Data science in secondary computing
CO907 Online course
Data Science in Computing will help you build your confidence in understanding and teaching data science in secondary computing. During this course, you will explore different types of data, consider how to evaluate data quality and reliability, and learn how to clean and process real-world datasets. You will use Python to analyse and visualise data, explore machine learning, and consider the ethical implications of data and AI. You will also explore practical, cross-curricular examples and reflect on how you can bring data science into your classroom.
Skip to course bookingExplore data science within the computing and the wider curriculum. Throughout the course, you’ll investigate the data science process, from collecting and cleaning raw data through to analysing, modelling and using data to make meaningful decisions.
You’ll explore key concepts including data types, data quality, bias and reliability, as well as how data can be represented and visualised. You’ll also investigate how misleading visualisations and incomplete or biased datasets can influence the conclusions we draw.
Through practical activities using Python, you’ll develop your confidence in working with data and explore authentic data science problems that could be used in your classroom. You’ll also consider the links between data science, artificial intelligence and machine learning, including the ethical considerations that arise when data is used to train and inform models.
By the end of the course, you’ll have a clearer understanding of data science and how it can be embedded within the computing curriculum and connected to learning across other subjects.
Who is it for?
This course is aimed at teachers of Key Stage 3 and Key Stage 4 computing, including both experienced computing teachers and those who are newer to teaching data science. It is suitable for teachers with a range of programming experience, including non-specialists who want to develop their confidence in using and teaching data science concepts.
Topics covered
- What is data science? - Explore what data science is, different types of data, how data is used across subjects and the role of the ‘thread of truth’.
- Processing and cleaning data - Explore why data needs to be processed and cleaned, using real-world datasets such as weather data, and compare the benefits of spreadsheets and Python.
- Analysing data and testing hypotheses - Explore how data can be analysed and hypotheses tested, including aggregation and investigating outliers, with opportunities to embed these approaches across the curriculum.
- Presenting data for decision making - Explore how understanding your audience influences data presentation, identify misleading representations and consider how effective data presentation can support learning across the curriculum.
- Introduction to machine learning - Explore how artificial intelligence and machine learning use data, including how models are trained and the role of rules and algorithms.
- How machine learning handles large datasets - Explore how working with large datasets changes the tools and approaches you use, including the benefits of AI and machine learning and how large datasets can be used in the curriculum.
- Ethics, bias and responsible use - Explore the importance of ethics, bias and responsible data use, and consider how you can address these key issues when teaching young people about data.
How long is this course?
This course is approximately eight hours in duration. It is split into separate sections, enabling you to complete the course flexibly.
How will you learn?
This online, self-paced course can be completed flexibly. You can join and start this course at any time after the advertised date, and keep access for a year after booking.
Outcomes
By the end of this course, you will be able to:
- Understand the data science process: Explore how data is defined, collected, cleaned and analysed.
- Explore how machines learn from data: Understand how machine learning models identify patterns and differ from traditional programming.
- Develop critical data literacy skills: Identify how bias, inaccurate data and misleading visualisations can influence interpretation.
- Design meaningful classroom experiences: Develop age-appropriate, cross-curricular activities that help students evaluate data, findings and decisions.
This course is part of the KS3 and GCSE Computer Science subject knowledge certificate
Key stage 3 and GCSE Computer Science certificate
Our certificate is designed to help you develop or refresh your computer science subject knowledge.
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