Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user’s guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model’s effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.

Explore financial data science using SAS. Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. This book shows readers how to implement data visualization, simulation, statistical predictive models, machine learning models, and financial optimizations using real-world examples in the SAS Analytics environment. Each chapter ends with practice exercises that include use case scenarios to allow readers to test their knowledge. Designed for university students and financial professionals interested in boosting their data science skills, Financial Data Science with SAS is an essential reference guide for understanding how data science is used in the financial services industry and for learning how to use SAS to solve complex business problems.

Customer Relationship Management (CRM) systems are a growing topic among small- and medium-sized enterprises, entrepreneurs, and solopreneurs, and it is completely clear that CRM is a tool that businesses should have in place to manage sales processes. Teams of salespeople must have a system to run their daily activities, and small businesses and solopreneurs must track their marketing effort, a functioning structure for maintaining their contacts with prospects and clients to improve the effectiveness of their sales effort. CRM, once only available to large corporations, is now powerful technology for small and medium businesses. Small and medium businesses are now able to implement CRM solutions under a more cost-effective balance as an alternative to traditional tools like Salesforce, Dynamics, or Oracle. The reason for the success is mainly the simplicity of the new tools and solutions that have been developed for the management of sales processes. This book discusses how to implement a CRM from the perspective of the businessperson—not the more typical IT consultant or the technical staff. It benefits business development, sales management, and sales process control. Small business owners must understand why and how implementing a CRM will create value for their business—how it will focus on business development, sales management, and how sales leads develop into happy customers. Small business owners must first understand what a CRM system is, how it works, what its main functions are, and how it serves to manage workflows in the company’s sales department. Generally, entrepreneurs struggle to find the time to read and study complex and fully comprehensive books. This book provides direct operational guidelines to those who need easy-to-read information about how to use CRM effectively. Business professionals must be able to set up CRM systems and avoid mistakes and wasting time. This book provides an overview of what can be done with CRM and how it happens to empower businesspeople to find new customers and win business opportunities. This book discusses the logic of CRM in sales, giving tips and explanations on why and what happens when CRM is implemented in a specific way. Essentially, this book gives the entrepreneur the know-how behind CRM in sales in general terms, supporting enhanced customer relationships.

See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.

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

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

Information Technology for Management provides students with a comprehensive understanding of the latest technological developments in IT and the critical drivers of business performance, growth, and sustainability. Integrating feedback from IT managers and practitioners from top-level organizations worldwide, the International Adaptation of this well-regarded textbook features thoroughly revised content throughout to present students with a realistic, up-to-date view of IT management in the current business environment. This text covers the latest developments in the real world of IT management with the addition of new case studies that are contemporary and more relevant to the global scenario. It offers a flexible, student-friendly presentation of the material through a pedagogy that is designed to help students easily comprehend and retain information. There is new and expanded coverage of Artificial Intelligence, Robotics, Quantum Computing, Blockchain Technology, IP Intelligence, Big Data Analytics, IT Service Management, DevOps, etc. It helps readers learn how IT is leveraged to reshape enterprises, engage and retain customers, optimize systems and processes, manage business relationships and projects, and more.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

A step-by-step guide for business leaders who need to manage successful big data projects Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You’ll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can’t control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right. Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing big data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book. Ensure the success of analytics initiatives, maximize ROI, and draw value from big data Learn to define success and failure in analytics and big data projects Set your organization up for analytics success by identifying problems that have big data solutions Bring together the people, the tools, and the strategies that are right for the job By learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.