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Politecnico di Milano

Decode data: a hands-on journey to data literacy

Politecnico di Milano via Polimi OPEN KNOWLEDGE

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Overview

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Reading, cleaning, visualizing, and interpreting data with Python: a practical journey from raw tables to informed decisions, with no prior programming or statistics knowledge required.

Syllabus

  • Board
  • Week 0
  • Week 1 - Foundations of Data Literacy
  • 1.1 Introduction to Data Literacy
  • 1.2 Types of Data and Data Sources
  • 1.3 Data Collection Methods and Considerations
  • 1.4 Introduction to Python for Data Analysis
  • 1.5 Case Study
  • 1.6 Additional readings
  • 1.7 Evaluation Quiz
  • 1.8 Download Week 1 Notebooks
  • Week 2 - Python, pandas and Data Preparation
  • 2.1 Python, numpy, and pandas Essentials
  • 2.2 Importing Real Datasets and First Exploration
  • 2.3 Data Cleaning and Preprocessing: Building a Reliable Dataset
  • 2.4 Advanced Imputation: Making the Most of Incomplete Data
  • 2.5 Exploratory Analysis and Feature Engineering
  • 2.6 Safe, Consistent, and Reproducible Data Work
  • 2.7 Case Study - Cleaning and Exploring Student Performance Data
  • 2.8 Additional readings
  • 2.9 Evaluation Quiz
  • 2.10 Download Week 2 Notebooks
  • Week 3 - Data Visualization with Matplotlib, Plotly and Seaborn
  • 3.1 Principles of Effective Data Visualization
  • 3.2 Basic Plots with Matplotlib
  • 3.3 Additional Plot Types
  • 3.4 From Plots to Insight
  • 3.5 Interactive Visualizations with Plotly
  • 3.6 Customizing Plots with Seaborn
  • 3.7 Case Study 1 - Student performance and habits
  • 3.8 Case Study 2 - Product sales and marketing (Plotly)
  • 3.9 Additional readings
  • 3.10 Evaluation Quiz
  • 3.11 Download Week 3 Notebooks
  • Week 4 - Descriptive Statistics, Probability and Inference
  • 4.1 Descriptive Statistics with Python
  • 4.2 Probability Distributions and Sampling
  • 4.3 Hypothesis Testing
  • 4.4 Relationships Between Variables
  • 4.5 Case Study 1 - Customer satisfaction and response times
  • 4.6 Case Study 2 - Web traffic, conversions and A/B testing
  • 4.7 Additional readings
  • 4.8 Evaluation Quiz
  • 4.9 Download Week 4 Notebooks
  • Week 5 - Exploratory Analysis, Time Series and Intro to Machine Learning
  • 5.1 Exploratory Data Analysis Workflow
  • 5.2 Seeing Data Over Time: Time Series Basics
  • 5.3 From Patterns to Predictions: Introduction to Machine Learning
  • 5.4 Reading Model Results: Interpreting Predictions and Errors
  • 5.5 Case Study 1 - Exploratory analysis and simple prediction
  • 5.6 Case Study 2 - Time-based web analytics and simple patterns
  • 5.7 Additional readings
  • 5.8 Evaluation Quiz
  • 5.9 Download Week 5 Notebooks
  • Additional resources

Taught by

Giacomo Fiumara

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