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Decode data: a hands-on journey to data literacy
Decode data: a hands-on journey to data literacy
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.
Course description
The course introduces data literacy - the ability to read, question, analyze, and communicate data - and immediately puts it into practice with Python. It begins with the fundamentals: what data is, how it is classified, where it comes from, and the ethical implications of collecting it. The course then covers data preparation with NumPy and pandas (importing, cleaning, imputing missing values, exploratory analysis, and safe, reproducible workflows); visualization with Matplotlib, Plotly, and Seaborn, starting from the perceptual principles that make a chart clear or misleading; descriptive and inferential statistics (probability distributions, sampling, hypothesis testing, correlation, and regression); and, finally, a complete analysis workflow including time series and an introduction to supervised machine learning. Each topic is supported by executable Jupyter notebooks and case studies based on realistic datasets. The course is delivered in English.
Total workload of the course: 150 hours
This MOOC was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 “Didattica e competenze universitarie avanzate".
Intended Learning Outcomes
- Classify data by type and structure and identify data sources and collection methods, assessing their ethical and privacy implications
- Import, inspect, and describe a real-world dataset in Python with pandas, using CSV and Excel files
- Assess the quality of a dataset by identifying errors, duplicates, missing values, and outliers, and apply appropriate cleaning and imputation strategies
- Select and create a visualization suited to the analytical question using Matplotlib, Seaborn, and Plotly, while recognizing misleading charts
- Calculate and interpret descriptive statistics and the shape of a distribution (skewness, quartiles, IQR, and outliers)
- Set up and interpret a hypothesis test, correctly interpreting the p-value and its limitations
- Quantify relationships between variables using correlation and linear regression, distinguishing correlation from causation
- Conduct a complete exploratory analysis workflow, analyze a time series, and train and evaluate a simple predictive model
Prerequisites
- No programming prerequisites: Python is introduced from scratch in Week 1;
- No statistics prerequisites: the required concepts are introduced in Week 4;
- Basic ability to use a computer and a web browser is sufficient; practical activities are completed in Jupyter notebooks;
- A good understanding of written and spoken English, as the course is delivered in English.
Activities
- Video lessons, one or two for each weekly module, with full transcripts;
- Python (Jupyter) notebooks containing the code and figures from each lesson;
- Guided case studies using realistic datasets: student performance, bike sharing, sales and marketing, support tickets, A/B testing of web traffic, and app engagement;
- A collection of supplementary readings and videos for each week;
- Weekly assessment quizzes: one per week, with 10 multiple-choice questions.
Section outline
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Video transcripts Folder
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Assessment
Your final grade for the course will be based on the results of your answers to the assessed quizzes. You have an unlimited number of attempts at each quiz, but you must wait 15 minutes before you can try again. You will have successfully completed the course if you score 60% (or higher) in each one of the assessed quizzes. The maximum score possible for each quiz is given at the beginning of the quiz. You can view your score in the quiz on your last attempt or on the 'Grades' page.
Certificate
You can achieve a certificate in the form of an Open Badge for this course, if you reach at least 60% of the total score in each one of the assessed quizzes and fill in the final survey.
Once you have completed the required tasks, you will be able to access ‘Get the Open Badge’ and start issuing the badge. Instructions on how to access the badge will be sent to your e-mail address.
The Badge does not confer any academic credit, grade or degree.
Information about fees and access to materials
The course is delivered in online mode and is available free of charge.
Course faculty
Giacomo Fiumara
Teacher
Contact details
If you have any enquiries about the course or if you need technical assistance please contact pok@polimi.it. For further information, see FAQ page.