Data analytics and Data mining

Additional Info

  • ECTS credits: 6
  • Semester: 2
  • University: University of L'Aquila
  • Prerequisites:

     

    Basic programming skills, introductory statistic.

  • Objectives:

     

    Learn fundamental techniques to examine raw data with the purpose of drawing data-driven decisions. The course deals with the main methods for supervised and non-supervised learning. Particular attention will be given to the statistical foundations of learning. The most established techniques to extract information from data to orient decisions will be treated both in their theoretical motivations and in their practical details. Open source tools will support the course step by step, providing continuous verification of the material.

  • Topics:

     

    Introduction to analytics. Data collection, cleaning and preprocessing. Exploratory Data Analysis and Visualization. Statistical inference and regression models. Optimization formulations of data analysis and learning problems. Statistical foundations of learning. Clustering and Principal Component Analysis. Decision trees - Logic methods. Support vector machines - Feature selection and extraction. Methods and tools for supervised learning.

  • Books:

     

    Python Data Science Handbook. Essential Tools for Working with Data
    Jake VanderPlas
    O'Reilly Media (2016)

    An Introduction to Statistical Learning
    Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Springer Texts in Statistics (2015)

    An Introduction to R
    Version 3.4.1 (2017)
    W. N. Venables, D. M. Smith and the R Core Team

Read 1117 times Last modified on Tuesday, 19 May 2020 13:35