Making the Most of ‘Omics Data - Manuel Mayr, King’s College London
Exploring innovative 'omics data analysis for cardiovascular research, focusing on proteomics, lipidomics, and their applications in disease prediction and understanding atherosclerosis progression.
Be Prepared to Show Your Working! - Professor Sir David Spiegelhalter
Explore algorithmic decision-making transparency, validation, and explainability with Professor Spiegelhalter. Learn about the importance of demonstrating trustworthiness and intelligent transparency in automated advice systems.
Modelling Passenger Behaviour on the Underground
Explore passenger behavior modeling on the London Underground, covering disruption response and causal models for complex systems with heterogeneous data and practical implications.
Statistics in Cyber-Security - Dr Nick Heard, Imperial College London
Explore statistical approaches for cyber-security, from micro-level models to full network graph representations, to identify illegitimate network activity using enterprise data sources.
Researching Dynamic Analysis of Road Traffic Flow and Traffic Signal Systems - Ben Heydecker, UCL
Explore innovative AI approaches for urban traffic planning and control, featuring insights from experts on optimizing mobility systems and addressing future challenges.
The Data Centre of the Future - Marie-Christine Sawley, Intel Exascale Laboratory
Explore the future of data centers with Marie-Christine Sawley, focusing on digital fusion, data analytics, and collaborative research in neuroscience and artificial intelligence.
Causal Inference in Observational Studies - Emma McCoy, Imperial College London
Explore causal inference in observational studies with Prof. Emma McCoy. Learn about robust estimation of treatment effects, time-series methodology, and applications in transport settings.
Preprocessing Song Dataset for Melody Generation
Collate encoded songs, create integer mapping for symbolic notation, and prepare data for neural network ingestion in melody generation preprocessing.
Convolutional Neural Networks Explained Easily
Explore CNNs, their components, and application to audio data. Learn about convolutions, pooling, and architectural decisions for effective deep learning in sound processing.
Medical Examiners - An Introduction by Dr. Alan Fletcher
Explore the role of Medical Examiners, their responsibilities, and the implementation of a new system in 2019, including key processes and case examples.
Statistical Rethinking 2023 - Horoscopes
Explore statistical rethinking through horoscopes, covering planning, execution, reporting, and science reform. Gain insights into innovative approaches for statistical analysis and interpretation.
Statistical Rethinking 2023 - Multilevel Models
Explore multilevel models, partial pooling, and hyperparameter tuning through practical examples like reedfrogs. Learn advanced techniques for statistical analysis and model optimization.
Statistical Rethinking 2023 - Counts & Hidden Confounds
Explore hidden confounds in statistical analysis, sensitivity analysis, and poison counts. Learn about Simpson's paradox and its implications for data interpretation.
Statistical Rethinking 2023 - Elemental Confounds
Explore key concepts in causal inference: the fork, pipe, collider, and descendant. Learn to simulate interventions and understand elemental confounds in statistical analysis.
Application of Molecular Methods in Diagnostic Microbiology - Dr. Alice Wort
Explore molecular methods in diagnostic microbiology, focusing on high-throughput screening and cutting-edge technologies to enhance patient care in a state-of-the-art laboratory setting.
Statistical Rethinking 2022 - Horoscopes
Explore statistical rethinking through subjective responsibilities, planning, working, reporting, and scientific reform. Gain insights into research methodologies and their practical applications.
Statistical Rethinking - Social Networks
Explore statistical approaches to analyzing social networks, including reciprocal ties, generalized giving, and household features. Learn techniques for modeling complex social structures.
Historical Fear and Greed Index Data - Jupyter Notebooks
Explore historical Fear and Greed Index data using Jupyter Notebooks for backtesting and analysis, enhancing your understanding of market sentiment trends.
Detecting Price and Volume Patterns with SQL and TimescaleDB
Learn to detect stock price patterns using SQL and TimescaleDB. Explore PostgreSQL functions, TimescaleDB features, and analyze real-world examples to identify bullish engulfing and three-bar breakout patterns.
TimescaleDB - PostgreSQL for Time-Series Data
Explore TimescaleDB for efficient time-series data management in PostgreSQL, focusing on stock market applications and practical coding examples.
Building Tools and Frameworks for Large-Scale Social Media Mining
Learn to build tools and frameworks for large-scale social media mining, focusing on creating data infrastructure for COVID-19 research using Twitter data. Gain insights on challenges, solutions, and best practices.