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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251002T140000
DTEND;TZID=Europe/London:20251002T150000
DTSTAMP:20260426T053556
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:20251009T110000
DTEND;TZID=Europe/London:20251009T180000
DTSTAMP:20260426T053556
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:20251010T093000
DTEND;TZID=Europe/London:20251010T123000
DTSTAMP:20260426T053556
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:20251016T143000
DTEND;TZID=Europe/London:20251016T173000
DTSTAMP:20260426T053556
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:20251024T093000
DTEND;TZID=Europe/London:20251024T123000
DTSTAMP:20260426T053556
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:20251031T093000
DTEND;TZID=Europe/London:20251031T123000
DTSTAMP:20260426T053556
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
END:VCALENDAR