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Abstract: 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) that looks into historical data to better understand business performance thereby improving performance, and creating 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 a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.

Abstract: 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) that looks into historical data to better understand business performance thereby improving performance, and creating 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 a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.

Summary: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python. Learn the basics of linear algebra, statistics, and probability--and understand how and when they're used in data science. Collect, explore, clean, munge, and manipulate data. Dive into the fundamentals of machine learning. Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering. Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.