BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//SWDTP - ECPv6.15.16.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:SWDTP
X-ORIGINAL-URL:https://www.swdtp.ac.uk
X-WR-CALDESC:Events for SWDTP
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260205T100000
DTEND;TZID=Europe/London:20260206T170000
DTSTAMP:20260430T150225
CREATED:20250819T142729Z
LAST-MODIFIED:20250819T142729Z
UID:10000528-1770285600-1770397200@www.swdtp.ac.uk
SUMMARY:Bayesian Statistics for Applied Research
DESCRIPTION:This two-day course provides a practical and accessible introduction to Bayesian statistics for applied research in any field. Students will benefit from a combination of lectures and discussion to explore fundamental concepts unlocking the potential to design bespoke statistical analyses based on your data and hypotheses as well as practical exercises to gain hands-on experience implementing Bayesian models using free and open-source software. The course is designed as a springboard to overcome the steepest part of the Bayesian learning curve with an immersive two-day deep-dive. \nStudents will have the opportunity to schedule a follow-up Bayesian surgery appointment (30 mins in-person or remote) for one-to-one engagement (or small groups\, as preferred) with the course instructor to answer burning questions that remain and/or to troubleshoot technical challenges related to their own research applications. \nPre-requisites: Students will need good programming skills in R and a basic understanding of linear regression to be successful in this course. \n  \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \n  \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/bayesian-statistics-for-applied-research/
LOCATION:London School of Economics and Political Science
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260127T103000
DTEND;TZID=Europe/London:20260127T120000
DTSTAMP:20260430T150225
CREATED:20250930T145717Z
LAST-MODIFIED:20250930T145804Z
UID:10000552-1769509800-1769515200@www.swdtp.ac.uk
SUMMARY:Philosophy of Social Science
DESCRIPTION:This talk will draw upon Alexander Betts’ recent book Social Science: A Very Short Introduction to offer a contemporary take on the philosophy of social science. It will focus in particular on the underpinnings of interdisciplinary social science\, arguing that across disciplines\, the social sciences have more in common than that which divides them.  \nWhere: Hybrid | GUDTP Hub \nWhen: 27.01.2026|10:30-12:00 \nAdvert & Registration: https://granduniondtp.web.ox.ac.uk/event/philospophy-of-social-science \nThis event is not organised by the SWDTP. Please direct enquiries to the Grand Union DTP: paula.sheppard@anthro.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/philosophy-of-social-science/
LOCATION:Online
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260127T090000
DTEND;TZID=Europe/London:20260128T170000
DTSTAMP:20260430T150225
CREATED:20251104T160046Z
LAST-MODIFIED:20251104T160046Z
UID:10000557-1769504400-1769619600@www.swdtp.ac.uk
SUMMARY:Qualitative Research Symposium - Applications & Call for Papers
DESCRIPTION:The Centre for Qualitative Research looks forward to inviting you all to the University of Bath to ponder important questions around participation\, access and inclusion in qualitative research. \n  \nIf you would like to attend or apply for the call\, check out the link below.
URL:https://www.swdtp.ac.uk/event-calendar/qualitative-research-symposium-applications-call-for-papers/
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260122T130000
DTEND;TZID=Europe/London:20260122T140000
DTSTAMP:20260430T150225
CREATED:20251205T142213Z
LAST-MODIFIED:20260113T142404Z
UID:10000561-1769086800-1769090400@www.swdtp.ac.uk
SUMMARY:Analysing large-scale assessment data
DESCRIPTION:Issues with using police data to investigate offending: A research perspective\nDr Ioana Crivatu\, Research Fellow at the University of Birmingham\nDr Ruth Spence\, Senior Research Fellow at Middlesex University \nPolice data is an important source of information for researchers about investigations\, suspects\, and victims. However\, crime records can be problematic to work with. Here we outline three key issues along with our approach in combining and quantitatively analysing police data from several police forces in England and Wales which used different crime recording systems. We discuss data quality\, which reflects missing and misclassified values; inconsistency\, which refers to the vague and at times different definitions provided; and granularity\, which reflects the lack of detailed information included in the datasets. We recommend developing a robust strategy for working with missing data\, triangulating across different sources\, creating higher-order categories where necessary\, and creating a detailed data governance plan before analysis begins. \nLink to published paper: https://journals.sagepub.com/doi/full/10.1177/0032258X251313944 \n  \nPreparation of a Large-scale Assessment in Education and its use in a Quantitative Intersectional analysis in R\nDr Natalia López-Hornickel\, Postdoctoral Research Associate at Roehampton University; SWDTP alumni\nIn this presentation\, first\, I aim to show the considerations and challenges of preparing large-scale assessment data\, using the International Civic and Citizenship Education Study (ICCS) from 2016. This includes the sources of the data and the merging process\, which is usually an overlooked but crucial step before proceeding with the analysis. Second\, I will refer to the analysis steps to obtain descriptives and models. Particularly\, I will use the case of the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to develop an intersectional analysis of students’ endorsement of the gender equality scale (Fifth paper of my thesis). This technique is a parsimonious alternative to multiplicative terms in regressions. \nAll the explanations will be conceptual and also accompanied by a description of some R syntax. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/analysing-large-scale-assessment-data/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260120T130000
DTEND;TZID=Europe/London:20260120T140000
DTSTAMP:20260430T150225
CREATED:20251104T151112Z
LAST-MODIFIED:20260113T142409Z
UID:10000556-1768914000-1768917600@www.swdtp.ac.uk
SUMMARY:What is Research Data? Practical Guidance on Organising and Sharing your Files and Findings
DESCRIPTION:Every research project generates data. It’s the material that you gather\, create\, or interpret to answer your research questions; whether that is numbers\, images\, recordings\, or documents. Good research data management and sharing are essential for making your work accessible\, your methods transparent\, and your findings easy to use and build upon. Funders\, publishers\, and universities require researchers to share and cite their research data – but what does this look like in practice? This webinar offers practical tips and guidance to organise\, store and share your documents and results effectively throughout your project. This session covers: \n\nWhat is research data and why it matters\nThe expectations of funders\, publishers\, and universities for research data storage and sharing\nHow to organise and describe your files so you can easily find and understand your research data throughout your project\nHow to ethically share research data when working with human participants\nHow to find a suitable research data repository for your work\nWhat support is available beyond your supervisory team\n\nThe speaker for this session is Dr Jade Godsall who is an Assistant Research Support Librarian in Research Data Management and Digital Scholarship at The University of Bristol. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/what-is-research-data-practical-guidance-on-organising-and-sharing-your-files-and-findings/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251210T100000
DTEND;TZID=Europe/London:20251211T160000
DTSTAMP:20260430T150225
CREATED:20250930T145538Z
LAST-MODIFIED:20250930T145538Z
UID:10000551-1765360800-1765468800@www.swdtp.ac.uk
SUMMARY:From Theory to Practice: Participatory Methods for Doctoral Students
DESCRIPTION:Are you interested in making your research more inclusive\, impactful\, and grounded in lived experience? This two-day interactive workshop delivered by Dr Ben Scher\, introduces doctoral students to the theory and practice of participatory research methods. \nWhere: In-person |6 Worcester St\, Oxford OX1 2BX \nWhen: 10.12.2025 & 11.12.2025|10:00-16:00 \nAdvert & Registration:  https://granduniondtp.web.ox.ac.uk/event/from-theory-to-practice-participatory-methods-for-doctoral-students-2  \nThis event is not organised by the SWDTP. Please direct enquiries to the Grand Union DTP: paula.sheppard@anthro.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/from-theory-to-practice-participatory-methods-for-doctoral-students-2/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251205T093000
DTEND;TZID=Europe/London:20251205T123000
DTSTAMP:20260430T150225
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000541-1764927000-1764937800@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-05/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251204T093000
DTEND;TZID=Europe/London:20251204T123000
DTSTAMP:20260430T150225
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000540-1764840600-1764851400@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-04/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251203T143000
DTEND;TZID=Europe/London:20251203T173000
DTSTAMP:20260430T150225
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000539-1764772200-1764783000@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-03/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251128T093000
DTEND;TZID=Europe/London:20251128T123000
DTSTAMP:20260430T150225
CREATED:20250829T091053Z
LAST-MODIFIED:20250829T091053Z
UID:10000538-1764322200-1764333000@www.swdtp.ac.uk
SUMMARY:Panel Data and Factor Model for Social and Economic Research
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Panel Data and Factor Model for Social and Economic Research  \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThere is huge body of literature applying panel data techniques using firm-level\, consumer\,  stock market and banking data. In this course\, we will present most important panel data techniques for stationary and nonstationary panels. We will discuss the importance of modelling heterogeneity and we will discuss static and dynamic models\, introducing the crucial distinction between fixed and random effects. The course will also provide a short introduction to both factor models and principal components. Practical applications using economic and financial (stocks\, interest rates) and banking (accounting) datasets will be delivered using Stata\, which is the most comprehensive econometric software for dealing with panel data analysis. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use Stata19 during the session. Participants are required to be familiar with the software and have it installed . \n  \nContent Outline \n\nStatic Panel Data Models\nDynamic Panel Data Models.\nNonstationary Panel Data Models.\nCross-sectional dependence in Panel Data.\nIntroduction of factor models\n\nLearning Objectives \n\nYou will learn how to handle and summarise panel datasets.\nYou will learn a large number of panel data techniques for stationary and nonstationary variables.\nYou will learn how to implement panel data analysis using econometric software.\n\nMain References \nA list of relevant papers will be provided at the beginning of the course. The following textbooks are recommended: \n\nBaltagi\, B. H. (2008)\, Econometric analysis of panel data\, Forth Edition\, John Wiley & Sons.\nBaltagi\, B. H. (2009)\, A companion to econometric analysis of panel data\, John Wiley & Sons.\n\n\nBoffelli\, S.\, and G. Urga (2016). Financial Econometrics Using Stata. Stata Press Publication\nBrooks\, C.\, (2019). Introductory Econometrics for Finance\, Cambridge University Press\, 4th edition.\n\n\nPesaran\, M. H. (2015)\, Time series and panel data econometrics. Oxford University Press.\nWooldridge\, J. (2010)\, Econometric analysis of cross section and panel data\, MIT Press.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \nhttps://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm
URL:https://www.swdtp.ac.uk/event-calendar/panel-data-and-factor-model-for-social-and-economic-research/2025-11-28/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251127T093000
DTEND;TZID=Europe/London:20251127T123000
DTSTAMP:20260430T150225
CREATED:20250829T091053Z
LAST-MODIFIED:20250829T091053Z
UID:10000537-1764235800-1764246600@www.swdtp.ac.uk
SUMMARY:Panel Data and Factor Model for Social and Economic Research
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Panel Data and Factor Model for Social and Economic Research  \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThere is huge body of literature applying panel data techniques using firm-level\, consumer\,  stock market and banking data. In this course\, we will present most important panel data techniques for stationary and nonstationary panels. We will discuss the importance of modelling heterogeneity and we will discuss static and dynamic models\, introducing the crucial distinction between fixed and random effects. The course will also provide a short introduction to both factor models and principal components. Practical applications using economic and financial (stocks\, interest rates) and banking (accounting) datasets will be delivered using Stata\, which is the most comprehensive econometric software for dealing with panel data analysis. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use Stata19 during the session. Participants are required to be familiar with the software and have it installed . \n  \nContent Outline \n\nStatic Panel Data Models\nDynamic Panel Data Models.\nNonstationary Panel Data Models.\nCross-sectional dependence in Panel Data.\nIntroduction of factor models\n\nLearning Objectives \n\nYou will learn how to handle and summarise panel datasets.\nYou will learn a large number of panel data techniques for stationary and nonstationary variables.\nYou will learn how to implement panel data analysis using econometric software.\n\nMain References \nA list of relevant papers will be provided at the beginning of the course. The following textbooks are recommended: \n\nBaltagi\, B. H. (2008)\, Econometric analysis of panel data\, Forth Edition\, John Wiley & Sons.\nBaltagi\, B. H. (2009)\, A companion to econometric analysis of panel data\, John Wiley & Sons.\n\n\nBoffelli\, S.\, and G. Urga (2016). Financial Econometrics Using Stata. Stata Press Publication\nBrooks\, C.\, (2019). Introductory Econometrics for Finance\, Cambridge University Press\, 4th edition.\n\n\nPesaran\, M. H. (2015)\, Time series and panel data econometrics. Oxford University Press.\nWooldridge\, J. (2010)\, Econometric analysis of cross section and panel data\, MIT Press.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \nhttps://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm
URL:https://www.swdtp.ac.uk/event-calendar/panel-data-and-factor-model-for-social-and-economic-research/2025-11-27/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251121T093000
DTEND;TZID=Europe/London:20251121T123000
DTSTAMP:20260430T150225
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000536-1763717400-1763728200@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-21/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251114T093000
DTEND;TZID=Europe/London:20251114T123000
DTSTAMP:20260430T150225
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000535-1763112600-1763123400@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-14/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251107T093000
DTEND;TZID=Europe/London:20251107T123000
DTSTAMP:20260430T150225
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000534-1762507800-1762518600@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-07/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251104T110000
DTEND;TZID=Europe/London:20251104T120000
DTSTAMP:20260430T150225
CREATED:20250919T162406Z
LAST-MODIFIED:20250919T162406Z
UID:10000549-1762254000-1762257600@www.swdtp.ac.uk
SUMMARY:Demystifying the publishing process and Open Access
DESCRIPTION:Publishing is central to being a researcher but we seldom talk about how you publish and what you need to do to look after yourself in the process. This session will talk through common routes to publishing and how to deal with\, the infamous\, Reviewer 2. Publishing your work is not just about what you say\, but how you say it. Open Access is a key part of the ‘how’ of research communication\, particularly if you’re work is supported by a funder who requires you to make your research freely available. In this webinar\, you’ll find out about the different free and paid routes to Open Access and start to think about Open Access publishing as part of broader ethical research practices. \nThe speaker for this session is Dr Kate Holmes who is an Open Access Advocacy Research Support Librarian at The University of Bristol. \nThis session is open to all interested attendees so please feel free to share widely. Attendance is free but please register to secure a place. Register on Ticket Taylor: https://buytickets.at/swdtp/1870887
URL:https://www.swdtp.ac.uk/event-calendar/demystifying-the-publishing-process-and-open-access/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251031T093000
DTEND;TZID=Europe/London:20251031T123000
DTSTAMP:20260430T150225
CREATED:20250829T090215Z
LAST-MODIFIED:20250829T090736Z
UID:10000533-1761903000-1761913800@www.swdtp.ac.uk
SUMMARY:Modelling Big Data using Autometrics
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Modelling Big Data using Autometrics\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn the age of big data\, researchers and analysts increasingly face challenges of model selection\, overfitting\, and interpreting complex high-dimensional datasets. This session introduces Autometrics\, a powerful econometric modelling tool designed to automate model selection in large datasets while maintaining strong statistical foundations. \nAutometrics is part of the OxMetrics suite and builds on the general-to-specific (Gets) modelling philosophy. It enables users to extract robust models from large sets of candidate variables\, making it especially suitable for empirical work in economics\, finance\, and the social sciences where theory may not fully specify the data-generating process. \nThis practical session will combine core theoretical principles with hands-on demonstrations\, using real-world data examples to illustrate how Autometrics addresses the challenges of modelling with many variables. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \n  \nContent Outline \n\nGeneral-to-specific (Gets) modelling and the problem of model selection\nAutometrics algorithm: overview and logic\nDiagnostic testing and model evaluation\nHands-on modelling with real-world big data using Autometrics\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles behind general-to-specific model selection and how Autometrics operationalises these.\nRecognise the challenges of modelling large datasets\, including overfitting\, multicollinearity\, and irrelevant variables.\nCritically evaluate the strengths and limitations of automated model selection tools in empirical research.\n\nMain References \n\nDoornik\, J.A.\, 2009a. Autometrics\, in: J.L. Castle\, N. Shephard (Eds.)\, The Methodology and Practice of Econometrics\, Oxford University Press\, Oxford (2009)\, pp. 88–121.\nHendry\, D.F.\, and J.A. Doornik (2014). Empirical Model Discovery and Theory Evaluation. MIT Press.\nHendry\, D.F.\, and J.A. Doornik(2018). Empirical Econometric Modelling – PcGive 16: Volume I. Timberlake Consultants Press\, London.\n\nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_9Zit5afuGNbuIfk \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/97349/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251029T090000
DTEND;TZID=Europe/London:20251029T170000
DTSTAMP:20260430T150225
CREATED:20250918T100518Z
LAST-MODIFIED:20250918T100518Z
UID:10000547-1761728400-1761757200@www.swdtp.ac.uk
SUMMARY:Justice beyond Criminal Justice
DESCRIPTION:A one-day\, in-person symposium bringing together academics from across the social sciences to explore the justice processes that sit outside of the formal criminal justice system\, used in response to serious harm and injury.\n\nThis event is a collaboration between the Criminology Research Group at the University of Bath\, the SWDTP\, and the British Society of Criminology (South West). The keynote speaker is Professor Richard Moorhead (University of Exeter)\, who will be sharing his research on the Post Office IT Horizon Inquiry. The event also incorporates panels for oral presentations\, as well as a parallel poster and art-work exhibition session. There will be an optional training session for attendees to learn more about the ethics and practicalities of using images in and for research\, as well as their role in engaging non-academic audiences. There is a fund available to support those who are interested in producing high-quality photographic images to display at this event.\n\nThis event is organised by colleagues at the University of Bath\, and supported in part by the SWDTP academic-led higher level training fund.\n\nKey dates:\nAbstract submission deadline: 30 September\nAttendee registration deadline: 6 October\nSymposium: 29 October\n\nFurther information\, abstract submission instructions and registration: https://www.eventbrite.co.uk/e/justice-beyond-criminal-justice-tickets-1606523477969?aff=oddtdtcreator\nPlease direct enquiries to the symposium organisers.
URL:https://www.swdtp.ac.uk/event-calendar/justice-beyond-criminal-justice/
LOCATION:University of Bath\, 8 West Room 2.23\, Bath\, BA2 7AY\, United Kingdom
CATEGORIES:Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251024T093000
DTEND;TZID=Europe/London:20251024T123000
DTSTAMP:20260430T150225
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000532-1761298200-1761309000@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-24/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251016T143000
DTEND;TZID=Europe/London:20251016T173000
DTSTAMP:20260430T150225
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000531-1760625000-1760635800@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-16/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251015T180000
DTEND;TZID=Europe/London:20251015T193000
DTSTAMP:20260430T150225
CREATED:20250901T172248Z
LAST-MODIFIED:20250901T172248Z
UID:10000542-1760551200-1760556600@www.swdtp.ac.uk
SUMMARY:Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?
DESCRIPTION:The lecture is themed around the important topic: “Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?”\nThis is a Public Lecture hosted by the Bristol Hub for Gambling Harms Research at the University of Bristol.\nGambling is a growing public health issue across the globe.\nThe global gambling industry is worth around $580bn – that’s bigger than the GDP of 86% of countries on our planet. But its human cost is vast. Millions of people worldwide experience serious harms\, from financial hardship and relationship breakdown to mental health problems and suicide. Smartphones have turned gambling into a 24/7 activity across the globe.\nJoin us for an insightful evening exploring innovative\, community-driven approaches to reducing gambling harm\, with a focus on powerful learnings from African research projects funded by the Bristol Hub.\nThe evening will start with a vibrant music video co-created and produced with young people affected by gambling harms in Uganda\, followed by a talk on the effects it has had on youth communities.\nThis will be followed by a panel discussion\, featuring researchers from Uganda\, Nigeria\, Namibia\, Kenya\, and South Africa\, alongside Professor Agnes Nairn from the University of Bristol.\nThis event will uncover:\n\n\nHow creative and digital tools can engage young people in awareness of gambling harms\n\n\nThe role of gambling in migration and university settings\n\n\nAdvertising’s influence in African sport and media\n\n\nDissemination of gambling messaging\n\n\nWhat the UK can learn from these diverse African perspectives\n\n\n\nWe’d love for you to join us for this important conversation. We anticipate high interest\, so please register as soon as possible to secure a place.\n\nMore details on the event can be found here\nFor queries\, email: gambling-harms@bristol.ac.uk\n\n\nThis event is kindly sponsored by Bristol Collegiate Research Society: The Bristol Collegiate Research Society is a charity committed to supporting an annual academic research symposium in the University of Bristol. The Society was founded in 1899 by a group of Bristol citizens who wished to assist University College Bristol gain a Royal Charter and become the University of Bristol. 
URL:https://www.swdtp.ac.uk/event-calendar/tackling-the-world-wide-gambling-epidemic-what-can-we-learn-from-africa/
LOCATION:Great Hall\, Wills Memorial Buildng\, University of Bristol\, Queen's Road\, Bristol\, BS8 1RJ
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251010T093000
DTEND;TZID=Europe/London:20251010T123000
DTSTAMP:20260430T150225
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000530-1760088600-1760099400@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-10/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251002T140000
DTEND;TZID=Europe/London:20251002T150000
DTSTAMP:20260430T150225
CREATED:20250819T141247Z
LAST-MODIFIED:20250819T141247Z
UID:10000526-1759413600-1759417200@www.swdtp.ac.uk
SUMMARY:Clean Python for Reproducible Research
DESCRIPTION:This live\, practical session introduces core techniques for improving the quality and transparency of research code.\nParticipants will learn how to: \n\nWrite clearer\, modular Python scripts\nUse Git for version tracking and collaboration\nSet up reusable Jupyter Notebooks for consistent workflows\n\nNo prior experience is required. The workshop is suitable for all doctoral students using (or planning to use) Python in their research. \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/clean-python-for-reproducible-research/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250930T093000
DTEND;TZID=Europe/London:20250930T163000
DTSTAMP:20260430T150225
CREATED:20250619T084718Z
LAST-MODIFIED:20250619T084718Z
UID:10000511-1759224600-1759249800@www.swdtp.ac.uk
SUMMARY:Getting to Grips with Reflexive Thematic Analysis
DESCRIPTION:This one-day online course offers a comprehensive introduction to Reflexive Thematic Analysis (RTA)\, balancing theoretical discussion with practical exercises. The aim of this course is to open up thinking about the processes of qualitative analysis and enable participants to enact RTA in flexible\, iterative and reflexive ways as appropriate to the needs of their projects. This course is interactive\, combining discussion\, demonstration and practical exercises. \n\nThe course covers:\n• The characteristics of RTA\n• The six phases of analysis\n• Engaging in reflexive practice\n• Understanding quality in RTA\n• Tools to support the process\n• Resources for further development \n\nBy the end of the course participants will: \n\nUnderstand the characteristics of Reflexive Thematic Analysis (RTA) in comparison to other approaches\nBe able to describe and enact the six phases of RTA (familiarising\, coding\, generating initial themes\, develop and review themes\, refine\, define and name themes\, write-up)\nBe able to engage in and document reflexive practices throughout the analysis process\nUnderstand the importance of quality and what it looks like in RTA practice\nConsider the appropriateness of different tools to facilitate RTA\nKnow where to access further resources to develop RTA practice
URL:https://www.swdtp.ac.uk/event-calendar/getting-to-grips-with-reflexive-thematic-analysis/
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250918T090000
DTEND;TZID=Europe/London:20250930T170000
DTSTAMP:20260430T150225
CREATED:20250918T100743Z
LAST-MODIFIED:20250918T100743Z
UID:10000548-1758186000-1759251600@www.swdtp.ac.uk
SUMMARY:Call for abstracts: Justice beyond Criminal Justice
DESCRIPTION:A one-day\, in-person symposium bringing together academics from across the social sciences to explore the justice processes that sit outside of the formal criminal justice system\, used in response to serious harm and injury.\n\nThis event is a collaboration between the Criminology Research Group at the University of Bath\, the SWDTP\, and the British Society of Criminology (South West). The keynote speaker is Professor Richard Moorhead (University of Exeter)\, who will be sharing his research on the Post Office IT Horizon Inquiry. The event also incorporates panels for oral presentations\, as well as a parallel poster and art-work exhibition session. There will be an optional training session for attendees to learn more about the ethics and practicalities of using images in and for research\, as well as their role in engaging non-academic audiences. There is a fund available to support those who are interested in producing high-quality photographic images to display at this event.\n\nThis event is organised by colleagues at the University of Bath\, and supported in part by the SWDTP academic-led higher level training fund.\n\nKey dates:\nAbstract submission deadline: 30 September\nAttendee registration deadline: 6 October\nSymposium: 29 October\n\nFurther information\, abstract submission instructions and registration: https://www.eventbrite.co.uk/e/justice-beyond-criminal-justice-tickets-1606523477969?aff=oddtdtcreator\nPlease direct enquiries to the symposium organisers.
URL:https://www.swdtp.ac.uk/event-calendar/call-for-abstracts-justice-beyond-criminal-justice/
LOCATION:University of Bath\, 8 West Room 2.23\, Bath\, BA2 7AY\, United Kingdom
CATEGORIES:Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250910T090000
DTEND;TZID=Europe/London:20250910T140000
DTSTAMP:20260430T150225
CREATED:20250829T083338Z
LAST-MODIFIED:20250829T093219Z
UID:10000529-1757494800-1757512800@www.swdtp.ac.uk
SUMMARY:Changing the Odds: Exploring Gambling and Gambling Harms Through Arts & Humanities
DESCRIPTION:Can history\, drama\, music and culture studies shed new light on gambling and its impact?\n\nChanging the Odds is a unique one-day symposium that puts the Arts and Humanities at the heart of the gambling harms debate. Moving beyond statistics and surveys\, this event will explore the stories\, meanings and creative responses that shape how gambling is understood across time and place. \nThe programme brings together leading researchers\, artists and performers to share fresh perspectives – from the gendered history of Australian sports betting to the complex role of lotteries in sixteenth century Florence. Highlights include two live drama performances by Moveable Type Theatre\, Film Director Melanie Manchot showing and talking about her recent film Stephen and an interactive discussion session with experts to spark new ideas and collaborations. \n\nWhat to expect:\n\nInspiring talks by:\n\nProfessor Evelyn Welch\, Vice-Chancellor and President\, University of Bristol (Lotteries in Early Modern Italy)\nProfessor Martin Hurcombe\, Professor of French Studies\, University of Bristol (A Brief History of How Sports and Gambling are Intertwined)\nRohann Irving\, University of Queensland (Sport\, Gambling and Masculinity: A Gendered History of Australian Sports Betting)\nSharon Martin\, University of Bristol (What Role can Songwriting play in the Lived Experience of Gambling Harms?)\n\n\nTwo live performances by Moveable Type Theatre exploring gambling through drama\nFilm Director Melanie Manchot showing and talking about her recent film Stephen\nLively Q&A sessions and a collaborative discussion\nOpportunities to connect over coffee and lunch\n\nWhether you’re a researcher\, practitioner or simply curious about how the arts can deepen our understanding of gambling\, this symposium promises lively debate\, creative inspiration and fresh insights.\n\nFind out more and register today – spaces are limited!\n\nPlease direct enquiries to gambling-harms@bristol.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/changing-the-odds-exploring-gambling-and-gambling-harms-through-arts-humanities/
LOCATION:Humanities Research Space (Room H20)\, Arts Complex\, University of Bristol
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250821T110000
DTEND;TZID=Europe/London:20250821T120000
DTSTAMP:20260430T150225
CREATED:20250818T082233Z
LAST-MODIFIED:20250818T082233Z
UID:10000525-1755774000-1755777600@www.swdtp.ac.uk
SUMMARY:UKRI Policy Internships at UKHSA - Sign up for Webinar
DESCRIPTION:UKRI Policy Internships scheme – UKRI provides an opportunity for UKRI-funded doctoral students to undertake a three-month placement at one of a selected group of influential policy organisations\, UKHSA being one of them. \nDeadline for applications to this scheme are 8th September by 4pm! \n\nWhat’s in store during the session: \n\n\nA behind-the-scenes look at UKHSA’s mission and work\nFirst hand insights from previous interns\nMeet the host teams you could be working with\nLive Q&A to answer all your burning questions\n\n\nWhy choose UKHSA for your internship? \n\n\nBe part of a multi-specialist organisation at the forefront of the UK’s health security\nWork alongside leading experts in Data Science\, AI\, Epidemiology\, Modelling\, Public Health Policy\, and Data Products\nAccess tailored learning and development opportunities\nContribute to meaningful projects with real-world impact\n\n\nWhether you’re passionate about health data\, policy\, or making a difference through research\, this is your chance to explore how your skills can help transform lives. \n\nDon’t miss out — join us and take the first step toward a placement that matters.
URL:https://www.swdtp.ac.uk/event-calendar/ukri-policy-internships-at-ukhsa-sign-up-for-webinar/
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250730T080000
DTEND;TZID=Europe/London:20250915T170000
DTSTAMP:20260430T150225
CREATED:20250730T142613Z
LAST-MODIFIED:20250730T152946Z
UID:10000524-1753862400-1757955600@www.swdtp.ac.uk
SUMMARY:Call for Abstracts: Data Analysis Webinar
DESCRIPTION:Deadline: 15 September 2025\n\nDo you have a story to share about how you did data analysis for your research in the social sciences?\n\nThe SWDTP is planning a series of webinars on data analysis in the 2025-6 academic year\, featuring presentations by researchers\, including doctoral researchers\, about how they analysed their data. We are interested in all kinds of data\, methods\, and all aspects of data analysis. In addition to the successes\, we also want to put a spotlight on the nitty-gritty details – the challenges\, frustrations\, surprises and change of plans that came with your journey.\n\nOur webinars are well attended by students and academic colleagues from across the SWDTP\, and often from institutions beyond. Your contribution will help paint a realistic picture of what conducting data analysis could look like\, and will help PGRs be better informed about different types of data analysis\, best practices\, and challenges to anticipate.\n\nExamples (not exhaustive) of the kind of questions we anticipate exploring through the webinar…\n\n – How did conducting a pilot study help with developing your methods?\n – What were the challenges of handling ethnographic data or data collected through participatory and/or creative methods?\n – Was writing part of your data analysis process?\n – Did you use AI or other software tools to help make sense of messy data?\n – How did you clean and prepare large datasets? What were the decisions you had to make along the way?\n – What ethical issues did you encounter during data analysis and how did you manage these?\n\nWe are hoping for a large number of short presentations (around 10-20 minutes each) so we can put together themed\, recorded webinars of 1 – 1.5 hour duration\, each with 2-3 presenters. We are inviting abstracts for both single presentations and complete webinars\, comprising 2-3 thematically linked presentations.\n\nIf you would like to contribute\, please email Jonathan Chow (jonathan.chow@bristol.ac.uk) with a brief abstract (100-200 words) by 15 September 2025\, including the type of data analysed and the methodology of your research. We are happy to meet to discuss an idea you are developing for a webinar.
URL:https://www.swdtp.ac.uk/event-calendar/call-for-abstracts-data-analysis-webinar/
CATEGORIES:Call,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250703T100000
DTEND;TZID=Europe/London:20250703T120000
DTSTAMP:20260430T150225
CREATED:20250310T095950Z
LAST-MODIFIED:20250310T095950Z
UID:10000505-1751536800-1751544000@www.swdtp.ac.uk
SUMMARY:Storytelling for Social Science Researchers
DESCRIPTION:Discover how storytelling can transform your social science research communication. While rigorous research is essential\, effectively sharing your findings with academic peers\, policymakers\, and the public requires additional skills. This workshop explores how narrative techniques can make your research more accessible\, compelling\, and memorable. \nThis session will help you: \n\nUnderstand why stories are effective engagement tools\nKnow when to use storytelling for the most impact\nAppreciate which stories are engaging and why\nFind structures to help you frame and explore your work in different contexts\n\nWith practical exercises\, examples\, and tools\, you’ll learn to change the way you think about research communication and how it can be done. This interactive workshop provides techniques you can immediately apply to different areas\, including presentations and writing.
URL:https://www.swdtp.ac.uk/event-calendar/storytelling-for-social-science-researchers/
LOCATION:Zoom
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250612T100000
DTEND;TZID=Europe/London:20250612T150000
DTSTAMP:20260430T150225
CREATED:20250513T102247Z
LAST-MODIFIED:20250513T122615Z
UID:10000508-1749722400-1749740400@www.swdtp.ac.uk
SUMMARY:Media Training
DESCRIPTION:This media and communications workshop is designed to ensure that the next time you address a rolling camera\, an audience or an interview panel\, you’re fully equipped to get your message across. This interactive session covers a range of tips\, techniques and broadcast skills including: developing and boosting your messages\, tackling nervousness\, communicating academic research to a wider audience\, building confidence and presence and dealing with hostile or curveball questions. \nWe’ll break down some interview examples and look at the broadcast media environment\, exploring different settings and how prepare for each one. We will run a recorded mock interview so you have a chance to put some of those skills into practice. Whether addressing a live audience or TV show\, a podcast\, panel or video blog\, by the end of the session\, you will have the confidence and skills to present your academic expertise. \nAn award-winning journalist\, author and broadcaster\, Rachel Shabi works with a variety of clients including universities\, international NGOs\, think tanks and other organisations to develop and enhance communications skills.
URL:https://www.swdtp.ac.uk/event-calendar/media-training-2/
CATEGORIES:Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250611T130000
DTEND;TZID=Europe/London:20250611T153000
DTSTAMP:20260430T150225
CREATED:20250310T095043Z
LAST-MODIFIED:20250310T095043Z
UID:10000504-1749646800-1749655800@www.swdtp.ac.uk
SUMMARY:Consultancy Skills
DESCRIPTION:This participative workshop run by How2Glu will help you to identify and develop key consultancy skills\, practical approaches to developing client relationships\, and workshop common issues. \nFirst\, we will look at drivers and prospective client / partner expectations and the characteristics of successful client consultant / partner-advisor relationships. We will then focus on essential ‘soft’ skills for consultancy – communication\, flexibility and collaboration. You will identify your strengths and areas for growth and explore strategies to develop and utilise these skills. During the session you will use glucard™ tools to help you identify ways of ‘adding value’ and ways to build / translate relationships for ‘upselling’\, repeat business and recommendations. We will look at different consultancy models\, such as salaried\, freelance\, associate\, and internal consultancy and consider how Intellectual Property (IP) relates to consultancy. Sabina will graphically illustrate concepts and demonstrate tools by sharing live visualisations. We will discuss common issues in consultancy and workshop ways to overcome them. We will use real examples and methods that are accessible\, visual and hands-on and mindful of the present context that will help you develop consultancy skills and opportunities.
URL:https://www.swdtp.ac.uk/event-calendar/consultancy-skills/
LOCATION:Zoom
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
END:VCALENDAR