NumPy Fundamentals for Python Data Analysis in Uganda & East Africa

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• By the end of this beginner NumPy course, the learner will be able to:
• Explain how NumPy supports Python data analysis, data science, and machine learning.
• Create one-dimensional and multidimensional NumPy arrays from Python data.
• Inspect array shape, size, dimensions, data types, and memory-related properties.
• Select, slice, filter, reshape, combine, split, sort, and transform array data.
• Replace slow manual calculations with clear element-wise NumPy operations.
• Calculate totals, averages, minimums, maximums, percentiles, variance, and standard deviation.
• Identify and handle missing or invalid numeric values with appropriate NumPy techniques.
• Load, analyse, and save simple CSV and text-based datasets.
• Use NumPy to answer practical questions from sales, agriculture, education, transport, and mobile-money data.
• Build a portfolio-ready Python data-analysis project and communicate the findings clearly.

Modules

1. 1

   Python and NumPy for Data Analysis

   What NumPy is and why analysts, researchers, and data scientists use it.Installing or opening a ready-to-use Python notebook environment.Importing NumPy with the standard `np` alias.Python numbers, lists, variables, operators, conditions, and functions needed for NumPy.Understanding the difference between Python lists and NumPy arrays.
2. 2

   Creating and Understanding NumPy Arrays

   Creating arrays with `array`, `arange`, `linspace`, `zeros`, `ones`, `full`, and identity-matrix functions.One-dimensional, two-dimensional, and higher-dimensional arrays.Array properties: `shape`, `size`, `ndim`, `dtype`, and `itemsize`.Choosing suitable numeric data types.Creating reproducible sample data for exercises.
3. 3

   Indexing, Slicing, and Filtering Data

   Accessing individual values, rows, columns, and blocks.Positive and negative indexing.Slicing one-dimensional and multidimensional arrays.Boolean masks and conditional filtering.Finding values that match multiple conditions.Avoiding common copy-versus-view mistakes.
4. 4

   Fast Calculations with Array Operations

   Element-wise addition, subtraction, multiplication, division, powers, and comparisons.Operator precedence and safe calculations.Universal functions for rounding, absolute values, square roots, logarithms, and exponentials.Understanding vectorised calculations without manual loops.Practical calculations using prices, quantities, revenues, costs, and percentage changes.
5. 5

   Reshaping, Combining, and Sorting Arrays

   Reshaping, flattening, transposing, and changing axes.Joining arrays with concatenation and stacking.Splitting arrays into useful sections.Sorting values and retrieving sorted positions.Identifying unique values and counts.Cleaning inconsistent array shapes.
6. 6

   Summary Statistics and Missing Values

   Sums, means, medians, minimums, maximums, ranges, percentiles, variance, and standard deviation.Calculations across rows, columns, and axes.Recognising `NaN`, infinity, and invalid values.Using `isnan`, `isfinite`, `nanmean`, and related functions.Interpreting results responsibly instead of reporting numbers without context.
7. 7

   Loading and Saving African Data

   Importing simple CSV and text data with NumPy.Selecting delimiters, skipping headers, and choosing columns.Saving cleaned arrays and analysis outputs.Validating data before analysis.Working with sample datasets for market prices, school performance, rainfall, transport fares, and retail sales.
8. 8

   Beginner Capstone — Kampala Retail and Mobile-Money Performance

   Import a practical business dataset containing branches, transactions, products, costs, and sales.Clean numeric errors and missing values.Calculate revenue, profit, transaction size, product performance, and branch comparisons.Use filters and summary statistics to answer business questions.Produce a well-organised notebook with findings, limitations, and recommendations.