Contributors and Influences

A reference manual of this size could not have been compiled without the help of a multitude of individuals over the years, both as contributors and influences:

• Oliver Benning (Reinforcement Learning);

• Kevin Cheung (Optimization; Deep Learning; Data Science with Streams);

• Youssouph Cissokho (Anomaly Detection and Outlier Analysis);

• Fabrizio Donzelli (Linear Algebra; Multivariate Calculus; Numerical Methods);

• Soufiane Fadel (Anomaly Detection and Outlier Analysis; Reinforcement Learning);

• Patrick Farrell (Survey Sampling Methods);

• Ehssan Ghashim (Programming Primer; Data Visualization and Data Exploration; Bayesian Data Analysis; Queueing Systems);

• Shintaro Hagiwara (Introductory Statistical Analysis; Spotlight on Classification);

• Lani Haque (Web Scraping and Automated Data Collection; Text Analysis and Text Mining; Network Data Analysis);

• Rafal Kulik (Probability and Applications; Introductory Statistical Analysis; Regression Analysis);

• Gilles Lamothe (Regression Analysis);

• Oliver Leduc (Spotlight on Classification; Feature Selection and Dimension Reduction);

• Dong (Elle) Liu (Time Series and Forecasting);

• Chunyun Ma (Programming Primer; Bookdown Set-Up);

• Andrew Macfie (Web Scraping and Automated Data Collection; Feature Selection and Dimension Reduction; Text Analysis and Text Mining; Natural Language Processing; Big Data and Parallel Computing);

• Aditya Maheshwari (Spotlight on Clustering; Feature Selection and Dimension Reduction; Data Engineering and Management);

• Richard Millson (Anomaly Detection and Outlier Analysis; Network Data Analysis);

• Rachel Ostic (Reinforcement Learning);

• Kate Park (Network Data Analysis);

• Smit Patel (Deep Learning);

• Maia Pelletier (Data Visualization and Data Exploration; Feature Selection and Dimension Reduction);

• Razieh Pourhasan (Deep Learning);

• Mohsen Rezapour Tougari (Recommender Engines);

• Jen Schellinck (Programming Primer; Simulations; Non-Technical Aspects of Data Work; Data Science Basics; Machine Learning 101; Spotlight on Clustering; Causality Modeling);

• Bing Wang (Data Science with Streams).

A hearty “thank you” to everyone, and to all others with whom we have crossed paths on this data adventure!