Learn to Code for Data Analysis
Learn to code in Python using Jupyter Notebook. Use your new programming skills to analyse and visualise open data.
Duration
4 weeksWeekly study
5 hours
Learn to Code for Data Analysis
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Learn to code in Python and analyse real, open data
This hands-on 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 it and analyse it, and produce visualisations. You will also learn how to write up and share your analyses, privately or publicly.
You will install free software to learn to code in Python, a widely used programming language. You will write up analyses and do coding exercises using the popular Jupyter Notebook platform. And you will look at real data from the World Health Organisation, the World Bank and other organisations.
What topics will you cover?
- Python: variables, assignments, expressions, basic data types, if-statement, functions
- Programming: using Jupyter Notebooks, writing readable and documented code, testing code
- Data analysis: using pandas to read CSV and Excel files, to clean, filter, partition, aggregate and summarise data, and to produce simple charts
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Demonstrate an understanding of basic programming concepts.
- Using a programming environment to develop programs.
- Develop an awareness of open data sources as a public resource.
- Produce and write simple programs to analyse large bodies of data and produce useful results.
Who is the course for?
The course does not assume prior experience in programming or data analysis. Basic familiarity with a spreadsheet application will be an advantage.
The course does not require any knowledge of statistics, but you need to have basic numeracy skills, like writing arithmetic expressions, using percentages and understanding scientific notation. If you wish to brush up on your numeracy skills, we recommend the FutureLearn course Basic Science: Understanding Numbers from The Open University.
What software or tools do you need?
To study this course you will use specialist software. You can use the software online, via a free account on a website, or offline, by downloading and installing a free software package. You will receive instructions about both options via email before the course starts. The online solution requires a good internet connection and has some limitations.
The offline software has no limitations and is the recommended option. However, you will need access to a desktop or laptop computer on which you can install software. The software is free and there are versions available for Windows, Mac and Linux platforms. You will need about 3 GB of free disk space to download and install the software, and to store datasets that will be provided in the course.
Whether you choose the online or offline software option, you will need to be proficient in basic computer tasks, like creating folders, downloading files and copying them to specific folders, etc. In terms of accessibility, you will be asked to use your web browser and to type code.
What do people say about this course?
"Excellent course, being a developer for a number of years it gave me insight into manipulating large sets of data and as I am taking other courses related to data visualization I am looking forward to new technologies. Thanks!"
"I wanted to say to the course designers and mentors that I enjoyed the course. I think it was well structured and a nice, progressive pace and degree of difficulty. There were a few moments where details and syntax seemed to escape me, but Chris offered encouragement and that was a benefit of the group-chat-discuss approach. It showed me a tool and its application to dice and slice and make some sense out of "spreadsheet type" data. I have a data base background -no or very limited Excel- and found the course different and useful. It's a challenge for a retiree to take on a new subject and tools, but I'm glad to have done it successfully."
Who will you learn with?
Michel does research on software maintenance. He likes producing OERs (www.open.edu/openlearn/profiles/mw4687), looking at data visualisations and playing German-style board games.
My research is on designing integrated programming / learning environments for students new to programming.
Open University academic and open data geek, with interests in data visualisation, data journalism and open education. Regular blogger at blog.ouseful.info
Who developed the course?
The Open University
As the UK’s largest university, The Open University (OU) supports thousands of students to achieve their goals and ambitions via supported distance learning, helping to fit learning around professional and personal life commitments.
Established
1969Location
Milton Keynes, UKWorld ranking
Top 510Source: Times Higher Education World University Rankings 2020
Learning on FutureLearn
Your learning, your rules
- Courses are split into weeks, activities, and steps to help you keep track of your learning
- Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
- Stay motivated by using the Progress page to keep track of your step completion and assessment scores
Join a global classroom
- Experience the power of social learning, and get inspired by an international network of learners
- Share ideas with your peers and course educators on every step of the course
- Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others
Map your progress
- As you work through the course, use notifications and the Progress page to guide your learning
- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
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