Fundamentals of Data Science

Basic information

Workload: 

45 hours

Syllabus: 

Data Concepts, Data Pre-Processing: Cleaning, Outlier Evaluation, Data Transformation, Reduction, Analysis and Selection of Variables; Knowledge Representation, Data Classification from Decision Tree Algorithms, Based on Instances; Clustering by partition, hierarchical; use of Neural Networks, Genetic Algorithms, Fuzzy Logic and Hybrid Systems in MD; MD Software and Tools; Domains of Application and Case Studies.

 

Teaching Plan

Bibliography

Mandatory: 

•    Ballard, D. H. (1999). Introduction to Natural Computation. MIT Press.
•    Goldschmidt, R., & Passos, E. (2005). Data Mining. Campus.
•    Han, J., Kamber, M., & Pei, J. (2000). Data Mining: Concepts and Techniques. Morgan Kaufmann.
•    Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Addison Wesley.
•    Witten, I. H., Frank, E., & Hall, M. A. (2000). Data Mining. Morgan Kauffmann.