Analytics and visualisation
Description: Lecture 6 presents analytics and visualisation techniques. Once we have processed, harmonised, and ingested our data, the next step is to use to solve the data problems at hand (i.e., address). Visualisation tools support decision makers by presenting data in formats that are easier to understand and analyse in context (e.g., dashboards). More advanced analytics are possible when probabilistic models support prediction or classification processes.Department: Departamento de Matemáticas y Estadística - Facultad de Ciencias Exactas y Naturales
Institution: Universidad de Nariño
Date: June 20, 2026
Hours: 5
From: 07:00 am
To: 01:00 pm
Week 3 links
Resources
- Linear Regression in Machine learning
- The Ascent of Gradient Descent
- Introductory Python course (optional video)
- scikit-learn user guide (reference)
- Plotly Python documentation (reference)
References
- Zaharia, M., et al. (2016). Apache Spark: a unified engine for big data processing. CACM, 59(11), 56–65.
- Jarrahi, M. H., et al. (2023). The Principles of Data-Centric AI. (Course PDF.)
- Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach 3rd ed - Chapter 19 . Prentice Hall
- Bishop, C. (2009). Pattern Recognition and Machine Learning - Chapter 3. Springer
- Deisenroth M. P. et. al. (2020). Mathematics for Machine Learning - Chapter 9
- Deisenroth M. P. et. al. (2020). Mathematics for Machine Learning - Chapter 10
- Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press - Chapters 6-9
- LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444