Summary
Business Intelligence &
Business Analytics
,Table of Contents
1. Week 1 lecture 1: Introduction to Data Management & Business Intelligence .................................. 1
1.1. Course introduction...................................................................................................................... 1
1.2. Introduction to Business Intelligence / Analytics ......................................................................... 2
1.3. Introduction to Databases ............................................................................................................ 4
1.4. Relational database ...................................................................................................................... 5
1.5. Week 1: Book materials................................................................................................................ 7
2. Week 1 lecture 2: Introduction to data warehousing ......................................................................... 9
3. Week 2 lecture 3: ETL, OLAP business databases & business dashboards ....................................... 20
4. Week 3 lecture 4: Data Mining Introduction..................................................................................... 29
4.1. Data Mining Intro ....................................................................................................................... 29
4.2. Data Mining Process(es): overview of the steps involved in data mining.................................. 30
5. Week 3 lecture 5: Regression models ............................................................................................... 34
EXTRA: Intro to Dataframes and Pandas ............................................................................................... 36
6. Week 4 lecture 6: Naïve Bayes Classifier........................................................................................... 37
7. Week 4 lecture 7: k-Nearest Neighbors Classifier ............................................................................. 40
8. Week 4 lecture 8: Performance Measures ........................................................................................ 43
8.1. Evaluating Predictive Performance: numerical (continuous) variables ..................................... 45
8.2. Judging Classifier Performance: categorical variables ............................................................... 46
8.3. Precision and recall..................................................................................................................... 50
9. Week 5 lecture 9: Decision trees ....................................................................................................... 53
10. Week 5 lecture 10: Association rules .............................................................................................. 58
10.1. Generation of frequent itemsets & selecting the strong rules ................................................ 59
11. Week 6 lecture 11: Clustering ......................................................................................................... 64
11.1. Hierarchical clustering .............................................................................................................. 67
11.2. Partitional clustering (k-means for this course) ....................................................................... 69
12. Week 7 lecture 12: Neural Networks .............................................................................................. 73
Quiz questions ....................................................................................................................................... 79
Quiz answers ......................................................................................................................................... 86
Notes ......................................................................................................... Error! Bookmark not defined.
,1. Week 1 lecture 1: Introduction to Data Management & Business
Intelligence
1.1. Course introduction
Data management: “managing data as a valuable
resource.”
Business intelligence (BI) / analytics (BA)?: “data-
driven decision-making”. Transforming data into
meaningful information/knowledge to support
business decision-making.
3 concepts of BI & BA:
Data: items that are the most elementary
descriptions of things, events, activities, and
transactions. Can be internal, external, structured,
unstructured.
Information: organized data that has meaning and value.
Knowledge: processed data or information that is applicable to a business decision problem.
Descriptive analytics: use data to understand past & present.
Diagnostic analytics: explain why something happened.
Predictive analytics: predict future behaviour based on past performance.
Prescriptive analytics: make decisions or recommendations to achieve the best performance.
1
, 1.2. Introduction to Business Intelligence / Analytics
General view definitions:
• Business intelligence: data warehousing + descriptive analytics.
• Business analytics: predictive + prescriptive analytics.
Our view in this course: BI = BA. They are all decision support systems (DSS).
2 definitions of BI:
• Process view (Sharba, 2014): “BI is an umbrella term that combines the processes,
technologies, and tools needed to transform data into information, information into
knowledge, and knowledge into plans that drive profitable business action.”
• Product/output view (Shaberwal, 2011): “BI is information and knowledge that enables
business decision-making.”
BI product, process, solution, and tools:
2
Business Intelligence &
Business Analytics
,Table of Contents
1. Week 1 lecture 1: Introduction to Data Management & Business Intelligence .................................. 1
1.1. Course introduction...................................................................................................................... 1
1.2. Introduction to Business Intelligence / Analytics ......................................................................... 2
1.3. Introduction to Databases ............................................................................................................ 4
1.4. Relational database ...................................................................................................................... 5
1.5. Week 1: Book materials................................................................................................................ 7
2. Week 1 lecture 2: Introduction to data warehousing ......................................................................... 9
3. Week 2 lecture 3: ETL, OLAP business databases & business dashboards ....................................... 20
4. Week 3 lecture 4: Data Mining Introduction..................................................................................... 29
4.1. Data Mining Intro ....................................................................................................................... 29
4.2. Data Mining Process(es): overview of the steps involved in data mining.................................. 30
5. Week 3 lecture 5: Regression models ............................................................................................... 34
EXTRA: Intro to Dataframes and Pandas ............................................................................................... 36
6. Week 4 lecture 6: Naïve Bayes Classifier........................................................................................... 37
7. Week 4 lecture 7: k-Nearest Neighbors Classifier ............................................................................. 40
8. Week 4 lecture 8: Performance Measures ........................................................................................ 43
8.1. Evaluating Predictive Performance: numerical (continuous) variables ..................................... 45
8.2. Judging Classifier Performance: categorical variables ............................................................... 46
8.3. Precision and recall..................................................................................................................... 50
9. Week 5 lecture 9: Decision trees ....................................................................................................... 53
10. Week 5 lecture 10: Association rules .............................................................................................. 58
10.1. Generation of frequent itemsets & selecting the strong rules ................................................ 59
11. Week 6 lecture 11: Clustering ......................................................................................................... 64
11.1. Hierarchical clustering .............................................................................................................. 67
11.2. Partitional clustering (k-means for this course) ....................................................................... 69
12. Week 7 lecture 12: Neural Networks .............................................................................................. 73
Quiz questions ....................................................................................................................................... 79
Quiz answers ......................................................................................................................................... 86
Notes ......................................................................................................... Error! Bookmark not defined.
,1. Week 1 lecture 1: Introduction to Data Management & Business
Intelligence
1.1. Course introduction
Data management: “managing data as a valuable
resource.”
Business intelligence (BI) / analytics (BA)?: “data-
driven decision-making”. Transforming data into
meaningful information/knowledge to support
business decision-making.
3 concepts of BI & BA:
Data: items that are the most elementary
descriptions of things, events, activities, and
transactions. Can be internal, external, structured,
unstructured.
Information: organized data that has meaning and value.
Knowledge: processed data or information that is applicable to a business decision problem.
Descriptive analytics: use data to understand past & present.
Diagnostic analytics: explain why something happened.
Predictive analytics: predict future behaviour based on past performance.
Prescriptive analytics: make decisions or recommendations to achieve the best performance.
1
, 1.2. Introduction to Business Intelligence / Analytics
General view definitions:
• Business intelligence: data warehousing + descriptive analytics.
• Business analytics: predictive + prescriptive analytics.
Our view in this course: BI = BA. They are all decision support systems (DSS).
2 definitions of BI:
• Process view (Sharba, 2014): “BI is an umbrella term that combines the processes,
technologies, and tools needed to transform data into information, information into
knowledge, and knowledge into plans that drive profitable business action.”
• Product/output view (Shaberwal, 2011): “BI is information and knowledge that enables
business decision-making.”
BI product, process, solution, and tools:
2
