Data Carpentry - Data Cleaning & Organising with Python

This online course is designed to teach the fundamental data skills needed for anyone that deals with data and datasets, or anyone who would like to reskill in this area. The course will cover data organising with spreadsheets, data cleaning with OpenRefine, and data analysis with Python.

The course is highly interactive and delivered as practical ‘hands on’ workshops. . It provides foundational skills – no prior computational experience is needed. As these skills are applicable across many sectors, the workshop will be tailored to the backgrounds of attendees. The lesson materials are all available under a CC-BY licence and maintained by the community itself.

The demand for fundamental data skills is growing across all sectors and this course will equip learners with the foundations needed to work with data. Python is one of the most popular programming languages and is used in web development, desktop applications and machine learning as well as many other areas.

Participants will learn fundamental data skills needed for anyone who deals with data and datasets. They will learn how to organise data with spreadsheets, clean data with OpenRefine, and analyse data with Python. This is a hands-on course that teaches you how to identify, diagnose, and treat various data-cleaning problems. Throughout this practical workshop, you will work with real, messy data with the goal of answering some of your most commonly-asked data cleaning questions. These skills are highly sought after in many industries.

The instructors come from a range of backgrounds with sound experience in dealing with data.

Though there are no specific entry requirements, learners will be expected to have a basic knowledge of computers. You must be comfortable studying and learning in English if it is not your first language.

Better data-entry with spreadsheets; data cleaning with OpenRefine; data analysis and visualisation with Python.

TThis is a 3 day course, comprising a total of 12 hours study. This course will be delivered over 3 half days, from 10am-3pm online.

Course fees for 23/24 are £240 but funded places are available for people employed or unemployed in Scotland (residency requirements apply).

Funding

Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh.

Eligibility

Funded places are available to those who meet SFC fee waiver criteria:

“Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.”

If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria:

•  be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and
• have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and
• be ordinarily resident in Scotland at the course start date.

You can find out more about residency criteria on the SAAS website  or in this summary.

Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here: https://www.ed.ac.uk/tuition-fees/fee-status/work-out Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying.

Please note that full-time students (including full-time PhD students) are not eligible for funding.

You will receive a certificate of completion.