Data Science

A compendium of data sources for data science, machine learning, and artificial intelligence ArXiv ID: 2309.05682 “View on arXiv” Authors: Unknown Abstract Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are application-specific, and it is impossible to produce an exhaustive list of such data sources, it seems that a comprehensive, rather than complete, list would still benefit data scientists and machine learning experts of all levels of seniority. The goal of this publication is to provide just such an (inevitably incomplete) list – or compendium – of data sources across multiple areas of applications, including finance and economics, legal (laws and regulations), life sciences (medicine and drug discovery), news sentiment and social media, retail and ecommerce, satellite imagery, and shipping and logistics, and sports. ...

Advances in Financial Machine Learning: Lecture 1/10 (seminar slides) ArXiv ID: ssrn-3270329 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, algorithmic trading, predictive analytics, data science, fintech, Multi-Asset / Quantitative Strategies Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The excerpt presents a high-level critique of econometric methods compared to machine learning, but it focuses on theoretical arguments and conceptual pitfalls rather than advancing novel mathematical techniques or presenting concrete backtesting results. flowchart TD A["Research Goal: Apply ML to Financial Markets"] --> B["Methodology: Identify Financial Signals & Features"] B --> C["Data Inputs: High-Frequency Trading & Market Data"] C --> D["Computation: Training Algorithms & Model Validation"] D --> E["Outcomes: Predictive Analytics for Multi-Asset Strategies"]

Advances in Financial Machine Learning: Lecture 2/10 (seminar slides) ArXiv ID: ssrn-3257415 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine learning, Data science, Automation, Technology Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The excerpt introduces concepts like high-dimensional spaces and non-linear relationships but is devoid of advanced formulas, focusing instead on conceptual discussions and examples. It lacks data, backtests, code, or specific implementation metrics, making it more of a high-level overview than an empirical or technical paper. flowchart TD Q["Research Goal: Applying ML to Finance"] --> D["Data: Financial Market Data"] D --> M["Methodology: ML Algorithms"] M --> C["Computational Process: Pattern Recognition"] C --> F["Outcome: Task Automation"] F --> O["Key Finding: Expert-Level Performance"]

Advances in Financial Machine Learning: Lecture 3/10 (seminar slides) ArXiv ID: ssrn-3257419 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning, Artificial Intelligence, Algorithmic Trading, Predictive Analytics, Data Science, Equity Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper introduces advanced financial data structures and labeling techniques like Fractionally Differentiated Features, Triple Barrier Method, and Meta-Labeling, involving statistical estimation and optimization, yet the provided excerpt is conceptual lecture slides without executable code, backtests, or specific datasets, limiting its immediate empirical implementation. flowchart TD A["Research Goal:<br>Predictive Analytics for Equity Markets"] --> B["Methodology: ML Algorithms"] A --> C["Data: Financial Time Series"] B --> D["Computational Process:<br>Feature Engineering & Backtesting"] C --> D D --> E["Outcome: Algorithmic Trading Signals"] D --> F["Outcome: Risk Assessment Models"] E --> G["Key Finding:<br>ML enhances trading efficiency"]

Advances in Financial Machine Learning: Lecture 5/10 (seminar slides) ArXiv ID: ssrn-3257497 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Algorithmic Trading, Data Science, Predictive Analytics, Multi-Asset Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The material features advanced statistical derivations, hypothesis testing, and combinatorial math for backtesting methods like CPCV, warranting a high math score. However, it lacks concrete code, dataset specifics, or reported backtest results, focusing instead on methodological warnings and theoretical frameworks, resulting in moderate empirical rigor. flowchart TD A["Research Goal: Assess ML Efficacy in Multi-Asset Algorithmic Trading"] --> B["Data Acquisition & Cleaning"] B --> C["Feature Engineering & Time-Series Splitting"] C --> D["Computational Process: Ensemble ML Models"] D --> E["Key Finding 1: ML Outperforms Traditional Econometrics"] D --> F["Key Finding 2: Meta-Labeling Improves Risk Management"] E --> G["Outcome: Enhanced Predictive Analytics for Financial Markets"] F --> G