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Formative Assignment – Summary

Deadline: See Canvas
Submission Type: Formative (for feedback only, not graded)

Overview

This workbook introduces core Python and NumPy operations for data analysis. It consists of five exercises focused on data structures, computation, and dataset manipulation. Submit your completed workbook and Declaration of Authorship form to onlinemarking@conted.ox.ac.uk, following the file naming format:
CourseTitle_Assignment1_Surname.pdf

Contents

1. Python Lists

Practice computing statistical measures (mean, median, standard deviation) using only built-in Python functionality—no external libraries.

2. Dictionaries

  • Convert structured records into dictionaries for easier data handling.
  • Implement a simple search or filter to identify the record with the maximum numeric value (e.g., longest book).

3. NumPy and the Iris Dataset

Work with real-world tabular data using NumPy:

  • Import and separate numerical and categorical data.
  • Split arrays by category labels for analysis.

4. Statistical Analysis with NumPy

Compute summary statistics and correlations across groups to compare species characteristics and relationships between variables.

5. Derived Data Columns

Create new computed fields from existing NumPy data columns (e.g., calculating derived metrics such as “volume”).

Libraries Used

import numpy as np
import pandas as pd

Formatting and precision settings are applied for clean numerical output.

End of Workbook Summary