Data Science Course | Data Science Unit

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, consisting of 7 sessions, 2 hours each. 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.

Data Science for Domain Scientists 04

The 4^th edition of DS4DS is scheduled for January – April 2026. The course has been redesigned to be primarily self-paced, but we will also provide weekly drop-in Q&A sessions and deliver a few practical and participatory sessions to consolidate the taught material. We will host a 30-min welcome / ice-breaker session on Teams on the 29^th of January at 10am.

What you’ll learn:

Introduction to DS4DS	Welcome and ice-breaker session	0.5h	29 Jan '26
Block 1: The pipeline to fit any (data) problem	What is Machine Learning and why it underpins Data Science, what (problems) is it good for (and what not), what is the blueprint framework for using it well	1.5-2h of self-paced learning	Drop-in session on 5 Feb '26
Block 2: Just a block of Data	What you ned to know about your data, how to better understand it & how to treat it well.	1.5-2h of self-paced learning	Drop-in sessions on 12 & 19 Feb '26
Block 3: Models may be wrong, how to make them useful?	What types of models are there and for doing what, how does the machine learn and how to be a good coach for it.	1.5-2h of self-paced learning	Drop-in sessions on 26 Feb & 5 Mar '26
Block 4: Runway vs reality	How to test your models for reality-readiness, what you need to know about the plumbing of the pipeline, and tips for planning your Data Science project.	1.5-2h of self-paced learning	Drop-in sessions on 12 & 19 Mar '26
Interactive 1: Under the Hood	A look into the workings of the Machine Learning pipeline, as seen through the eyes of the Data Scientists: a walk-through one of our research projects.	1h of interactive session	26 Mar '26
Interactive 2: Data Science Group Discussion	A participatory session where you select one of 4 challenging Data Science problems and work in a group to design a potential solution.	1-2h hours of participatory session	TBD
Interactive 3: TBD	Depending on demand, another interactive session around data & LLMs is available	1-2h hours of participatory session	TBD

Drop-in sessions will be running on Thursdays 10-11am, from 5^th February to 19^th March, inclusive. We are planning for Block 1 to have 1 drop-in, and Blocks 2-4 to have 2 each, as a rough guide. There is no expectation to attend, but we encourage synchronising with the schedule to make the most of the curriculum.

You can register for DS4DS 4 here.

Data Science for Domain Scientists 03

The third edition of the course ran between Jan'25 - Mar'25. The first four sessions were mostly taught material, giving an overview of the key components of a Machine Learning Pipeline, and the foundational concepts behind them. In the second part of the course, after a break, we moved to more interactive content, a look under the hood of a complex Data Science project, and practical advice on engaging in interdisciplinary research.

The sessions were scheduled on consecutive Tuesdays, from 10am-12pm (between 14 Jan '25 and 4 Mar '25 except 11 Feb'25). We delivered the course online.

Week	Date	Topic	Type	Venue
1	14 Jan '25	Introduction and Ice-breaker + Archetypal ML Pipeline	Taught, Interactive Ice-breaker	Online
2	21 Jan '25	It's All About The Data	Taught	Online
3	28 Jan '25	Machine Learning Modelling	Taught	Online
4	4 Feb '25	Model Evaluation and Criticism + Computing Best Practices + Computing and Entrepreneurship Guests*	Taught	Online
	11 Feb '25	(break)
5	18 Feb '25	Data Show-Off + (Don't) TeLL Me Lies - working with Large Language Models	Interactive	Online
6	25 Feb '25	Recap + Starting Interdisciplinary Research + Data Science: Under The Hood	Taught	Online
7	4 Mar '25	Data Science Group Discussion	Interactive	Online

* tentative, subject to availability

Data Science for Domain Scientists 02

The second edition of the course ran between Jan'24 - May'24. Each session included an overview of a topic, some sessions were supported by a guest lecture or more participatory activity. The sessions were scheduled on alternating Thursdays, from 10am-12pm (except for the first session that took 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

Data Science for Domain Scientists 01

The first course ran between Sep'23 - Dec'23. Each session included an overview of a topic (45 mins), a guest lecture (45 mins), and a group discussion (20 mins). The sessions were 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

This article was published on 2024-11-22