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Big Data Analysis

Objectives

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.

General characterization

Code

2440

Credits

3.5

Responsible teacher

Carlos Daniel Santos

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

Pre-requisite:

- Statistics

Bibliography

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).

Teaching method

Applied lectures with empirical case studies.

Evaluation method

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%

  • Cases: 50%

Subject matter

  • 1. Big data in Business;

  • 2. Data preprocessing and exploration;

  • 3. Description and Prediction (Lasso and Ridge);

  • 4. Decision trees.