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 03The 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.WeekDateTopicTypeVenue114 Jan '25Introduction and Ice-breaker + Archetypal ML PipelineTaught, Interactive Ice-breakerOnline221 Jan '25It's All About The DataTaughtOnline328 Jan '25Machine Learning ModellingTaughtOnline4 4 Feb '25Model Evaluation and Criticism + Computing Best Practices + Computing and Entrepreneurship Guests*TaughtOnline 11 Feb '25(break) 518 Feb '25Data Show-Off + (Don't) TeLL Me Lies - working with Large Language ModelsInteractiveOnline625 Feb '25Recap + Starting Interdisciplinary Research + Data Science: Under The HoodTaughtOnline7 4 Mar '25Data Science Group DiscussionInteractiveOnline* tentative, subject to availabilityData Science for Domain Scientists 02The 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).WeekDateTopic 1Topic 2Venue**116 Jan '24Introduction and OrientationLecture 1: Archetypal ML project pipeline121 Feb '24Lecture 2: It's All About The Data IGuest Lecture 1: Messy Data *1322 Feb '24Lecture 3: It's All About The Data IIElevator Pitches - Show off your data147 Mar '24Lecture 4: Machine Learning ModellingLecture 4: continued1521 Mar '24Lecture 5: Model Evaluation and CriticismGuest Lecture 2: Neural Networks *1618 Apr '24Lecture 6: Computing and Best PracticesData Science Group Discussion272 May '24Lecture 7: Starting Interdisciplinary ResearchData Science: Under the Hood1* tentative topic subject to availability of lecturer**Venue: 1 - G.03 Bayes Center; 2 - G.07 Informatics ForumData Science for Domain Scientists 01The 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.WeekDateTopic 1Topic 2Venue**121 Sep '23Introduction and OrientationLecture 1: Archetypal ML project pipeline125 Oct '23Lecture 2: Everything about DataGuest Lecture 1: Messy Data (Chris Williams)1319 Oct '23Lecture 3: Exploratory Data AnalysisElevator Pitches - Show off your data242 Nov '23Lecture 4: Machine Learning ModellingGuest Lecture 2: Generative AI (Lexi Birch-Mayne)2516 Nov '23Lecture 5: Model Evaluation and CriticismGuest Lecture 3: Explainable AI (Vaishak Belle)2630 Nov '23Lecture 6: Computing and Best PracticesGuest Lecture 4: Digital Twin (Chris Dent)2707 Dec '23Lecture 7: Starting Interdisciplinary ResearchGuest 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