The aim of the course is to train students to apply some of the most commonly used predictive modeling techniques. Through this course, students will create a solid foundation of predictive analytics, which is used to build predictive models based on data. Special emphasis will be placed on the proper application of selected predictive analytics methods and on the interpretation of the obtained results. In this way, skills are acquired without which it will not be possible to make any business decisions in the future. The methods and techniques learned in this course are applied in all areas within business organizations, including accounting, finance, human resource management, marketing, operations, strategic planning. R software will be used in data analysis
The course content is divided into 7 modules:
1. Business analytics. Data mining. Data science.
2. The Data Analysis Process. Introduction to R.
3. How to evaluate the results of the analysis.
4. Development, validation and application of a linear regression model for solving a business problem
– car/house/stock price prediction
5. Development, validation and application of classification and regression decision trees for solving
business problem – prediction of trading outcome, whether the customer will buy the product
6. Development, validation and application of logistic regression to solve a business problem - will it
client repay loan
7. Development, validation and application of cluster analysis to solve a business problem - how
segment customers
1. Apply appropriate techniques and methods of predictive analytics to solve business problems
2. Interpret the results of the analysis from the perspective of business logic
3. Propose business solutions based on the performed analysis using predictive models
4. To develop critical inferential thinking
