Programming Laboratory for Data Science

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Programming Laboratory for Data Science

Credits

6

Prerequisites

Programming Laboratory.

Scientific-disciplinary sector (SSD)

INF/01 Computer Science.

Examination method

Laboratory activities, written and oral exam.

Learning
objectives

The course aims to explore in depth the software tools and methodologies for the development and implementation of algorithms in the scientific field, with particular regard to contexts characterised by the manipulation, analysis and visualisation of numerical and textual data. Laboratory activity is an integral part of the course.

Syllabus

  • Dynamic data structures and algorithms for their management. Recursive algorithms. Records and
    files.
  • Languages and tools for data science: The Python language. Control structures, structured data
    types, reading and writing files, development of modules, interaction with other programming
    languages.
  • The main extensions for data processing, analysis and visualisation
    (NumPy, Pandas, Scipy, Matplotli).
  • General methodologies for the efficient processing of large
    amounts of data: multithreaded programming, introduction to high-performance computing.

Expected learning
outcomes

Students are expected to be able to read and write working C code with complex data structures and recursion mechanisms, but also to make conscious use of the constructs of a high-level language such as Python, consolidating their software design skills. They will have the opportunity to experiment with, and develop an interest in, the differences and possibilities offered by the tools presented.

Learning outcomes
to be assessed

Students are expected to be able to read and design an algorithm for solving a problem, and to become familiar with computing systems and with the basic tools for Data Science.