Big Data Analysis
This course will equip students with the tools to make informed decisions on data driven business areas. It will provide them with a good understanding on the working of big data and data analytics and we will cover some of the methods in detail, their advantages and disadvantages. In the end of the course students should be able conduct analysis and take decisions based in data driven environments. The course is practical with several data cases covered and analysis using dedicated software.
Carlos Daniel Santos
Weekly - Available soon
Total - Available soon
Dean, J. (2014) Big Data, Data Mining and Machine Learning, Wiley (JD) Linoff and Berry (2011) Data Mining Techniques, 3rd edition, Wiley (LB).
James, G., D. Witten, T. Hastie and R. Tibshirani (2013) An Introduction to Statistical Learning with applications in R, Springer (ISL).
Tan, P., M. Steinbach, V. Kumar, (2006) Introduction to Data Mining, Addison Wesley (IDM).
Applied lectures with empirical case studies.
The final exam is mandatory and must cover the entire span of the course. Its weight in the final grade can be between 30 to 70%. The remainder of the evaluation can consist of class participation, midterm exams, in class tests, etc. Overall, written in class assessment (final exam, midterm) must have a weight of at least 50%.
Final exam: 50%
1. Big data in Business;
2. Data preprocessing and exploration;
3. Description and Prediction (Lasso and Ridge);
4. Decision trees.
Programs where the course is taught: