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DTSTART;TZID=Europe/London:20260113T091500
DTEND;TZID=Europe/London:20260115T160000
DTSTAMP:20260621T061850
CREATED:20251017T114807Z
LAST-MODIFIED:20251017T114807Z
UID:10000553-1768295700-1768492800@www.swdtp.ac.uk
SUMMARY:Introduction to Multilevel Modelling\, Using MLwiN\, R\, or Stata
DESCRIPTION:Introduction to Multilevel Modelling\, Using MLwiN\, R\, or Stata  \n13-15 January 2026\, Online via Zoom \nClosing date for applications is 23rd November 2025. \nThis workshop is run by the University of Bristol School of Education. Further information and the booking form can be found at the following link: \nhttp://www.bris.ac.uk/cmm/software/support/workshops/
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-multilevel-modelling-using-mlwin-r-or-stata-2/
LOCATION:Online
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260113T091500
DTEND;TZID=Europe/London:20260115T160000
DTSTAMP:20260621T061850
CREATED:20250930T125839Z
LAST-MODIFIED:20250930T125839Z
UID:10000550-1768295700-1768492800@www.swdtp.ac.uk
SUMMARY:Introduction to Multilevel Modelling Using MLwiN\, R\, or Stata
DESCRIPTION:Run in partnership with NCRM\n\nPlease note this event is not run by the SWDTP. Please direct enquiries to Lucy Haslam at the University of Bristol Centre for Multilevel Modelling: lucy.haslam@bristol.ac.uk\nPlease note the closing date for applications is 23rd November 2025\n \nGo to booking form >>\n\nInstructors\nProfessor George Leckie and Professor William Browne\n \nSummary\nThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN\, R\, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when\, for example\, analysing exam scores of students nested within schools\, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered\, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore.\n\nTestimonials\n\n“The course was really excellent – clearly structured and in a logical order. Speakers were fantastic.”\n\n“The course was excellent – far exceeded expectations. The course has given me the confidence to use MLM\, something I very much lacked before. I feel I understand the theory behind MLM\, why each stage is so important\, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”\n\n“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content\, time for questions and discussion\, and practical sessions. Both George and Bill delivered fantastic lectures – explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb – can’t rate it highly enough.”\n\n“I thought it was a really good double act between George and Bill – they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn’t get derailed by people’s questions.”\n\n“Both George and Bill have excellent presentation styles. I really liked that they ‘riffed’ off of each other with gentle humour.”\n\nTopics\n\nOverview of multilevel modelling\nVariance-components models\nRandom-intercept models with covariates\nBetween- and within-effects of level-1 covariates\nRandom-coefficient models\nGrowth-curve models\nThree-level models\nReview of single-level logistic regression\nTwo-level logistic regression\n\n\nFormat\nThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The instructors alternate the lecturing. The lectures are software independent. Each lecture is immediately followed by a software practical giving participants the chance to replicate the presented analyses and to consolidate their knowledge. The practicals are offered in participants’ choice of MLwiN\, R\, or Stata and are self-directed: participants complete the practicals at their own pace. At the end of each practical session the instructors demo the different software. In both the lectures and practicals\, participants have opportunities to interact with the instructors.\n\nZoom\nThe course will be delivered online via the freely accessible Zoom platform. The lectures will be delivered live. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.\n\nParticipants are encouraged to join the lectures live\, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. After twelve weeks\, video access will end and will not be extended.\n\nDuring the practicals\, participants can also speak with the instructors. Participants can use these opportunities to ask specific questions about the course material or about multilevel modelling related to their own research. Each software package will be demonstrated in a different breakout room.\n\nMaterials\nParticipants will be emailed in advance with comprehensive PDF copies of the lecture slides together with point-and-click instructions and datasets for MLwiN\, and annotated syntax files and datasets for R and Stata. During the practicals\, participants are encouraged to view the lecture slides on a second screen (or tablet etc.)\, else print copies out to have in front of them. Those choosing to use MLwiN may also want to view the point-and-click instructions on a second screen\, else print them out.\n\nSoftware\nFor those choosing to use MLwiN\, we will provide instructions as to how to download and install the free teaching version of this software. For those wishing to use R or Stata we assume you are already users of these software so have them installed.\n\nPre-requisites\nWe assume no prior knowledge of multilevel modelling. However\, participants should be familiar with estimating and interpreting linear regression models\, including the writing and interpretation of model equations\, hypothesis testing and model selection\, and the use and interpretation of dummy variables and interaction terms.\n\nWe will email in advance a pre-recorded lecture\, to be completed at the participant’s leisure\, which provides a review of linear regression accompanied with software instructions and datasets to replicate the analyses in MLwiN\, R\, and Stata.\n\nFor those choosing to use MLwiN\, we assume no prior knowledge of using this software and so we provide step-by-step instructions to allow you to replicate all presented analyses in MLwiN. For those choosing R or Stata\, we assume you are already users of these software and so know the basics.\n\nTimings\nThe course starts and ends each day at 09:15 and 16:00 with a 30-minute morning break and a one-hour break for lunch from 13:00 to 14:00.\n \nFees\n\nFor UK-registered MSc and PhD students – £180\nFor UK university academics\, UK public sector staff\, and staff at UK registered charity organisations – £360\nFor all other participants – £660\n\n\nPlease note\, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address.\n \nCancellation/refunds\nA full refund will be given if cancellation occurs two weeks prior to the event. No refund is given after this date. By completing the application form\, you are accepting these cancellation terms.\n \nApplications\nIf you would like to attend the workshop\, please complete and submit the online booking form (see below). Please note the closing date for applications is 23rd November 2025.\n\nApplications will be processed on a rolling basis\, once a week\, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.\n\nIf you have any queries\, please email info-cmm@bristol.ac.uk.\n \nGo to booking form >>\n\nTerms and conditions\nPlease click here to read the booking terms and conditions before completing the booking form. Note that it is the participant’s responsibility to ensure that Zoom and their choice of MLwiN\, R\, or Stata software is up-to-date and works on their computer in advance of the course\, as the Centre for Multilevel Modelling is unable to provide technical support.\n\nMLwiN\nMLwiN is dedicated multilevel modelling software developed by our research team for more than 30 years. On this course we will be using the free teaching version of MLwiN. This version works with all the datasets used on the course and a wide range of other teaching datasets which come with the software. We will email you the teaching version prior to the start of the course.\n\nShould you wish to use MLwiN after the course with your own data\, you will need to use the regular version of MLwiN. This is free to UK academics (but without user support) reflecting long periods of funding from the UK’s Economic and Social science Research Council (ESRC). For all other users\, there is a 30-day trial version\, but after that you will have to purchase MLwiN if you wish to continue using it to analyse your own data. There are various price options available. http://www.bristol.ac.uk/cmm/software/mlwin/\n \nMLwiN is Windows software\, but can be run on Mac via the Wine software or through a virtual machine such as Parallels\, depending on the Mac model and version of MacOS on your machine.
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-multilevel-modelling-using-mlwin-r-or-stata/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251210T100000
DTEND;TZID=Europe/London:20251211T160000
DTSTAMP:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20260621T061850
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:20251024T093000
DTEND;TZID=Europe/London:20251024T123000
DTSTAMP:20260621T061850
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:20260621T061850
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:20251010T093000
DTEND;TZID=Europe/London:20251010T123000
DTSTAMP:20260621T061850
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:20251009T110000
DTEND;TZID=Europe/London:20251009T180000
DTSTAMP:20260621T061850
CREATED:20250905T163924Z
LAST-MODIFIED:20250905T163924Z
UID:10000543-1760007600-1760032800@www.swdtp.ac.uk
SUMMARY:Education-focused Quantitative Research Methods Workshops Using R
DESCRIPTION:The Centre for Multilevel Modelling (CMM) at Bristol is running two education-focused quantitative research method workshops using R at the School of Education. “R Progamming for Beginners” and “An Introduction to Rasch Modelling Using R”. \nFurther details and registration instructions can be found here:  https://www.eventbrite.co.uk/e/two-cmm-hosted-quantitative-research-methods-workshops-using-r-tickets-1656870958639?aff=oddtdtcreator&_gl=1%2A1r1e1il%2A_up%2AMQ..%2A_ga%2AMTcyMTE2NzAyMC4xNzU3MDY0OTA2%2A_ga_TQVES5V6SH%2AczE3NTcwNjQ5MDYkbzEkZzAkdDE3NTcwNjQ5MDYkajYwJGwwJGgw\n\nPlease send enquiries to: ed-events@bristol.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/education-focused-quantitative-research-methods-workshops-using-r/
LOCATION:35 Berkeley Square\, Bristol\, 35 Berkeley Square\, Bristol\, BS81JA\, United Kingdom
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251002T140000
DTEND;TZID=Europe/London:20251002T150000
DTSTAMP:20260621T061850
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:20250520T110000
DTEND;TZID=Europe/London:20250520T120000
DTSTAMP:20260621T061850
CREATED:20250507T152340Z
LAST-MODIFIED:20250507T152340Z
UID:10000507-1747738800-1747742400@www.swdtp.ac.uk
SUMMARY:Introducing Critical Interpretive Synthesis: a systematic\, inclusive and generative approach to reviewing the literature
DESCRIPTION:This session introduces critical interpretive synthesis (CIS) as a systematic approach to collating\, reviewing and synthesising a diverse body of research evidence. CIS aims to produce fresh insights on an issue of concern\, drawing on techniques that are common in qualitative inquiry\, such as the ongoing refinement of research questions and an iterative process of data collection and analysis. I will share my own experiences of using CIS in solo and collaborative projects in the field of education (Mitchell\, 2025). In this interactive session we will consider CIS in relation to other approaches to systematic review\, comparing their logics and processes. We will consider the affordances and challenges of using CIS in a doctoral research project\, and strategies for incorporating CIS within your own work. \nThis session is hosted by Dr Rafael Mitchell\, Senior Lecturer in Comparative and International Education\, and co-director of the Centre for Comparative and International Research in Education (CIRE) at the University of Bristol. \nReference: Mitchell\, R. (2025). Using critical interpretive synthesis (CIS) for theoretical development: Mobilising African education research. In Sage Research Methods Cases Part 1. SAGE Publications\, Ltd.\, https://doi.org/10.4135/9781071984185 \nRegistration: https://www.tickettailor.com/events/swdtp/1651269
URL:https://www.swdtp.ac.uk/event-calendar/introducing-critical-interpretive-synthesis-a-systematic-inclusive-and-generative-approach-to-reviewing-the-literature/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250512T110000
DTEND;TZID=Europe/London:20250512T120000
DTSTAMP:20260621T061850
CREATED:20250507T151858Z
LAST-MODIFIED:20250507T152424Z
UID:10000506-1747047600-1747051200@www.swdtp.ac.uk
SUMMARY:Making your literature review count
DESCRIPTION:The literature review is a key part of your doctoral thesis. It takes time to develop and builds an original argument. So\, why not make it available to others by turning it into a peer-reviewed publication? In this session of our Conducting Literature Reviews webinar series\, two early career researchers (ECRs) will talk about how they used their literature reviews in publications and gain recognition in their field. \nDr Nasrul Ismail is Senior Lecturer in Criminology in the School for Policy Studies at the University of Bristol. He is a co-editor of Justice\, Power and Resistance (Bristol University Press) and also serves on the International Advisory Board for the International Journal of Prison Health. Nasrul also happens to be an SWDTP alumnus.\nDr Freya Wise is an SWDTP-awarded ESRC Research Fellow at UWE Bristol in the Centre for Advanced Built Environment Research (CABER) and a Visiting Fellow at The Open University. \nRegistration: https://www.tickettailor.com/events/swdtp/1651287
URL:https://www.swdtp.ac.uk/event-calendar/making-your-literature-review-count/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250403T110000
DTEND;TZID=Europe/London:20250403T120000
DTSTAMP:20260621T061850
CREATED:20250307T145540Z
LAST-MODIFIED:20250307T145839Z
UID:10000502-1743678000-1743681600@www.swdtp.ac.uk
SUMMARY:Conducting a Systematic Scoping Review: Methods and Insights from a Study on the Indian Diaspora and Education
DESCRIPTION:This webinar\, part of our Conducting Literature Reviews webinar series\, explores the process of conducting a systematic scoping review. Using a worked example on education in the post-independence Indian diaspora\, the session will outline the five-stage framework of Arksey and O’Malley (2005)\, covering search strategies\, inclusion criteria\, and thematic analysis. It will also discuss challenges in refining search terms\, identifying relevant literature\, and synthesising findings. This presentation provides practical guidance for doctoral students on conducting rigorous scoping reviews and demonstrates their value in mapping research landscapes. \nThis presentation will be presented by Dr Rita Chawla-Duggan\, Associate Professor and Deputy Head of the Department of Education at The University of Bath. No interaction is expected\, but there will be Q&A during the session. \nReserve a space on our Ticket Tailor page: https://buytickets.at/swdtp/1620296
URL:https://www.swdtp.ac.uk/event-calendar/conducting-a-systematic-scoping-review-methods-and-insights-from-a-study-on-the-indian-diaspora-and-education/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250402T150000
DTEND;TZID=Europe/London:20250402T160000
DTSTAMP:20260621T061850
CREATED:20250307T145156Z
LAST-MODIFIED:20250311T095149Z
UID:10000501-1743606000-1743609600@www.swdtp.ac.uk
SUMMARY:Unlock the Power of Corpus-Based Techniques for systematic literature reviews
DESCRIPTION:Advances in computer technology made it possible to create and use large language databases often referred to as corpora in language studies. A recent development in the field is to apply corpus-based approaches to a wide range of areas beyond linguistic analysis (Pérez-Paredes\, 2021; Seale & Charteris-Black\, 2010). This interactive webinar will showcase how both quantitative and qualitative corpus analysis techniques—such as keyword analysis\, analysis of collocation (frequently co-occurring words)\, keywords in context\, and multi-word items—can be effectively utilized to identify and examine key themes for systematic literature reviews. Mastering corpus-based techniques is useful for researchers and PhD students as it allows for a more systematic and comprehensive analysis of existing literature. These skills enable participants to uncover patterns and themes that might be missed through traditional methods\, thereby enhancing the depth and rigour of research. \nThis session will be presented by Dr Reka Jablonkai\, Senior Lecturer (Associate Professor) at the Department of Education and Research Group Convenor for the Language in and for Education Research Group at The University of Bath. \nReserve a space on our Ticket Tailor page: https://buytickets.at/swdtp/1620092
URL:https://www.swdtp.ac.uk/event-calendar/unlock-the-power-of-corpus-based-techniques-for-systematic-literature-reviews/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240306
DTEND;VALUE=DATE:20240307
DTSTAMP:20260621T061850
CREATED:20240220T160435Z
LAST-MODIFIED:20240423T155100Z
UID:10000379-1709683200-1709769599@www.swdtp.ac.uk
SUMMARY:Open House - University of Bath
DESCRIPTION:We at the SWDTP love connecting with our community and what better way than to come straight to you! The purpose of this meet-up is for students to have the chance to come and speak directly with the SWDTP team\, it need not be a PhD or academic-related discussion either\, we always love to simply see how our students are doing and help them in any way we can. \n  \nIf you’d like to book a meeting with one of us\, please do drop us an email. You can find our profiles and contact details on the Meet the Team page of our website. And don’t forget\, you don’t have to have a specific request to come along. If you’d like a catch-up and a coffee (on us)\, we’d love to see you!\n\nAlso\, I’ve been asked to remind you that the closing date for applications to get involved in the ARC Accelerator training and placement opportunity is in 2 days (Thursday 22nd February). The scheme focuses on impact\, commercialisation and researcher identity\, and you can read more about it in our January Newsletter.\nHere’s the link to apply. if you’re interested: https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKUz_XULcoLX5OtCkh72tvPpFURUNDT1hVVFI2TDJLMUhBUExGMUVaVkJUMy4u.\n 
URL:https://www.swdtp.ac.uk/event-calendar/open-house-university-of-bath/
LOCATION:University of Bath\, Bath\, United Kingdom
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220913
DTEND;VALUE=DATE:20220915
DTSTAMP:20260621T061850
CREATED:20220609T123212Z
LAST-MODIFIED:20240509T121434Z
UID:10000354-1663027200-1663199999@www.swdtp.ac.uk
SUMMARY:NCRM: MethodsCon 2022
DESCRIPTION:MethodsCon is a wholly in-person event that will take place over two days in Manchester (UK) on 13-14 September 2022. The theme of the event is “rethinking the boundaries of methods in health and social science research”. \nFollowing two years of online delivery\, NCRM is delighted to announce a return to in-person\, live events. This innovative and cross-sector meet-up is part conference\, part learning opportunity\, part innovation incubator. It has been designed to re-connect and re-energise people with a focus on interdisciplinary perspectives on health and social science. \n\nProgramme\nThe provisional MethodsCon programme is now available. The final programme will be announced in the near future. View the provisional programme. \nSessions will be in three main formats: \n\nInteractive Seminars\nProfessional Development Workshops\nInnovation Incubators\n\n\nApplications to attend\nTo attend MethodCon\, please complete our quick online application form. You will be notified if you have been successful within two weeks of your application. \nComplete the MethodsCon application form\n  \nPlease note\, MethodsCon has been designed to maximise the collaborative and creative opportunities of people being in the same physical space. To build on this\, we are looking to ensure that those attending are interested in participating in one\, or both\, full days of participatory activities. \n\n 
URL:https://www.swdtp.ac.uk/event-calendar/ncrm-methodscon-2022/
LOCATION:etc.venues\, 11 Portland Street\, Manchester\, M1 3HU
CATEGORIES:Conference,Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220608T130000
DTEND;TZID=UTC:20220608T143000
DTSTAMP:20260621T061850
CREATED:20220506T121829Z
LAST-MODIFIED:20240507T124812Z
UID:10000347-1654693200-1654698600@www.swdtp.ac.uk
SUMMARY:Creative Approaches to Ethical Qualitative Research
DESCRIPTION:The Centre for Qualitative Research is running weekly online workshops for doctoral researchers funded by the South West Doctoral Training Partnership (SWDTP). This event is part of the CQRs Qualitative Innovation Series. \nFind out more about the series here\n \n  \n\nDr Shona McIntosh and Dr Rachel Wilder will discuss their recent work experimenting with creative\, inclusive and alternative ways to do ethical qualitative research. Drawing from the example of an online \nseminar series exploring methodologies for epistemic justice\, the speakers reflect on the issues of working collaboratively within hierarchical\, patriarchal and Euro-centred research traditions. They reflect on the potentials and limitations of creative approaches to foster ethical\, inclusive practice. \nThis work is underpinned by a commitment to and interest in epistemic justice theory (Fricker\, 2007) which focuses on whose knowledge is valued and whose voices are heard and listened to (e.g. Masaka\, 2019). \n  \nRegister your place here
URL:https://www.swdtp.ac.uk/event-calendar/creative-approaches-to-ethical-qualitative-research/
LOCATION:Online
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220607T130000
DTEND;TZID=UTC:20220607T170000
DTSTAMP:20260621T061850
CREATED:20220429T074537Z
LAST-MODIFIED:20240509T120823Z
UID:10000345-1654606800-1654621200@www.swdtp.ac.uk
SUMMARY:Social media training for mental health research
DESCRIPTION:Are you a PhD candidate researching child and adolescent mental health and interested in improving the dissemination of your research via social media? The SWDTP is providing a half-day training course facilitated by Andre Tomlin of The Mental Elf to improve your social media skills. The course will take place at the University of Bath on the 7th June 2022 (1-5pm).  \nThe training will guide you in: \n\n\n\n\n\n\n\nBecoming familiar with the range of online tools and methods available to disseminate mental health research\, e.g.\, blogs\, social media\, podcasts\, online events.\nWorking through scenarios and reaching consensus about the best ways to build online profiles and networks in mental health.\nExploring different online communication styles and consider what works best when discussing mental health in the public domain.\nLeaving with practical examples of what to do next to take your public engagement to the next level.\n\n\n\n\n\n\n\nThere are 20 places on the training programme for students across the SWDTP network\, including 3-5 places for non-SWDTP students. Some limited travel funds are available for students who are unable to use their training support fee for travel to Bath. Please indicate on the form whether you would need financial support to attend. \n\nTo apply please complete this short survey.\n\nAs places are limited\, we ask that you only apply if you believe you will be available on the day. Applications will be open until Monday 16th May\, after which time we will get in touch to confirm if your application was successful.
URL:https://www.swdtp.ac.uk/event-calendar/social-media-training-for-mental-health-research/
LOCATION:University of Bath\, Bath\, United Kingdom
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220519T130000
DTEND;TZID=UTC:20220519T143000
DTSTAMP:20260621T061850
CREATED:20220504T121446Z
LAST-MODIFIED:20240507T125034Z
UID:10000346-1652965200-1652970600@www.swdtp.ac.uk
SUMMARY:Feminist Perspectives and Methodologies
DESCRIPTION:Sign up on Eventbrite here\n\nAbout the session\nTaking inspiration from the epistemological and theoretical critiques and developments in feminisms\, feminist methods and methodologies are about more than just including women in research or women studying women. \nFeminist methods tend to offer a challenge to knowledge production itself interlinked with feminist political intent\, ethical processes\, egalitarianism\, and the examination of power\, dominance\, inequality\, or discrimination. \nThis webinar will provide an introduction to the history of feminist methods in concert with the growth of feminist thought. We illustrate both specific methodologies developed in and through feminist thought\, and how feminist thought can be brought to bear on other methods and methodologies (e.g.\, interviews\, fieldwork\, ethnography\, media studies)\, as well as on other aspects of the research process (e.g.\, ethics\, representation). \n\nFind out more about the webinar series here
URL:https://www.swdtp.ac.uk/event-calendar/feminist-perspectives-and-methodologies/
LOCATION:Online
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220512T093000
DTEND;TZID=UTC:20220512T173000
DTSTAMP:20260621T061850
CREATED:20220425T103826Z
LAST-MODIFIED:20240507T124909Z
UID:10000150-1652347800-1652376600@www.swdtp.ac.uk
SUMMARY:PGR Workshop - Narrative research: Possibilities and challenges of researching people's stories
DESCRIPTION:Sign up on Eventbrite here\n\nFull-day workshop for PGR students at UoB interested in learning\, discussing and sharing their work on narrative research. \n \n  \nNarrative research has gained popularity and relevance in diverse disciplines and has become a diverse\, complex and contested field. Multiple theoretical approaches\, methods\, strategies of analysis coexist under the term “narrative”. The workshop is designed as a space for dialogue\, integration and exchange on narrative research in order to explore the potential of this field. \nAs doctoral researchers our aim is to bring together PGR students interested or working in narrative research from different disciplines and theoretical/methodological approaches. We want to create a friendly space to share and discuss our work. \nTwo distinguished professors will be participating in the workshop: \n\n\n\nProfessor Corinne Squire\, Chair of Global Inequalities\, School for Policy Studies\, University of Bristol. Co-director Association of Narrative Research and Practice\nProfessor the Collaboration Facilitator Andrews\, Professor of Political Psychology\, University College London. Co-Director Association of Narrative Research and Practice\n\n\n\n  \nFor more information\, please contact: s.espinalmeza@bristol.ac.uk / g.hidalgobazan@bristol.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/pgr-workshop-narrative-research-possibilities-and-challenges-of-researching-peoples-stories/
LOCATION:Online
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220503T090000
DTEND;TZID=UTC:20220504T120000
DTSTAMP:20260621T061850
CREATED:20220331T125452Z
LAST-MODIFIED:20240429T122321Z
UID:10000147-1651568400-1651665600@www.swdtp.ac.uk
SUMMARY:NCRM - A Friendly Introduction to Quantitative Methods
DESCRIPTION:About the event\nThe aim of this course is to introduce students and researchers who have little to no quantitative training to the key concepts in quantitative methods in an accessible way. It is targeted at those who are writing theses/papers based on qualitative or review methods but who want to include some quantitative analysis and/or to be confident when reading and incorporating quantitative research. It is particularly aimed at encouraging people who find quantitative methods intimidating or unwelcoming. For those that enjoy the course it may act as a basis for further quantitative training. \nThe course it is two mornings and will equate to one teaching day for payment purposes. \nBy the end of the course participants will: \n\n\n\n\n\nHave greater understanding of and confidence in engaging with quantitative work\nHave knowledge of the key concepts involved in quantitative analysis\nHave been introduced to key sources of data and quantitative software options\n\n\n\n\n\n\nFind out more on the NCRM website here\nReady to register?\n 
URL:https://www.swdtp.ac.uk/event-calendar/ncrm-a-friendly-introduction-to-quantitative-methods/
LOCATION:Online
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220412T100000
DTEND;TZID=UTC:20220614T120000
DTSTAMP:20260621T061850
CREATED:20220331T110155Z
LAST-MODIFIED:20240507T100554Z
UID:10000145-1649757600-1655208000@www.swdtp.ac.uk
SUMMARY:Centre for Qualitative Research Core Training begins
DESCRIPTION:The Centre for Qualitative Research from the University of Bath is running weekly online workshops for doctoral researchers funded by the South West Doctoral Training Partnership (SWDTP). \n\nBringing together academics from across the SWDTP institutions interested in qualitative research\, across disciplinary pathways\, to collaboratively develop and deliver a programme of bespoke training for SWDTP doctoral researchers. These SWDTP-Funded Bespoke Qualitative Training Workshops will provide practical and cross-disciplinary qualitative training to SWDTP-funded doctoral scholars and others who are interested in qualitative training\, through the collaborative resources of the SWDTP network. \n\n\n\nThe core training package focuses on from planning to product: the process of conducting qualitative research. This will consist of a series of 7 bespoke online seminars delivered fortnightly to up to 50 students by collaborators across the SWDTP universities. \nRegistration is available here\n  \n1. Where does qualitative research come from? – Tuesday 12th April\, 10am-12pm \n2. Planning and designing qualitative research – Thursday 21st April\, 10am-12pm \n3. Preparing for speaking-based data collection – Tuesday 26th April\, 10am-12pm \n4. Conducting\, improving\, and refining interviews and focus groups – Tuesday 3rd May\, 10am-12pm \n5. Analysing interview and focus group data using thematic analysis – Tuesday 10th May\, 10am-12pm \n6. Exploring the diversity of forms of qualitative analysis – Wednesday 18th May\,10am-12pm \n7. Series conclusion: Publishing qualitative research – Tuesday 14th June 2022
URL:https://www.swdtp.ac.uk/event-calendar/centre-for-qualitative-research-core-training/
LOCATION:Online
CATEGORIES:Higher Level Training
END:VEVENT
END:VCALENDAR