Python: de usuario a explorador de datos

This module covers the fundamentals of data extraction, transformation, and loading (ETL) as well as data exploration in data science. It introduces pandas and dataframes, reading CSV and XLSX files, understanding time series concepts, and performing basic dataframe operations. The module also covers visual data exploration, statistical and structural data analysis, handling null values, and changing data frequency.

This module introduces Matplotlib for data visualization. It covers the anatomy of a graph, the concept of figures and axes, various types of plots, creating individual graphs, and customizing figures. Students learn to create temporal plots, work with multiple graphs including complex patterns, use interactive graphs with ipywidgets, and prepare figures for publication.

This module focuses on array manipulation with NumPy. It introduces NumPy and numerical arrays, importing files with NumPy, attributes and properties of arrays, and basic array operations. Students learn slicing and fancy slicing, image manipulation with NumPy, handling multiple files with glob, creating intervals with arange and linspace, array manipulation, saving data in NumPy, creating meshes for numerical calculations, and tools for linear algebra.

This module covers the complete ETL (Extract, Transform, Load) process and EDA (Exploratory Data Analysis). Topics include workspace concepts, computational narrative in notebooks, concatenating files with pandas, multi-indices in time series columns and indices, encoding, developing local packages, and best practices review. The module culminates in a final exercise on visual data exploration, data cleaning, analysis, and creating a final figure. It also introduces data science packages to explore.

This module provides instructions for completing and submitting the final honors project to earn an honors certificate for the course. It includes guidance on how to submit the project correctly.