| Week | Topic | Reading | Slides | Practice | Assignment |
|---|---|---|---|---|---|
| 1 | The foundation under data analysis What one row means, what one column holds, N/O/Q/T, grain | PDA ch. 1 — Preliminaries (web) PDA ch. 5, 5.1–5.3 — Getting Started with pandas (web) PPDW ch. 1 — Introduction to Data Wrangling and Data Quality (O’Reilly) PPDW ch. 3 — Understanding Data Quality (O’Reilly) |
Slides | Repository | |
| 2 | DataFrames and profiling Indexing, filtering, sorting, derived columns, type conversion, duplicates, a systematic profiling method | PDA 5.2 — Essential Functionality (web) PPDW ch. 6 — Assessing Data Quality (O’Reilly) |
TBD | TBD | |
| 3 | Aggregation and grouping Split-apply-combine, aggregation traps, average-of-averages, non-additive measures | PDA ch. 10 — Data Aggregation and Group Operations (web) | TBD | TBD | A1 released |
| 4 | Text and missing values String methods, regular expressions, NaN behaviour and strategy, parsing dates | PDA 7.1 — Handling Missing Data (web) PDA 7.4 — String Manipulation (web) PDA 11.1 — Date and Time Data Types and Tools (web) PPDW ch. 7 — Cleaning, Transforming, and Augmenting Data (O’Reilly) |
TBD | TBD | A1 due |
| 5 | Tidy data and reshaping Wide and long, time as grain, resampling, categorical types. No lecture this week: the mid-term exam is in the lecture time | PDA 7.5 — Categorical Data (web) PDA 8.3 — Reshaping and Pivoting (web) PDA 11.2 — Time Series Basics (web) PDA 11.6 — Resampling and Frequency Conversion (web) |
Mid-term | TBD | A2 released |
| 6 | Data formats CSV, JSON, Excel, Parquet; nested JSON; reading from an API | PDA ch. 6 — Data Loading, Storage, and File Formats (web) PPDW ch. 4 — Working with File-Based and Feed-Based Data in Python (O’Reilly) PPDW ch. 5 — Accessing Web-Based Data (O’Reilly) |
TBD | TBD | A2 due A3 released |
| 7 | Joining and pipelines Joins, concatenation, fan-out, join validation, method chaining, end-to-end cleaning | PDA ch. 7 — Data Cleaning and Preparation (web) PDA 8.1 — Hierarchical Indexing (web) PDA 8.2 — Combining and Merging Datasets (web) PPDW ch. 6 — Assessing Data Quality (O’Reilly) PPDW ch. 7 — Cleaning, Transforming, and Augmenting Data (O’Reilly) PPDW ch. 8 — Structuring and Refactoring Your Code (O’Reilly) |
TBD | TBD | A3 due |
| 8 | Synthesis and review Where these skills go next, exam preparation. The final exam is in this week, after the practice sessions | PDA ch. 13 — Data Analysis Examples (web) PPDW ch. 9 — Introduction to Data Analysis (O’Reilly) |
TBD | TBD |
PDA is Python for Data Analysis, 3rd edition by Wes McKinney, free to read on the web. PPDW is Practical Python Data Wrangling and Data Quality by Susan E. McGregor, on O’Reilly.
The final exam date is on the timetable. Deadlines for practices and assignments are on Moodle.