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<p>Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in <i>Invisible Women</i>, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.</p>
<p>Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in <i>Invisible Women</i>, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
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[[Image:Computer-age-statistical-inference.jpg|150px|Computer age statistical inference: Algorithms, evidence, and data science]]
<h3 style="text-decoration:none;">Computer age statistical inference: Algorithms, evidence, and data science</h3>
<p class="author">by Bradley Efron and Trevor Hastie</p>
<p>The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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<p><i>Numbersense</i> gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p>
<p><i>Numbersense</i> gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
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[[Image:Hands-on-machine-learning-with-Scikit-Learn-and-TensorFlow.jpg|150px|Hands-on machine learning with Scikit-Learn and TensorFlow]]
<h3 style="text-decoration:none;">Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems</h3>
<p class="author">by Aurélien Géron</p>
<p>By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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