Template For The Book Review: Managerial Analytics, An Applied Guide to Principles, Methods, Tools, and Best Practices.
Tasks Template:
Identify key Concept
Reason it was important
Exam
Material
2 Questions
1
Definition of the term “analytics”
Types of analytics: prescriptive, descriptive, predictive
Managerial Analysis
The term “analytics” has different meaning across contexts
To choose the right type of analytics
Managerial analysis different from other types of analysis.
Definition of the term “analytics”
Types of analytics: prescriptive, descriptive, predictive
Managerial Analysis
What is the confusion associated with application of the word “analytics”?
In what way prescriptive, descriptive and predictive differ?
What are the examples of prescriptive, descriptive and predictive analytics?
What is the competitive advantage of different analytics types?
What type of analytics apply?
What is the purpose of managerial analytics?
How is managerial analytics connected with prescriptive, descriptive and predictive analytics?
2
Data
Big data
Allows to understand what is meant by data and in what domains it can be encountered
Enables to understand what is meant by big data and how to work with it, what scientific methods can be applied to process the data.
Data
Big data
What is the problem associated with vast data?
What are the sources of data?
What is the first definition of big data?
What are the three Vs?
What is the second definition of big data?
What is the third definition of big data?
What is the relation between big data and science?
How to do analytics without big data?
How to apply prescriptive, descriptive and predictive analytics on big data?
In what way testing hypotheses enable to work with big data?
3
Managerial Innumeracy
Illusion on numeracy
Filtration fallacy
Analytics mindset
The 80/20 Rule
Variability
Data capture
Data bucket
Demonstrates how to work with innumerate managers
Shows drawbacks of high reliance on data
Provides techniques to apply assess the data accuracy
Teaches on how to approach data
Allows to be efficient
Provides information of how to check data
Gives insights about errors that might occur
Teaches how to work with specific data
Managerial Innumeracy
Illusion on numeracy
Filtration fallacy
Analytics mindset
The 80/20 Rule
Variability
Data capture
Data bucket
What is a successful running of analytics?
What is a managerial innumeracy? Where does this term come from?
What is the illusion of numeracy? Provide an example
What is the filtration fallacy? Provide an example
What is the analytics mindset and why is it used?
managerial innumeracy?
Why the 80/20 rule is efficient? In what fields this rule can apply?
Why it is important to incorporate variability into the analysis?
What is the difference between numbers and data?
What is meant by data error? How frequent it is?
What are the most frequent data concerns?
How to decide on the data bucket?
What are the simplest tests to do with data?
4
Machine learning
Data mining
Training Data
Decision tree
Regression analysis
Associated rules
It enables to work with data in a more sophisticated way
Enhances knowledge
Enables to make predictions
Allows to assess opportunities
Enables to define correlation
Enables to assess different events
Machine learning
Data mining
Training Data
Decision tree
Regression analysis
Associated rules
What is meant by machine learning?
What is data mining?
What are the trends in machine learning?
What is training data and why it is used for?
What is the decision tree? Why it used for?
What is the regression analysis and why it used for?
What is algorithm and how many type?
What is meant by associated rules and what is the most popular association rule?
5
Descriptive analytics
Database
SQL
No-SQL
Data warehouse
Allows to better understand what is happening in …
