![Graphic mashup - Calton Hill, Edinburgh-8002418 [Chris Close]](/files/styles/large_16x9/public/2023-03/SummerSchool_Header_3_0.png?itok=IUoo2NM5)
This course is designed for researchers who are complete beginners with no prior knowledge of coding and data analysis. Through lectures and exercises, attendees will learn how to code in Python, starting from core concepts such as variables and loops, through to coding live data visualisation.
The course has a strong practical focus, with all coding happening in student pairs with support from teachers and instructors.
This course explores the basics of programming: variables, functions, loops, operating on data structures, data wrangling, visualisation, and publishing to the web. By the end of the course, attendees will understand how to bridge the gap between humans and computers, and how to apply the skills they have learnt to their own data analysis and research.
No previous coding experience is required.
Encourage collaboration with other students to achieve goals and tasks. Instructors will be available in the room to help.
The course combines mini-lectures, guided coding examples and programming challenges.
Students will work towards describing and visualising data.
Provides the foundations needed to continue your coding journey through self-study.
All coding will happen in student pairs, with support from teachers. Attendees will learn to communicate and solve problems together.
The course examines data from social science, humanities, and health and social care.
In this short video, the course organiser, Dr. Pawel Orzechowski, outlines the objectives behind the course and the learning outcomes. The skills gained on this course will be a great foundation for your journey towards analysing Humanities and Social Sciences research data.
Writing code - running code
In the first day we will explore the basic concepts in coding, focusing on areas that computers are great at:
Making simple data simple
Next we will look at the basic elements of using data:
Applying your skills
We will learn about visualisation techniques and use what we have learned in previous days to look at real data and solve a number of challenges.
Wrangling, manipulating and cleaning data
We will see the final components of coding: loops and advanced data types (Pandas, NumPy). We will also complete more challenges. This will be an opportunity to specialise in one type of data: text, time, or location.
Sharing your code with the world
In the final day we will learn how to build and publish interactive dashboards. At the end of the day attendees will be able to share their Shiny dashboard on GitHub pages.
| Monday | Tuesday | Wednesday | Thursday | Friday | |
| 09:00-09:30 | Registration | ||||
| 09:30-09:40 | Welcome | Setting Up | Setting Up | Setting Up | Setting Up |
| 09:40-10:40 | Seminar | Seminar | Seminar | Seminar | Seminar |
| 10:40-11:00 | Coffee | Coffee | Coffee | Coffee | Coffee |
| 11:00-12:30 | Variables | Tests | Graphs | Loops | Shiny |
| 12:30-13:30 | Lunch | Lunch | Lunch | Lunch | Lunch |
| 13:30-15:00 | Conditionals | Collections | Data API | NumPy | Portfolio |
| 15:00-15:30 | Coffee | Coffee | Coffee | Coffee | Coffee |
| 15:30-17:00 | Functions | Data Filters | Project | Keynote | Next Steps |
| Evening | Pub Quiz | Pub Crawl | Ceilidh | Drinks Reception | Dinner |
Lecturer in Programming for Business. Pawel teaches programming at the Business School, Edinburgh Futures Institute, and the Usher Institute. Building on years of experience in the tech industry and teaching in coding bootcamps, Pawel will help you kickstart your coding journey.
Karim Rivera is a PhD in Psychology with a passion for programming and statistics. She has been teaching statistics using R and Python for over five years to students from different backgrounds.
Chris is a PhD mathematics student with the MAC-MIGS Centre of Doctoral Training, who is affiliated with the Institute of Genetics and Cancer. He loves teaching individuals on how to get the most out of ‘big data’ by using data analysis techniques appropriately and accurately, and most importantly how to implement this in Python.
Dr Beatrice Alex is Senior Lecturer and Chancellor’s Fellow at the Edinburgh Futures Institute and the School of Literatures, Languages and Cultures.
She leads the Edinburgh Language Technology Group and the Edinburgh Clinical NLP Group and her research is centred around text mining and its applications to different domains.
Dr Clare Llewellyn: Lecturer in Governance, Data and Technology at the School of Social and Political Science and Edinburgh Futures Institute at the University of Edinburgh. Clare brings experience in computational social science, specifically transdisciplinary expertise in the computational methods of analysing digital media. In her high-impact research, she is adept at describing and engaging with technological detail at the highest levels and making digital media analysis engaging and accessible. Her research has reached both policymakers and the public at large raising awareness on the use and misuse of social media in political campaigning.
Melissa Terras is Professor of Digital Cultural Heritage at the University of Edinburgh‘s College of Arts, Humanities, and Social Sciences.
Her research focuses on the digitisation of cultural heritage, including its technologies, procedures, and impact, and how this intersects with internet technologies. She was the founding director of the Centre for Data, Culture, & Society.
