The course content is dedicated to the applications of machine learning and data mining to business analysis. The intent is to give a high-level overview of the potential of machine learning and data mining in different areas and, more specifically, the area of business analytics. The main sections are:
1. Core ideas of the data mining process - this section covers the definitions of the concepts of data mining, big data, business analytics and business intelligence.
2. Basics of exploratory data analysis - this section covers EDA with R.
3. Visualizing complex data sets - in this part the main visualizations used in business analytics and advanced data analysis are discussed in detail.
4. Housing valuation with multiple linear regression - this section covers the basic steps of the data mining process using housing valuation example.
5. Store discounts with random forest and natural language processing - in this section, the topic of machine learning methods, such as, decision trees and random forest, is examined. An example of store discounts is given to illustrate the application of natural language processing and random forest to text-rich data.
6. Market basket analysis with unsupervised machine learning - in this section unsupervised machine learning methods, such as, Apriori and associative rules are examined in detail. An example with market basket analysis is given to illustrate the application of Apriori and associative rules.
