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Practical Business Analytics Using SAS

Practical Business Analytics Using SAS

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Using real-life business scenarios, Practical Business Analytics Using SAS shows SAS programmers and business people how to analyze data performance predictive modeling. This book presents the tools you need to gain insight into the data at fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.

Big data and predictive analytics have become extremely relevant to all business domains, especially for the position of CEOs. Predictive analytics makes use of the variety and the volume of big data to provide analysis and understanding of what happened in the past, and, by taking advantage of the identified schemes and trends, it uses sophisticated algorithms to predict the future as accurately as possible. It is essentially a science about understanding customer behavior when making purchases on ecommerce sites, tracking and modeling market and price dynamics, and finding the correlation between internal and external financial factors. SAS, a software package recognized as an industry leader in analytics, provides the most comprehensive, integrated and easy-to-use reporting and analysis features on big data, either on a desktop computer or on the go. Its toolboxes for business analytics and business intelligence can mine, alter, manage and retrieve data from a variety of data sources. SAS performs statistical analysis and provides a graphical point-and-click user interface for non-technical users. It also offers more advanced options through the SAS programming language. SAS offers functions for descriptive statistics, such as calculating mean, standard deviation, correlation, regression and analysis of variance, and it represents data both in tabular and graphic formats. The main features that allow SAS users to perform business analytics tasks include: reading and accessing data, combining and manipulation of data, modeling and analytics with linear regression, SAS programs for repetitive analysis, presentation of output results in proper format, as well as interpretation of results and their connection with other (external) software tools. SAS allows business users to find relationships in data, by structuring and analyzing data stored in different IT systems within their company. Importantly, SAS also combines this data with external data sources, such as social networks and/or market research tools, to derive new knowledge relevant to the company business. This edition describes practical business analytics practices using SAS. It consists of three sections: basic SAS features, advanced data manipulation functions in SAS, and practical business analytics performed by SAS. Section 1 covers different statistic functions implemented in SAS, explaining descriptive statistics, frequencies, reading and accessing data, combining and manipulation of data, and modeling and analytics with linear regression. Section 2 covers advanced data manipulation and analytics with SAS, explaining creation and recording of variables, sub-setting variables and observations, labeling data, variables and values, using Proc sort and ?By? statement, concatenating (stacking) SAS data files, etc. Section 3 covers practical business analytics case studies using SAS, describing how to get meaningful information with SAS high-performance analytics, the SAS enterprise data integration server, data entry in the SAS Strategy Management tool, building interactive, data-driven dashboard applications with the SAS Business Intelligence Dashboard, and applications of spatial data using SAS business analytics.

Jovan Pehcevski

Jovan Pehcevski

Jovan obtained his PhD in Computer Science from RMIT University in Melbourne, Australia in 2007. His research interests include big data, business intelligence and predictive analytics, data and information science, information retrieval, XML, web services and service-oriented architectures, and relational and NoSQL database systems. He has published over 30 journal and conference papers and he also serves as a journal and conference reviewer. He is currently working as a Dean and Associate Professor at European University in Skopje, Macedonia.