Base Knowledge
Programming skills are recommended, particularly in python and the SciPy ecosystem
Teaching Methodologies
Lectures
– Presentation and discussion of program topics
– Presentation and discussion of papers and examples
– Guest lectures
Practical lessons
Exercises, practical work with real data and presentations are carried out.
Learning Results
1- Understand the main steps of the data analysis process
2 – Apply the CRISP-DM methodology in a data analysis process
3 – Know data visualization, pre-processing and transformation techniques
4 – Know supervised and unsupervised computational learning methods
5 – Understand the performance metrics used in evaluating results
6 – Select, adapt and apply learning techniques to create classification, detection or prediction models
7 – Evaluate critically and quantitatively the quality of the models created
Program
1. Introduction
1.1. What is data analysis
1.2. Importance of data analysis in organizations
2. Methodologies to follow in a data analysis process
2.1 CRISP-DM methodology
2.2. Business understanding, Data understanding, Data Wrangling
2.3. Modeling, Results analysis and Deployment
3. Pre-processing and transformation
3.1. Visualization
3.2. Missing and discrepant data
3.3. Normalization, dimensionality reduction and feature selection
4. Computational learning methods
4.1. Performance metrics
4.2. Unsupervised learning
4.3. Supervised learning
5. Modeling
5.1 Image analysis
5.2 Text analysis
5.3 Time series analysis
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing. Loc. 1A-4-187 (ISEC) – 18950.
Chollet, F. (2021). Deep learning with Python. Shelter Island, NY : Manning, cop. Loc. 1A-4-205 (ISEC) – 19262
Géron, A. (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media. ISBN-13 : 978-1492032649. Loc. 1A-4-179 (ISEC) – 18948.
Kelleher, J., & Tierney, B. (2018), Data Science (The MIT Press Essential Knowledge series). MIT Press. ISBN-13: 978-0262535434. Loc. 1A-19-31 (ISEC) – 19364.
Goodfellow, I., & Bengio, Y., & Couville, A. (2016). Deep learning. The MIT Press. ISBN: 0262035618.
Kelleher, J., & Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press. ISBN: 9780262044691.
Pointer, A. (2019). Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications (1st Ed.). O’Reilly Media.
Han J., Kamber M., & Pei J. (3rd Ed.). (2012). Data Mining Concepts and Techniques. Elsevier. Loc. 1A-19-11 (ISEC) – 14693.
Rebala G., Ravi A., & Churiwala S. (2019). An Introduction to Machine Learning. Springer.
Other online resources, namely Python, sk-learn, tensorflow, pytorch and keras manuals.