Data Analytics in Python

From Big Data to Machine Learning, applications based on Data Analytics have become ubiquitous over the past decades, as we truly are in the Age of Information. This course aims at providing the basis to understand and navigate autonomously the topic, from both a theoretical and practical perspective. The Python programming language will also receive significant attention, as to provide the students with tools and skillset immediately useful in the modern industrial and academic landscape, including application-oriented Machine Learning.
The course material is in English and available at all time. The students are encouraged to follow the lectures at their own pace, and start at any time. Support is provided at specific times during the academic year.

• Ziel

  This online course provides the students with the competences and skills to perform advanced Data Analysis using the Python programming language.

• Inhalte und Themen

  This online course offers an overarching study of Data Analytics from multiple perspectives, which will be addressed as they become needed through the course progression. The main topics are:

  • Python programming: language fundamentals, Jupyter notebooks, object-oriented programming, advanced functionalities, core libraries, debugging.
  • Handling data: data sources, defects and mitigation, access and formats, visualization.
  • Analysis: experiment design, data selection, normalization, feature selection, fundamental statistics.
  • Machine Learning: classification, regression, clustering, imputation, dimensionality reduction, with a few selected algorithms on each topic.
  • Additional Python libraries: Numpy, IPython, Pandas, Seaborn/Matplotlib, Scikit-Learn, Keras/Tensorflow.
• Form, Datum und Ort

  Online course, 1 semester.

• Voraussetzungen

  The course "Python Programming online" is strongly recommended: https://www.unifr.ch/digitalskills/fr/student/python-programming-online.html

• Validierung

  The acquisition of ECTS credits is possible and optional, based on passing an exam at the end of the course. The format of the exam will be communicated at the beginning of the course.

• Verantwortliche

  Giuseppe Cuccu