Data Science in Action

Domain
Instructor Sandro Fiore
Duration 12 hours
ECTS 2
Period First semester

Course objectives

Data science is the study of extracting value from data. It combines insights, techniques, and tools from several disciplines including, among others, computer science, statistics and applied mathematics. The course is intended to provide students with practical skills on Data Science to address real-world case studies. The course targets students from all disciplines and consists of 12 hours; it includes short theoretical parts jointly with more extensive practical ones developed through a set of hands-on/exercises based on Jupyter Notebooks. A step-by-step approach from basics of Python programming language to more advanced Python libraries for data science will be followed throughout the course. At the end of the course students will be equipped with a comprehensive knowledge of Data Science tools that will allow them to work on a variety of data-driven problems in any discipline.

Entry requirements
  • The course on Basics of Data Science would help in terms of data science theoretical foundations, but it is not strictly required.
  • Basic knowledge of Python programming language would help, but it is not mandatory

Content summary

The course will include the following topics:

  1. Course introduction
  2. Toolboxes for Data Scientists:
    • Python and Python Libraries for Data Scientists (NumPy, Scipy, SCIKIT-Learn, PANDAS)
    • Data Science Ecosystem
    • Integrated Development Environments (Jupyter, Jupyter Notebooks)
  3. Python fundamentals for Data Scientists
  4. Main Data Science toolboxes with practical examples in Python (each lecture will include practical parts)

Teaching methods

The course is structured with theoretical and (mostly) practical sessions. Theory and exercises will be integrated, so for each concept we will see both. Jupyter Notebooks will be extensively used used for the hands-on part. Homeworks will be also assigned to students and discussed in class.

All information will be available on the website of the course.

Test and assessment criteria

A final Data Science project will be used to assess students’ knowledge and practical understanding of the course topics.

Texts

  • Introduction to Data Science, A Python Approach to Concepts, Techniques and Applications, by L. Igual, S. Seguí, Publisher Springer, https://link.springer.com/book/10.1007/978-3-319-50017-1
  • Python for Data Analysis, by Wes McKinney, August 2022, Publisher(s): O’Reilly Media, Inc. ISBN: 9781098104030