This ebook is written with an objective of giving essential knowledge of Business Analytics in a clear cut and precise manner. The book is divided into four parts. The part I deals with introduction to Business Analytics. In this part, I dealt with basics of business analytics. Specifically, I tried to answer the following questions:
What is business analytics?
What are the types of analytics?
What is the process of business analytics?
It also discusses similarity between business analytic process (BAP) and organization decision-making process (ODMP). In this I also tried to give the meaning of frequently used terminology such as data, database, Big data and little data. The book is broadly divided into three parts. Part II deals with descriptive analytics for decision making. In this part the book not only provide conceptual clarity but also tell you how to carry out analytics using MS Excel. The various topics it includes are: Line Chart, Bar Chart, Sub-divided Bar Graph, Percentage Bar Graph, Pie Chart, Doughnut Chat, Area Chart, Stock Chart, Scatter Chart, Bubble Chart, Stem-and leaf display, Measures of Central Tendency, Measures of Dispersion and Measures of Shape, Exploratory data analysis, Five-Number summary, Box Plot, Weighted arithmetic mean, mean in case of grouped data, variance and standard deviation in grouped data, Corvariance and Correlation, and Regression.
Part III deals with Predictive analytics. The various topics it includes are: Introduction to Prediction, Regression analysis, linear trendline, Quadratic trendline, cubic trendline, exponential trend, double log model, moving average, exponential smoothing,
Part IV deals with prescriptive analytics. The ebook is written with an objective of providing prescription to business managers based on objective analysis. It rules out subjectivity in the decision making process. Topics included are linear optimization, applications of linear programming in various areas, decision analysis.
Contents
Part – I
Chapter 1:Introduction to Business Analytics
Part – II
Chapter 2: Introduction to Data Analytics
Chapter 3: Data Visualization and Representation
Chapter 4: Descriptive Analytics by Numerical Methods
Chapter 5: Measures of Shape
Chapter 6: Covariance and Correlation
Part – III
Chapter 7: Introduction to Predictive Analytics
Chapter 8: Time Series and its Components
Chapter 9: Trend Analytics
Chapter 10: Predictive Models for Stationary Time Series
Chapter 11: Simple Regression
Chapter 12: Multiple Regression
Chapter 13: Seasonal Forecasting with Dummy Regression
Chapter 14: ARIMA Modeling
Chapter 15: Markov Analysis
Chapter 16: Monte Carlo Simulation
Chapter 17: Qualitative Methods of Prediction
Part – IV
Chapter 18:Linear Programming
Chapter 19:Applications of Linear Programming
Chapter 20:Decision Analysis
What is business analytics?
What are the types of analytics?
What is the process of business analytics?
It also discusses similarity between business analytic process (BAP) and organization decision-making process (ODMP). In this I also tried to give the meaning of frequently used terminology such as data, database, Big data and little data. The book is broadly divided into three parts. Part II deals with descriptive analytics for decision making. In this part the book not only provide conceptual clarity but also tell you how to carry out analytics using MS Excel. The various topics it includes are: Line Chart, Bar Chart, Sub-divided Bar Graph, Percentage Bar Graph, Pie Chart, Doughnut Chat, Area Chart, Stock Chart, Scatter Chart, Bubble Chart, Stem-and leaf display, Measures of Central Tendency, Measures of Dispersion and Measures of Shape, Exploratory data analysis, Five-Number summary, Box Plot, Weighted arithmetic mean, mean in case of grouped data, variance and standard deviation in grouped data, Corvariance and Correlation, and Regression.
Part III deals with Predictive analytics. The various topics it includes are: Introduction to Prediction, Regression analysis, linear trendline, Quadratic trendline, cubic trendline, exponential trend, double log model, moving average, exponential smoothing,
Part IV deals with prescriptive analytics. The ebook is written with an objective of providing prescription to business managers based on objective analysis. It rules out subjectivity in the decision making process. Topics included are linear optimization, applications of linear programming in various areas, decision analysis.
Contents
Part – I
Chapter 1:Introduction to Business Analytics
Part – II
Chapter 2: Introduction to Data Analytics
Chapter 3: Data Visualization and Representation
Chapter 4: Descriptive Analytics by Numerical Methods
Chapter 5: Measures of Shape
Chapter 6: Covariance and Correlation
Part – III
Chapter 7: Introduction to Predictive Analytics
Chapter 8: Time Series and its Components
Chapter 9: Trend Analytics
Chapter 10: Predictive Models for Stationary Time Series
Chapter 11: Simple Regression
Chapter 12: Multiple Regression
Chapter 13: Seasonal Forecasting with Dummy Regression
Chapter 14: ARIMA Modeling
Chapter 15: Markov Analysis
Chapter 16: Monte Carlo Simulation
Chapter 17: Qualitative Methods of Prediction
Part – IV
Chapter 18:Linear Programming
Chapter 19:Applications of Linear Programming
Chapter 20:Decision Analysis
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