96804 - BIG DATA AND ANALYTICS

Learning outcomes

Aim of the course is to learn the fundamentals of the most important multivariate techniques that help to make intelligent use of large data base by recognizing patterns for predicting or estimating an output based on one or more inputs. At the end of the course the student is able; - to represent and organize knowledge about big data collections; - to turn data into actionable knowledge; - to choose the best suited methodology for the problem at hand to critically interpret the results.

Course contents

Part 0: Introduction to Supervised Statistical Learning

Part 1: Resampling methods

• Cross-Validation
• Bootstrap

Part 2: Classification

• Naive Bayes
• k-Nearest Neighbours
• Logistic Regression
• Linear Discriminant Analysis

Part 3: Dimension Reduction and Regularisation

Part 4: Tree-based methods

• Regression and Classification trees
• Bagging; Random Forests; Boosting

Part 5: Overview of the main machine learning methods

• Support Vector Machines
• Neural Networks

Readings/Bibliography

The primary text for the course:

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to Statistical Learning. Second Edition. New York: Springer. ISBN: 978-1-0716-1417-4. E-book ISBN 978-1-0716-1418-1
  
  The book is freely available here:
  https://hastie.su.domains/ISLR2/ISLRv2_website.pdf
  

In addition, we will use:

• T. Hastie, R. Tibshirani, and J. Friedman (2001) The Elements of Statistical Learning: data mining, inference and prediction. Springer Verlag.
  Freely available at: https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Teaching methods

Theoretical lectures and practical sessions in R Studio.

Assessment methods

The learning assessment will be by a written test lasting between 60 and 90 minutes. The test will be composed of theoretical and practical questions, aimed at assessing the student's knowledge of explained statistical methods and the student's ability to perform statistical analyses and to interpret the resulting outputs in R Studio. The final grade is out of thirty.

During the written exam, students can only use the cheat sheet that is provided on virtuale.unibo.it, containing references to R packages and functions. Students cannot make use of the textbook, personal notes and mobile phones (smart watch or similar electronic data storage or communication devices are not allowed either).

Students that, despite having passed the exam, do not feel represented by the obtained result can ask to have an additional (optional) oral exam that can change the grade by +/-3 points.

Teaching tools

The students will be provided with course slides, commented R codes, mock exams. Electronic tablet will be used during lectures. The R files related to the primary textbook will be exploited from the website https://www.statlearning.com/resources-second-edition.

Office hours

See the website of Matteo Farnè

See the website of Simone Tiberi