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Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter offers an overview of business analytics and business intelligence and outlines the similarities and differences between the two. Business analytics, types of analytics, and the analytics process are all explained. Different tools used in each type of analytics are discussed as well as their relationship and interconnection. These tools overlap in applications and a combination of these tools can be used in many situations. In comparing business analytics and business intelligence, business analytics, data, analytics, and advanced analytics all fall under the wide scope of business intelligence. Business intelligence tools include the business analytics tools but also include data mining, online analytical processing, and business performance management among other things.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter offers an overview of the text as a whole, describing business analytics and how it relates to business intelligence and statistical analysis. Data analytics, its applications, and business analytics models are all reviewed as are artificial intelligence, machine learning, and deep learning. Business analytics can be divided into descriptive analytics, predictive analytics, prescriptive analytics, and advanced analytics models. This book focuses on predictive analytics, the techniques of which use mostly statistical models and algorithms to predict future business trends. The ultimate goal of business analytics is to improve business performance by making data-driven decisions through helping businesses increase revenues and profitability, improve operational efficiency, optimize marketing campaigns and respond quickly changing market trends, improve customer service efforts, address the needs and requirements of customers, gain a competitive edge over the competition, and increase market share.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 9 provides an introduction to data mining and its applications to predictive analysis. Data mining explores patterns and relationships in the vast amounts of data that businesses collect. The information that is extracted is used to predict future business outcomes. Several data mining methodologies and tasks are described, including data preparation, knowledge representation, pattern evaluation, and descriptive and predictive data mining. Data mining techniques can also be categorized into supervised and unsupervised learning. Key methodologies of data mining include anomalies detection, association learning, classification, clustering, sequence, prediction, and time series and forecasting. The growing application areas of data mining are discussed, including applications in machine learning. The relationship between data mining and machine learning as well as different types of machine learning are also introduced.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter discusses forecasting and time series analysis, both major tools of predictive analytics. Forecasting deals with predicting future business outcomes using quantitative and qualitative methods and is used to make informed decisions. Many business planning production, operations, sales, demand, and inventory decisions are based on forecasting. Produce-to-order and produce-to-stock companies rely on forecasting for production and operations planning. Companies with good forecasting are able to balance demand and supply. The general meaning of forecasting applications and several models-including the simple moving average, weighted moving average, and exponential smoothing-are discussed as well as measuring forecast accuracy.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 7 gives an introduction to regression and correlation analysis. Regression techniques allow us to explore the relationship between different variables. Essentials to developing regression models are explained, including dependent and independent variables, the least squares method, determining the best-fitting line through the data points, calculating the slope and y-intercept of the best fitting regression line, interpreting the meaning of the regression line, and measures of association between the covariance and the correlation coefficient. Developing regression models to predict one variable using the other variable or multiple variables is also discussed. Successful implementation of regression models requires an understanding of different types of models. A lack of understanding may lead to drawing wrong conclusions and selecting alternative courses of action.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 6 explains key models used in predictive modeling. These models are the core of predictive analytics and are used to predict future business outcomes. Explanations and application areas of each tool are given. The tools described include various types of regression models, forecasting models, ANOVA, data mining, machine learning, artificial intelligence, neural networks, and deep learning.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter starts with a discussion about what predictive analytics is and how it differs from descriptive analytics. While descriptive analytics is the process of exploring and explaining data to reveal trends, predictive analytics is about predicting future business outcomes. It uses several models that can be categorized into logic-driven models and data-driven models. A number of tools and statistical models are introduced, which are considered prerequisites to predictive analytics. Descriptions and application areas of these prerequisite tools - including probability concepts, probability distributions, and correlation analysis - are given. The understanding of these prerequisite tools is critical in understanding and applying predictive analytics models.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter provides a more in-depth look at descriptive analytics. Descriptive analytics tools are used to understand the occurrence of certain business phenomena or outcomes and explaining these outcomes through graphical, quantitative, and numerical analyses. A case is given to demonstrate the tools and applications of visual techniques used in descriptive analytics. Descriptive analytics is critical in studying the current state of a business and in learning what has happened in the past using company data. Knowledge derived from descriptive analytics lays a foundation for further analysis and leads to predictive analytics.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 3 builds on chapter 2, discussing how analytics, business analytics, and data analytics all fit into business intelligence and going further into the comparison between business analytics and business intelligence. Business intelligence is a technology-driven process for processing and analyzing data to make sense from huge quantities of data that businesses collect and obtain from various sources. It is both visualization and analytics. Various functions and applications of business intelligence are described, including but not limited to reporting, online analytical processing, and business process management. Business intelligence can combine external and internal data in order to create an "intelligence" that cannot be derived by any one set of data. Business intelligence along with business analytics can empower organizations and give them a better understanding of the existing markets and customer behavior.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 1 gives an overview of the field of business analytics, which uses data, statistical analysis, mathematical and statistical modeling, data mining, and advanced analytics tools to explore, investigate, and understand business performance. The tools of business analytics give insight to and drive business planning and decisions. Descriptive analytics tools use statistical, graphical, and numerical methods to understand the occurrence of certain business phenomena; predictive analytics tools are used to predict future business phenomena; prescriptive analytics tools have applications in optimizing and automating business processes. Business analytics helps with informed decision-making in business and can be used to automate and optimize business processes. Data is an asset and can be leveraged for competitive advantage. Successful business analytics depends on data quality and skilled analysts. Business analytics is an organizational commitment to data-driven decision-making.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Chapter 1 provides an overview of business analytics and why these techniques are important.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. This chapter summarizes the preceding material, provides some insights related to implementation, and offers a brief introduction to the second volume.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. This chapter deals with the basic statistical tools of analytics. The primary objective of this chapter is to enable you to master the techniques of describing data using numerical methods, perform analyses and use these methods to compare and draw meaningful conclusions from data. All these techniques use computer software in real world.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. The previous chapters in this book presented numerous graphical displays created from both variables and attributes data that are powerful visualization tools. One of the major objectives of data visualization is to reveal the essential characteristics of data that may not be apparent otherwise. Data visualization makes complex and large data understandable. Visualization with big data is becoming a requirement because of the increase in the volume of data being collected and stored. The term data analytics is often used with big data. It is the science of analyzing vast amount of data to draw meaningful conclusions and make better business decisions. Big data presents numerous challenges in storing, analyzing, processing, and communicating the huge amount of data.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Data visualization involves presenting the data visually or graphically to detect patterns, trends, and correlations that are not usually apparent from the raw data. The trends and the patterns in the data cannot be recognized and they go undetected if not in the visual form. Data visualization is an integral part of business intelligence (BI). Most of the BI application software heavily emphasize on data visualiza­tion and have strong data visualization capabilities. One of the reasons for the popularity of visualization tools is that they are easier to use and comprehend and do not require extensive training as in the case of sta­tistical software.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Statistics is the science and art of making decision using data. Almost every field uses data and statistics to learn about systems and their processes. In fields such as business, research, health care, and engineering, a vast amount of raw data are collected and warehoused rapidly. Data and statistical tools aid in gaining skills such as: (1) Collecting, describing, analyzing, and interpreting data for intel­ligent decision-making (2) realizing that variation is an integral part of data and almost all data show variation (3) understanding the nature and pattern of variability of a phenomenon in the data, and (4) being able to measure reliability of the population parameters from which the sample data are collected to draw valid inferences.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Chapter 2 discusses analytics, business intelligence (BI) and business analytics (BA) as decision-making tools in businesses today. Although these terms are used interchangeably, there are slight differences in the terms of the tools they use. The connection between BI and BA tools combined with the applications in the computer science and information technology are critical for the success of BA in a company. This chapter discusses the broad meaning of the terms-BI, BA, data analytics (DA), computer and information technology, and how they are used in business decision making. The chapter also distinguishes between BI and BA, and the specific tools used in each case. provides an overview of business analytics and why these techniques are important.