Excel for Data Analysis: PivotTables, Charts and Dashboards
Turn raw lists into answers: clean data, write analysis formulas, build PivotTables and charts, and finish with a one-page dashboard you can share.
- 18 lessons
- 5 h 15 min
- Certificate
Data and databases - Advanced
Data Analysis with Python: pandas and Charts
Use Python, pandas and matplotlib to load, clean, summarise and chart real-world data, then complete a full survey analysis project with ethical, privacy-aware habits.
About this course
Organisations in Uganda and around the world collect data every day: sales records, school results, survey answers, health clinic registers, mobile money logs. The people who can turn that raw data into clear answers are in demand. pandas is the most widely used Python library for this job, and matplotlib is the standard library for drawing charts.
This course follows the real workflow of an analyst: ask a question, load the data, clean it, summarise it, chart it and report it honestly. You will work in Jupyter notebooks or Google Colab, use small datasets that you can check by hand, and finish with a complete survey project. Each snippet shows the output you should see so that you always know whether your code is right.
The course assumes you have finished the Python introduction and can write variables, loops, lists, dictionaries and functions without help. Everything else is taught here. All code uses pandas 2.x syntax, and the lessons point out the few places where newer or older versions differ.
What you will learn
- Set up a Python environment and work in Jupyter notebooks or Google Colab.
- Load CSV data with pandas and inspect its shape, types and summary statistics.
- Select, filter, sort and create columns using loc, iloc and boolean masks.
- Clean data: handle missing values, duplicates, wrong types, messy text and outliers.
- Combine tables with merge and concat and check that the result is correct.
- Summarise data with groupby, aggregations, pivot tables and simple time series.
- Draw clear, honest bar, line, histogram and scatter charts with matplotlib and export your results.
- Complete an end-to-end analysis project and handle personal data ethically.
Course content
5 modules · 20 lessons · 5 h 25 min · 6 quizzes
Module 1: The workflow and the tools
Learn the steps of a data analysis, set up Python, Jupyter or Google Colab, meet NumPy arrays and read your first CSV file with pandas.
- The data analysis workflow 14 min
- Jupyter notebooks and Google Colab 14 min
- NumPy arrays in brief 12 min
- Reading a CSV file with pandas 16 min
- Quiz: 8 questions
Module 2: pandas basics
Work with Series and DataFrames, select data with loc, iloc and boolean masks, sort, add columns and manage data types.
- Series and DataFrames 14 min
- Selecting data with loc and iloc 16 min
- Filtering with boolean masks, and sorting 16 min
- New columns and data types 16 min
- Quiz: 8 questions
Module 3: Cleaning data
Find and handle missing values, duplicates, wrong types, messy text and outliers, then combine tables with merge and concat.
- Finding and handling missing values 18 min
- Duplicates, types and dates 16 min
- Cleaning text and spotting outliers 18 min
- Merging and concatenating tables 16 min
- Quiz: 8 questions
Module 4: Summarising and reshaping
Group and aggregate data, count categories, transform columns with apply and cut, build pivot tables and analyse simple time series.
- groupby and aggregations 18 min
- value_counts, apply and cut 15 min
- Pivot tables 14 min
- Dates and simple time series 16 min
- Quiz: 8 questions
Module 5: Visualising and reporting
Draw bar, line, histogram and scatter charts with matplotlib, label them honestly, export results, complete a survey project and handle data ethically.
- matplotlib basics: bar, line, histogram and scatter 20 min
- Honest charts: labels, scales and exporting 16 min
- Mini project: a youth digital skills survey 25 min
- Ethics and privacy of data 15 min
- Quiz: 8 questions
Final exam
30 questions. Pass with 70% or more to earn your certificate.
What you need
- Completion of Introduction to Programming with Python, or equivalent Python experience (variables, loops, lists, dictionaries, functions)
- A computer that can run Python, or a browser and a Google account for Google Colab
- Basic spreadsheet knowledge helps (see Excel for Data Analysis)
Further reading
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