neural networks, all in the MATLAB environment. Each topic is detailed with practical examples for clear understanding and immediate application. What this Book will help me do Understand the key concepts of machine learning and how they integrate with MATLAB. Learn to preprocess and transform data for effective machine learning workflows. Explore regression methods and apply them to analyze and predict trends in your data. Master classification and clustering techniques for model creation and data categorization. Gain expertise in using MATLAB Neural Network Toolbox for building neural network-based solutions. Author(s) None Kolluru and Giuseppe Ciaburro are seasoned experts in using MATLAB for data analysis and machine learning. With years of experience in research and teaching, they have meticulously curated this book to bridge concepts of theory with real-world applications. Their writing approach is clear, instructional, and focused on equipping learners with practical skills. Who is it for? This book is ideal for data analysts, aspiring data scientists, and students eager to delve into machine learning using MATLAB. Even if you're new to the field, you'll find the instructions gentle yet comprehensive to help you follow along. However, having some background in math and statistics will definitely enhance your learning experience. If you're passionate about data and its insights, this is the guide for you.

"Matplotlib 2.x By Example" is your comprehensive guide to mastering data visualization in Python using the Matplotlib library. Through detailed explanations and hands-on examples, this book will teach you how to create stunning, insightful, and professional-looking visual representations of your data. You'll learn valuable skills tailored towards practical applications in science, marketing, and data analysis. What this Book will help me do Understand the core features of Matplotlib and how to use them effectively. Create professional 2D and 3D visualizations, such as scatter plots, line graphs, and more. Develop skills to transform raw data into meaningful insights through visualization. Enhance your data visualizations with interactive elements and animations. Leverage additional libraries such as Seaborn and Pandas to expand functionality. Author(s) Allen Yu, Claire Chung, and Aldrin Yim are seasoned data scientists and technical authors with extensive experience in Python and data visualization. Allen and his coauthors are dedicated to helping readers bridge the gap between their raw data and meaningful insights through visualization. With practical applications and real-world examples, their approachable writing makes complex libraries like Matplotlib accessible and production-ready. Who is it for? This book is perfect for data enthusiasts, analysts, and Python programmers looking to enhance their data visualization skills. Whether you're a professional aiming to create high-quality visual reports or a student eager to understand and present data effectively, this book provides practical and actionable insights. Basic Python knowledge is expected, while all Matplotlib-related aspects are thoroughly explained.

In "Advanced Analytics with R and Tableau," you will learn how to combine the statistical computing power of R with the excellent data visualization capabilities of Tableau to perform advanced analysis and present your findings effectively. This book guides you through practical examples to understand topics such as classification, clustering, and predictive analytics while creating compelling visual dashboards. What this Book will help me do Integrate advanced statistical computations in R with Tableau's visual analysis for comprehensive analytics. Master making R function calls from Tableau through practical applications such as RServe integration. Develop predictive and classification models in R, visualized wonderfully in Tableau dashboards. Understand clustering and unsupervised learning concepts, applied to real-world datasets for business insights. Leverage the combination of Tableau and R for making impactful, data-driven decisions in your organization. Author(s) Ruben Oliva Ramos, Jen Stirrup, and Roberto Rösler are accomplished professionals with extensive experience in data science and analytics. Their combined expertise brings practical insights into combining R and Tableau for advanced analytics. Advocates for hands-on learning, they emphasize clarity and actionable knowledge in their writing. Who is it for? "Advanced Analytics with R and Tableau" is ideal for business analysts, data scientists, and Tableau professionals eager to expand their capabilities into advanced analytics. Readers should be familiar with Tableau and have basic knowledge of R, though the book starts with accessible examples. If you're looking to enhance your analytics with R's statistical power seamlessly integrated into Tableau, this book is for you.

This comprehensive guide dives into predictive analytics with R, exploring the powerful functionality and vast ecosystem of packages available in this programming language. By studying this book, you will gain mastery over predictive modeling techniques and learn how to apply machine learning to real-world problems efficiently and effectively. What this Book will help me do Develop proficiency in predictive modeling processes, from data preparation to model evaluation. Gain hands-on experience with R's diverse packages for machine learning. Understand the theoretical foundations and practical applications of various predictive models. Learn advanced techniques such as deep learning implementations of word embeddings and recurrent neural networks. Acquire the ability to handle large datasets using R for scalable predictive analytics workflows. Author(s) James D. Miller and Rui Miguel Forte are experts in data science and predictive analytics with decades of combined experience in the field. They bring practical insights from their work in both academia and industry. Their clear and engaging writing style aims at making complex concepts accessible to readers by integrating theoretical knowledge with real-world applications. Who is it for? This book is ideal for budding data scientists, predictive modelers, or quantitative analysts with some basic knowledge of R and statistics. Advanced learners aiming to refine their expertise in predictive analytics and those wishing to explore the functionality of R for applied machine learning will also greatly benefit from this resource. The book is suitable for professionals and enthusiasts keen to expand their understanding of predictive modeling and learn advanced techniques.