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Modern statistics for modern biology

Dive into deep learning

The statistical physics of data assimilation and machine learning

The science of science

Optimization for data analysis

A practical introduction to regression discontinuity : extensions

A first course in random matrix theory : for physicists, engineers and data scientists

Privacy-preserving computing : for big data analytics and AI

Large-scale data analytics with Python and Spark : a hands-on guide to implementing machine learning solutions

Model-based clustering and classification for data science : with applications in R

Deep learning in science

Random matrix methods for machine learning

Quantitative methods of data analysis for the physical sciences and engineering

A practical introduction to regression discontinuity : extensions

Data visualization for social and policy research : a step-by-step approach using R and Python

Auditing Corporate Surveillance Systems: Research Methods for Greater Transparency

Data Science for Complex Systems

Deep Learning in Science

Text analysis in Python for social scientists: discovery and exploration

Modern Dimension Reduction

First Course in Random Matrix Theory: For Physicists, Engineers and Data Scientists

Unsupervised Machine Learning for Clustering in Political and Social Research

Text analysis in Python for social scientists: discovery and exploration

Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Mathematical pictures at a data science exhibition

Optimization for data analysis

Optimization for Data Analysis

The Statistical Physics of Data Assimilation and Machine Learning

Computational Topology for Data Analysis

Data Modeling for the Sciences: Applications, Basics, Computations

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