Book Review: Managerial Analytics, An Applied Guide to Principles, Methods, Tools, and Best Practices example

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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 …

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