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Learn to code for data analysis

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Please note: Learners have reported issues with the software and quizzes within this course.

Software and data make the world go round. Learn programming, to analyse and visualise open data, with this free course, Learn to code for data analysis.

This course will teach you how to write your own computer programs, one line of code at a time. You'll learn how to access open data, clean and analyse it, and produce visualisations. You will also learn how to write up and share your analyses, privately or publicly.

You'll code in Python, a widely used programming language across all disciplines.

You will write up analyses and do coding exercises using the popular Jupyter Notebooks platform, which allows you to see immediately the result of running your code and helps you identify – and fix – any errors more easily.

You will also look at real data from the World Health Organisation, the World Bank and other organisations.

Transcript

Enrolling in the course will give you the opportunity to earn an Open University digital badge. Badges are not accredited by The Open University but they're a great way to demonstrate your interest in the subject and commitment to your career and to provide continuing professional development.

Once you're signed in, you can manage your digital badges online from My OpenLearn. In addition, you can download and print your OpenLearn Statement of Participation – which also displays your Open University badge.

The Open University would appreciate a few minutes of your time to tell us about yourself and your expectations for the course before you begin, in our optional start-of-course survey . Once you complete the course we would also value your feedback and suggestions for future improvement, in our optional end-of-course survey . Participation will be completely confidential and we will not pass on your details to others.

This course is accredited by the CPD Standards Office . It can be used to provide evidence of continuing professional development and on successful completion of the course you will be awarded 24 CPD points. Evidence of your CPD achievement is provided on the free Statement of Participation awarded on completion.

Anyone wishing to provide evidence of their enrolment on this course is able to do so by sharing their Activity Record on their OpenLearn Profile, which is available before completion of the course and earning of the Statement of Participation.

Earn this free Open University digital badge if you complete this course! The badge can be displayed, shared and downloaded as a marker of your achievement. The badge is awarded for completing the course and passing the quizzes.

Course learning outcomes

After studying this course, you should be able to:

understand basic programming and data analysis concepts
recognise open data sources as a public resource
use a programming environment to develop programs
write simple programs to analyse large bodies of data and produce useful results.

Enter course

First Published: 15/10/2018

Updated: 20/01/2020

You can start this course right now without signing-up. Click on any of the course content sections below to start at any point in this course.
If you want to be able to track your progress, earn a free Statement of Participation, and access all course quizzes and activities, sign-up.

Course content

Introduction and guidance
- Current section:
  Introduction and guidance
  Welcome to this badged open course, Learn to code for data analysis.The free course lasts eight weeks, with approximately three hours of study each week. You can work through the course at your own pace, so if you have more time one week there is no problem with pushing on to complete another week’s study.This course will teach you how to write your own computer programs, one line of code at a time. You'll learn how to access open data, clean and analyse it, and produce visualisations. You ...
- What is a badged course?
- How to get a badge
- Acknowledgements
Week1Week 1: Having a go at it Part 1
- Current section:
  Introduction
  Please note: A number of learners have reported issues with the software and quizzes within this course. We will be carrying out a review and updating the material where necessary. Additionally, a link to a supplementary FAQs page will be added here soon.Welcome to Learn to code for data analysis! This course has been written to show how a little bit of code can go a long way in making sense of the increasingly vast amounts of open data available online.If you’re used to the manual approach ...
- 1 Install the software
- 2 This week’s quiz
- 3 Summary
- Acknowledgements
- Week 1 practice quiz
Week2Week 2: Having a go at it Part 2
- Current section:
  1 Enter the pandas
  As you probably realised, this way of coding is not practical for large scale data analysis.Three lines of code were required for each country, to store the number of deaths, store the population, and calculate the death rate. With roughly 200 countries in the world, my trivial analysis would require 400 variables and typing almost 600 lines of code! Life’s too short to be spent that way.Instead of using a separate variable for each datum, it is better to organise data as a table of rows and...
- 2 Writing up the analysis
  - 2.1 Practice project
  - 2.2 Sharing your project notebook
- 3 This week’s quiz
- 4 Summary
  - 4.1 Week 1 and 2 glossary
- Acknowledgements
- Week 2 practice quiz
Week3Week 3: Cleaning up our act Part 1
- Current section:
  Introduction
  Welcome to Week 3.Please note: in the following video, where reference is made to a study ‘week’, this corresponds to Weeks 3 and 4 of this course.In Week 1 and 2 you worked on a dataset that combined two different World Health Organization datasets: population and the number of deaths due to tuberculosis.They could be combined because they share a common attribute: the countries. This week you will learn the techniques behind the creation of such a combined dataset.
- 1 Weather data
- 2 This week’s quiz
- 3 Summary
- Acknowledgements
- Week 3 practice quiz
Week4Week 4: Cleaning up our act Part 2
- Current section:
  1 Loading the weather data
  You have learned some more about Python and the pandas module and tried it out on a fairly small dataset. You are now ready to explore a dataset from the Weather Underground.Open the file London_2014.csv and save it in the disk folder or CoCalc project you created in Week 1.Do not be tempted to open this file with Excel as this application will attempt to localise the data in the file, i.e. use your country’s local data formats, which will make much of what follows rather incomprehensible! ...
- 2 Every picture tells a story
  - 2.1 Changing a dataframe’s index
  - 2.2 The project
- 3 This week’s quiz
- 4 Summary
- 4.1 Week 4 glossary
- Acknowledgements
- Week 4 compulsory badge quiz
Week5Week 5: Combine and transform data Part 1
- Current section:
  Introduction
  Welcome to Week 5. Please note: in the following video, where reference is made to a study ‘week’, this corresponds to Weeks 5 and 6 of this course.In Week 1 you worked on a dataset that combined two different World Health Organization datasets: population and the number of deaths due to tuberculosis.They could be combined because they share a common attribute: the countries. This week you will learn the techniques behind the creation of such a combined dataset.
- 1 Life expectancy project
- 2 This week’s quiz
- 3 Summary
- Acknowledgements
- Week 5 practice quiz
Week6Week 6: Combine and transform data Part 2
- Current section:
  1 Joining left, right and centre
  Let’s take stock for a moment. There’s the original, unchanged table (with full country names) about the life expectancy: In []: life Out[]: Country nameLife expectancy (years)0China751Russia712United States793India664United Kingdom81… and a table with the GDP in millions of pounds and also full country names. In []: gdp Out[]: Country nameGDP (£m)0United Kingdom17117271United States107160292China59052023Brazil14351484South Africa233937Both tables have a common column with a common name ...
- 2 Correlation
  - 2.1 Scatterplots
  - 2.2 My project
- 3 This week’s quiz
- 4 Summary
  - 4.1 Weeks 5 and 6 glossary
- Acknowledgements
- Week 6 practice quiz
Week7Week 7: Further techniques Part 1
- Current section:
  Introduction
  Welcome to Week 7 Please note: in the following video, where reference is made to a study ‘week’, this corresponds to Weeks 7 and 8 of this course.In Week 6 you saw how to merge two datasets containing a common column to create a single, combined dataset. Combining datasets allows us to make comparisons across datasets, as you discovered when looking for correlations between GDP and life expectancy.This week, you’ll learn how to go the other way, separating out distinct ‘subsets’ or groups ...
- 1 I spy with my little eye
- 2 This week’s quiz
- 3 Summary
- Acknowledgements
- Week 7 practice quiz
Week8Week 8: Further techniques Part 2
- Current section:
  1 The split-apply-combine pattern
  In the exercise in Week 7, you downloaded data from Comtrade that could be described as ‘heterogenous’ or mixed in some way. For example, the same dataset contained information relating to both imports and exports.To find the partner countries with the largest trade value in terms of exports means filtering the dataset to obtain just the rows containing export data and then ranking those. Finding the largest import partner requires a sort on just the import data.But what if you wanted to ...
- 2 Pivot tables
- 3 This week’s quiz
- 4 Summary
  - 4.1 Week 7 and 8 glossary
  - 4.2 What next?
- Tell us what you think
- Acknowledgements
- Week 8 compulsory badge quiz

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Alfha Hikmah 8 May 2025 6:18PM

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This is a great course as I gain more knowledge from it

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Joseph Francis 27 April 2025 6:14PM

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first quiz's are totally off and the descriptions are totally bonkers to follow. its like reading riddles

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Learn to code for data analysis

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