Data Analysis for Economic Policy 4.

Course Term: 
1st year
Term: 
Winter
Credits: 
2.0
ECTS credits: 
2.0
Status: 
Core
Course Description: 

Data Analysis 4 covers the fundamentals of statistical prediction and predictive analytics. This course equips students with the knowledge and skills necessary to carry out and evaluate predictions in business and policy environments. Similar to Data Analysis 1, 2 & 3 we focus on the most robust, credible and transparent methods, and we emphasize correct interpretation and convincing presentation. This course starts with the fundamentals of predictive analytics and covers topics such as prediction from regressions, tree-based models, time series forecasting models and unsupervised learning algorithms.

Learning Outcomes: 

By successfully completing the course the students will be able to:

- Carry out reasonably good predictions and evaluate their performance;

- Evaluate the predictive performance of all kinds of models;

- Discuss and evaluate results of predictive analysis.

- Present the results of predictive analytics and write short reports;

- Evaluate the merits of presentations and reports that carry out predictive analytics.

Assessment: 

Grading

Quizzes 15%

Assignments 10%

Term Project 25%

Exam: 50%

Passing the course requires a passing score on the exam (over 50%)

 

Prerequisites: 

5. Relationship with other courses: Prerequisite: Data Analysis 1 & 3.

File Attachments: