F03 Stage A · Foundations 36 h (12 T / 24 P)
Data Handling, Wrangling & Visualisation
Turns raw, imperfect data into a trustworthy analysis table.
Prerequisite
F01
Modules
4
NOS
SSC/N9004
Statutory mapping
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Tech stack
NumPyPandasMatplotlibSeaborn
Modules
4 modules · 36 hours
M1 · NumPy
8 h TOPICS
- ndarray and dtypes
- Broadcasting
- Vectorisation
- Axis semantics
- Memory layout
- Random number generation
Lab: Replace loops with vectorised operations; benchmark the speed-up
Course material for this module is in production.
M2 · Pandas
10 h TOPICS
- Series and DataFrame
- Indexing and selection
- Joins and merges
- Group-by aggregation
- Reshaping
- Time series
- Categoricals
Lab: Join four heterogeneous sources into one analysis table
Course material for this module is in production.
M3 · Data quality
8 h TOPICS
- Missing values
- Duplicates
- Outliers
- Type coercion
- Encoding issues
- Validation rules
- Data contracts
Lab: Build a validation layer that quarantines bad rows
Course material for this module is in production.
M4 · Exploratory analysis and visualisation
10 h TOPICS
- EDA workflow
- Matplotlib and Seaborn
- Chart selection
- Communicating uncertainty
Lab: Produce a decision-oriented EDA report
Course material for this module is in production.
Real-world work scenario
Business reports a revenue dashboard is wrong. Trace the discrepancy through the join logic, discover duplicate keys inflating totals, quantify the impact, correct the pipeline, and write the incident summary for a non-technical stakeholder.
Assessment
Notebook rubric · data-quality practical