Beginner Pandas Course for Data Analysis in Uganda

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

• Create, inspect, and explain Pandas Series and DataFrame objects.
• Import CSV and Excel files, identify data types, and export clean results.
• Select, filter, sort, and update records safely with loc, iloc, masks, query, and assign.
• Clean missing values, duplicate records, inconsistent text, invalid numbers, and poorly formatted dates.
• Create useful business indicators with vectorised calculations and conditional categories.
• Summarise data with groupby, named aggregations, pivot tables, cross-tabulations, and rankings.
• Combine related tables with concat and merge while checking unmatched and duplicated keys.
• Produce clear charts and stakeholder-ready Excel or CSV reports.
• Explain findings in plain language and present a complete Uganda-focused portfolio notebook.

Modules

1. 1

   Pandas Setup and Essential Python for Data Analysis

   Choose Google Colab or a local Python environment and organise a clean project folder.Use variables, lists, dictionaries, functions, comparisons, and loops only where they add value.Import Pandas as pd, read error messages, use built-in help, and restart a notebook safely.Write readable notebook headings, comments, and result explanations for reproducible work.
2. 2

   Series, DataFrames, CSV and Excel Data

   Understand rows, columns, indexes, Series, DataFrames, labels, and data types.Load CSV and Excel files with read_csv and read_excel; control sheets, headers, and selected columns.Inspect shape, columns, head, tail, sample, info, describe, unique values, and value counts.Recognise numbers stored as text, inconsistent categories, unexpected blanks, and invalid dates.Save results with to_csv and to_excel without accidental index columns.
3. 3

   Selecting, Filtering and Sorting Records

   Select columns, rows, and individual values with brackets, loc, and iloc.Filter with comparisons, multiple conditions, isin, between, notna, and string matching.Use query for readable business rules and sort_values for ranked outputs.Reset, set, and understand indexes without confusing labels with row positions.Update values safely and avoid chained-assignment mistakes under Pandas 3.x.
4. 4

   Cleaning Messy Uganda Business and Survey Data

   Standardise column names, spaces, capitalisation, phone-like text fields, and category labels.Detect, explain, fill, or remove missing values using business-aware rules.Find exact and rule-based duplicates and preserve a record of what changed.Convert text to numeric, date, Boolean, and category data types with controlled error handling.Use replace, map, string methods, and date methods for dependable cleaning.Apply simple range, uniqueness, and allowed-value checks after cleaning.
5. 5

   Transforming Data and Creating Useful Indicators

   Create columns with arithmetic, assign, where, mask, map, cut, and qcut.Calculate revenue, margin, growth, utilisation, arrears, completion, and rate indicators.Use vectorised operations instead of slow row-by-row loops.Handle categories in an intentional order and label bands clearly.Check denominators, units, currency fields, and impossible values before interpreting ratios.
6. 6

   GroupBy, Pivot Tables and Business Summaries

   Apply split-combine logic with groupby and named aggregations.Calculate totals, averages, medians, counts, distinct counts, minimums, and maximums.Create pivot tables and cross-tabulations for district, branch, product, gender, or programme reporting.Add shares, percentages, rankings, and contribution analysis.Reshape summary outputs into a format suitable for decision-makers.
7. 7

   Combining Tables, Visualising Results and Exporting Reports

   Append similar files with concat and combine related tables with merge.Choose join keys and join types; use validate and indicator to detect data loss or duplication.Create bar, line, and distribution charts that answer a specific question.Label plots clearly and avoid misleading scales, clutter, and unsupported conclusions.Export clean detail sheets, KPI summaries, and charts for stakeholder review.
8. 8

   Capstone: Uganda Business Performance Analysis

   Define a decision-focused question and document the dataset source and limitations.Profile, clean, validate, transform, summarise, and visualise a realistic Uganda dataset.Create at least five decision-relevant indicators and two clearly labelled charts.Present findings, limitations, and recommended actions without overstating the evidence.Package the notebook, cleaned data, output report, and short project README.