Python: de explorador de datos a analista

This module focuses on Python installation and advanced practices. It covers Python installation, PowerShell introduction, virtual environments, workspace setup, Python package development, Python improvement proposals, object-oriented programming, error handling, decorators, unpacking, lambda functions, and the Walrus operator. The module includes practical exercises to reinforce these concepts.

This module covers advanced Exploratory Data Analysis (EDA) techniques. It includes a review of Matplotlib, object-oriented programming in Matplotlib, decorators in Matplotlib, plot styles, interactive graphs, data structures in OWD, introductions to Plotly and Bokeh, grammar of graphics, and comparisons between different visualization tools. The module focuses on enhancing data visualization skills for more effective data analysis.

This module delves into advanced Pandas techniques for data analysis. Topics include an introduction to Pandas, moving averages, functions for consecutive values, new file formats, the groupby method, pivot_table method, combining dataframes, handling missing data, and working with time zones. The module provides deeper insights into efficient data manipulation and analysis with Pandas.

This module introduces new tools in data science. It covers executing notebooks, converting notebooks to scripts, unit testing, introduction to Quarto, converting notebooks to reports with Quarto, creating presentations in Quarto, introduction to Shiny, and libraries for data exploration. The module aims to expand the student's toolkit with modern data science tools for more effective analysis and presentation.

This module focuses on the final project of the course. Students are required to apply all the concepts and techniques learned throughout the course to complete a comprehensive data analysis project. This hands-on project allows students to demonstrate their skills in Python programming, data visualization, data manipulation, and analysis.