Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - <i>Data Science Ethics</i> addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitivegroups, and various explainable AI techniques. Real-life cautionary tales further illust
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Amidst a growing movement to use science for positive change, Ethical Data Science offers a solution-oriented approach to the ethical challenges of data science. As one of the first books on public interest technology, it provides a starting point for...
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Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part-text starts from the basics of probability...
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<b>Step-by-step guide to practising data science techniques with Jupyter notebooks</b><br/><br/><b>Description</b><br/>Modern businesses are awash with data, making data driven decision-making tasks increasingly...
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