Here we gather a curated list of online resources for marketers and data scientists – ebooks, papers, tutorials or software.
Data Science Ebooks
- Networks, Crowds and Markets – an amazing ebook touching on topics from networks, game theory, online auctions and many others.
- Mining of Massive Datasets – a book based on Stanford’s Data Mining course focusing mostly on Big Data solutions for frequent itemsets, clustering and mining social graphs.
- Learning Deep Architectures for AI – a standard manual for Deep Learning enthusiasts.
- Natural Language Processing with Python – a handbook for NLTK 3. Good introductory material.
- The Elements of Statistical Learning – AI bible by Hastie, Tibshirani and Friedman.
- Think Bayes – an easy to digest intro to Bayesian methods.
- Probability and Statistics Cookbook – a set of great reference cards with formulas, charts and definitions.
Data Science Books
- “Bayesian Statistics and Marketing“, Peter E. Rossi, Greg M. Allenby, Rob McCulloch – a thorough overview of Bayesian modeling for marketing. A companion to the bayesm package in R.
- “Pattern Recognition and Machine Learning“, Christopher M. Bishop – one of the standard manual about machine learning methodologies.
- data.table – a specialized data type to work with very large tables. Fast aggregation and join.
- marittr – a pipe-forwarding mechanism for R.
- ggplot2 – powerful package for data visualization.
- arules – frequent itemsets and association rules mining.
- BTYD – fitting Pareto/NBD, GB/NBD models for non-contractual customer behavior.
- NMF – Non-linear Matrix Factorization.