Title: Practice and formative deliverable
Course: Advanced Data Science: A Graduate Course > Section: Module 2: Preprocessing messy and dynamic data

Practice and formative deliverable

The practice items below are formative and ungraded. They build intuition before the graded assessment. The formative deliverable is checked for completion and returned with feedback, and it earns completion points on submission, credited on completion rather than scored for correctness.

Build fluency with the mechanics of cleaning and the visual diagnosis of missingness before the graded simulation.

1. Complete the Kaggle Learn Data Cleaning micro-course (auto-graded), covering missing values, scaling and normalization, parsing dates, character encodings, and inconsistent-entry cleanup.
2. Complete the Kaggle Learn Feature Engineering micro-course (auto-graded), paying particular attention to constructing features without leaking target information.
3. In a notebook, load a small messy dataset and visualize its missingness with missingno, generating the matrix, bar, and heatmap/dendrogram views. Read the correlation structure of the nulls: columns whose missingness co-occurs are evidence pointing away from MCAR and toward MAR or MNAR.

◎ Formative deliverable: Submit a cleaning log for a small messy dataset of your choosing. For each cleaning decision, record three things: the action taken, the mechanism assumption behind it (for missing values, which of MCAR/MAR/MNAR you infer and what evidence, including your missingno views, supports that inference), and how the step is reversible (what raw value or state it can be reconstructed from). State how you checked each decision, for example by comparing distributions before and after, or by confirming row counts reconcile after a join. This earns completion points on submission and is returned with rubric-referenced feedback before the summative opens.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”