Subtopic
Data Science
18 lessons to explore at your own pace.
Lessons in Data Science
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Self-paced lesson · ~17 min read
Regression Analysis for Finance: OLS, Inference, Robust Methods, and Regularization
Regression connects an outcome to explanatory variables. In finance, it can estimate market exposure, explain credit spreads, or predict returns. The central question is which…
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Self-paced lesson · ~16 min read
Regression Analysis II: Hypothesis Tests, Structural Breaks, and CAPM Regimes
A regression coefficient describes a relationship in a model. Hypothesis testing asks whether the data are compatible with a specific restriction on that relationship. Structural-break…
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Self-paced lesson · ~17 min read
Regression Analysis III: Stationarity, Autocorrelation, and ARMA–ARIMA Models
Ordinary regression relates an outcome to predictors. Time-series modeling also asks how the past predicts the future and whether that relationship remains stable through time. A trend,…
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Self-paced lesson · ~17 min read
Time Series Analysis: Likelihood, Unit Roots, Seasonality, and Volatility Forecasts
A time-series model can fit a financial series well while answering the wrong forecasting question. A rolling volatility measure, for example, is partly predictable because most of its…
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Self-paced lesson · ~16 min read
Principal Component Analysis in Finance: Yield-Curve Factors, Hedging, and Relative Value
Hundreds of financial series can share a few major sources of variation. Principal component analysis (PCA) finds directions that summarize that variation. In rates, those directions…
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Self-paced lesson · ~19 min read
Artificial Intelligence in Finance: Recent Advancements, Challenges, and Future Directions
The adoption of artificial intelligence (AI) in financial services is accelerating. Industry surveys indicate that the proportion of finance professionals using AI grew from 37% in 2023…
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Self-paced lesson · ~18 min read
Machine Learning for Finance: Validation, Neural Pricing, and Reinforcement-Learning Hedges
Machine learning fits patterns from data to support predictions or decisions. In finance, a model can estimate a return, approximate an expensive pricing engine, organize risk exposures,…
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Self-paced lesson · ~17 min read
Neural Networks and Deep Learning: Training Deep Neural Networks
A neural network learns a prediction rule by adjusting weights and biases. A deep network composes several transformations, allowing it to learn useful intermediate representations as…
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Self-paced lesson · ~16 min read
Training Deep Neural Networks: Keras, TensorFlow, and Tabular Data
Training a neural network means turning a mathematical prediction rule into an experiment: prepare the data, choose an objective, update parameters, and measure performance on unseen…
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Self-paced lesson · ~17 min read
Deep Learning for Computer Vision: Building Convolutional Neural Networks from Scratch
Images contain structure: neighboring pixels form edges, edges combine into shapes, and shapes help identify objects. A convolutional neural network, or CNN, builds that structure into…
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Self-paced lesson · ~16 min read
Deep Learning for Computer Vision: Transfer Learning, Fine-Tuning, and Hugging Face
Training every visual feature from random initialization can be wasteful when you have only a few hundred labeled images. Transfer learning starts from representations learned on another…
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Self-paced lesson · ~16 min read
Deep Learning for Natural Language: The Basics
Text carries explanations, opinions, stories, instructions, and conversations. Natural language processing, or NLP, turns this unstructured material into representations that…
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Self-paced lesson · ~17 min read
Deep Learning for Natural Language: Word Embeddings
A word ID tells a model which token it has encountered, but says nothing about what that token means. Word embeddings replace isolated token indicators with learned dense vectors. This…
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Self-paced lesson · ~15 min read
Deep Learning for Natural Language: Transformers
A static embedding gives a token one vector. A Transformer combines token representations with information about their positions and surrounding tokens, producing a contextual state at…
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Self-paced lesson · ~14 min read
Deep Learning for Natural Language: Transformers and Self-Supervised Learning
A Transformer specifies how token representations interact. A learning objective specifies what those interactions should help predict. Self-supervised learning connects the two by…
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Self-paced lesson · ~15 min read
Generative AI: Large Language Models and Retrieval-Augmented Generation
A language model can produce fluent answers from patterns learned during training, but an application often needs information that is private, recent, or too extensive to fit in one…
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Self-paced lesson · ~15 min read
Generative AI: Adapting LLMs with Parameter-Efficient Fine-Tuning
Adapting a large language model can mean changing its instructions, supplying external evidence, or training parameters for a target task. Parameter-efficient fine-tuning, or PEFT, makes…
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Self-paced lesson · ~18 min read
Applying Data Science and Artificial Intelligence to Managing Biomedical Portfolios
A promising biological discovery is not yet a medicine, and a promising medicine is not yet a financeable investment. Biomedical portfolio management connects scientific evidence,…