Book Review: Managerial Analytics, An Applied Guide to Principles, Methods, Tools, and Best Practices.
KEY CONCEPTS by Chapter: Identify key concepts from each chapter and the reason it was important. May be done in a table format or written out.
Chapter
Identify key Concept
Reason it was important
Exam
Material
2 Questions
1
Analytics
Managerial Analysis
Different types of analytics (prescriptive, descriptive, and predictive) serve different purposes. The value of different types of analytics is tied to the problem that needs to be solved.
Managerial analysis helps to identify the best tools to solve the business problems.
Analytics: prescriptive, descriptive, predictive
Managerial Analysis
What is analytics and how it can be applied to solve the business problems?
What is the purpose of managerial analysis?
2
Big data
Managers have aces to a great amount of data. It is important to analyze it and use in the best possible way.
Big data (IT-centric definition, Mayer-Schonberger and Cukier definition, and popular press definition)
What is Big Data?
How does the managerial analysis use Big Data?
3
Managerial innumeracy
The filtration fallacy
The 80/20 rule
Data capture process
Managers have to make decisions supported by data and analysis.
When working with analytical techniques it is important not to overestimate the level of accuracy.
This technique helps to separate the important from trivial and save time.
Incorrect data capture process results in errors.
Managerial innumeracy
The filtration fallacy
The 80/20 Rule
Data capture
How managers can overcome managerial innumeracy?
How to analyze the data set in the most effective way?
4
Machine learning
Training data
Classification algorithms
Regression analysis
Clustering and K-means algorithm
Basics of machine learning are essential for managerial analysis.
Managers can train algorithms to make predictions.
Good tools to make better decisions.
Helps to make predictions based on the data
Unsupervised algorithms that help to find patterns.
Machine learning (supervised and unsupervised)
Training Data
Classification algorithms (classification and decision trees)
Regression analysis
How can machine learning be used to make managerial predictions?
What purposes serve different types of machine learning algorithms?
5
Descriptive analytics
Databases
Data modeling
Structured Query Language SQL
Data warehouse
Online Analytical Processing (OLAP) and Data cube
Descriptive analytics allow turning data into information and insight.
Databases help to establish relationships between different datasets.
Helps to manage large sets of data
It is an established standard for querying relational database software. Helps to analyze data in many effective ways.
Allows managers to access the data.
Allow filtering data.
Descriptive analytics
Database basics
SQL
Data warehouse (subject oriented, integrated, non-volatile, time variant)
What makes up good descriptive analytics?
How to make the analysis and management of the data more effective?
6
Predictive analytics
Forecasting with regression
A/B testing
Simulation
Help managers to make future predictions and test hypothesis.
Helps to predict the value of one variable based on the values of other variables
Tests business ideas
Emulates a real business process.
Predictive analytics
Forecasting with regression
A/B testing
Simulation
What …
