95010 - PYTHON FOR ANALYTICS

Anno Accademico 2020/2021

  • Modalità didattica: Convenzionale - Lezioni in presenza
  • Campus: Bologna
  • Corso: Laurea Magistrale in Statistical sciences (cod. 9222)

Conoscenze e abilità da conseguire

In this course the student will acquire the fundamental Python tools with reference to advanced topics such as object-oriented programming patterns, graphical user interfaces, data management and analysis. They will also learn the main features of Python libraries and combination of tools, like Jupyter Notebook and Gitlab for various data-science applications.

Contenuti

The course focuses on the paradigm and fundamental characteristics of Python, as a programming language, suitable for data manipulation within the data science field. The emphasis is on exploring its libraries, which assist in reading/writing data, in grouping, aggregation, merging and joining data frames, and thus enable data visualizations and its analysis. The practical part of the course involves the use of tools and development platforms, such as Jupyter Notebook and Gitlab, for sharing and supporting data analysis. The course also includes access to various data sets for the purpose of illustrating the applicability of the material in real life examples.

Part 1 – Introduction to Python

Basic structures in and functionality of Python, which includes reading and writing data files.

Introducing libraries and debating their role and purpose within the data science.

Using DataFrame and Series, and running basic statistical analysis.

Part 2 – Data management and representation in Python

Techniques and methods for structuring and visualization of data.

Applicability and functionality of libraries such as matplotlib, seaborn, and plotly.

Data preparation for statistical analysis.

Part 3 – Introduction to Machine Learning in Python

Introduction to Machine Learning (ML) techniques.

Demonstration of supervised and unsupervised ML approaches.

Introduction to libraries, such as scikit-learn, TensorFlow and nltk.

Testi/Bibliografia

Online materials and other suggested readings will be indicated during the courses.

Metodi didattici

  • Theoretical lessons in teaching room
  • Tutorials in lab

During the classes the students will be guided in the implementation and practice of the presented concepts.

If possible, seminar on specific topics of interest will be organized.

Modalità di verifica e valutazione dell'apprendimento

Students will be assessed through two different types of assessments.

  1. Assignment 1: in their group work, which focuses on a programming Project, students will demonstrate their ability to analyses a given data set, using Python libraries, answer questions from given tasks and share the results of the project group on the public online repository, like Gitlab.
  2. Assignment 2:in their individual assessment, students will present the results of their work on Assignment 1 (programming Project), in the form of presentation, where each student will debate issues related to the Project and answer relevant questions.

Strumenti a supporto della didattica

Course notes. Open source projects used as teaching examples.

Orario di ricevimento

Consulta il sito web di Elisabetta Ronchieri