Data Science Course
We provide different training opportunities based on your data science need and availability
We run short on-demand ad-hoc lectures on machine learning and data science targeted toward specific School, Institute or research group. The format is 1 hour of introduction to data science or a specific topic of interest followed by 1 hour for Q&A. If you would like us to organise one, please get in touch.
We run a day-long data science course (Day of Data Science) with multiple talks, hands-on sessions, and Q&A designed to give you a broad overview of state-of-the-art data science tools and how they can be incorporated in your research.
We run a more extensive introductory data science course (Data Science for Domain Scientists) available without cost to the University staff. The course is 7 weeks long with a 2 hour session every other week spanning over a semester. DS4DS is aimed at academics and researchers interested in applying machine learning to their research and developing funding bids with an element of data science and machine learning. We intend it to be a gateway to incorporating data science into your domain research rather than technical upskilling, and as such we mean to provide you with an eagles-eye view of the applied side of the field and real-life practical examples. DS4DS will give a broad overview of the standard machine learning pipeline and foundational concepts, a basic introduction to traditional techniques and most recent developments, and practical guidance on developing an interdisciplinary bid and engaging with the data and machine learning experts at the University. The next Data Science for Domain Scientists course will be offered in Jan'25-Apr'25. If you are interested in joining, please leave us your contact and we will reach out.
The second edition of the course will run between Jan'24 - May'24. Each session will include an overview of a topic, some sessions will also have a guest lecture or more participatory activity. The sessions are scheduled on alternating Thursdays, from 10am-12pm (except from the first session taking place on Tuesday).
| Week | Date | Topic 1 | Topic 2 | Venue** |
| 1 | 16 Jan '24 | Introduction and Orientation | Lecture 1: Archetypal ML project pipeline | 1 |
| 2 | 1 Feb '24 | Lecture 2: It's All About The Data I | Guest Lecture 1: Messy Data * | 1 |
| 3 | 22 Feb '24 | Lecture 3: It's All About The Data II | Elevator Pitches - Show off your data | 1 |
| 4 | 7 Mar '24 | Lecture 4: Machine Learning Modelling | Lecture 4: continued | 1 |
| 5 | 21 Mar '24 | Lecture 5: Model Evaluation and Criticism | Guest Lecture 2: Neural Networks * | 1 |
| 6 | 18 Apr '24 | Lecture 6: Computing and Best Practices | Data Science Group Discussion | 2 |
| 7 | 2 May '24 | Lecture 7: Starting Interdisciplinary Research | Data Science: Under the Hood | 1 |
* tentative topic subject to availability of lecturer
**Venue: 1 - G.03 Bayes Center; 2 - G.07 Informatics Forum
The first course will run between Sep'23 - Dec'23. Each session will include an overview of a topic (45 mins), a guest lecture (45 mins), and a group discussion (20 mins). The sessions are scheduled on alternating Thursdays, from 10am-12pm.
| Week | Date | Topic 1 | Topic 2 | Venue** |
| 1 | 21 Sep '23 | Introduction and Orientation | Lecture 1: Archetypal ML project pipeline | 1 |
| 2 | 5 Oct '23 | Lecture 2: Everything about Data | Guest Lecture 1: Messy Data (Chris Williams) | 1 |
| 3 | 19 Oct '23 | Lecture 3: Exploratory Data Analysis | Elevator Pitches - Show off your data | 2 |
| 4 | 2 Nov '23 | Lecture 4: Machine Learning Modelling | Guest Lecture 2: Generative AI (Lexi Birch-Mayne) | 2 |
| 5 | 16 Nov '23 | Lecture 5: Model Evaluation and Criticism | Guest Lecture 3: Explainable AI (Vaishak Belle) | 2 |
| 6 | 30 Nov '23 | Lecture 6: Computing and Best Practices | Guest Lecture 4: Digital Twin (Chris Dent) | 2 |
| 7 | 07 Dec '23 | Lecture 7: Starting Interdisciplinary Research | Guest Lecture 5: Causal Machine Learning (Sotirios Tsaftaris) | 2 |
* tentative topic subject to availability of lecturer
**Venue 1: G.07 Informatics Forum, Venue 2: G.03 Bayes Center