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The 9 Pitfalls of Data Science by Gary Smith, Jay Cordes

• The 9 Pitfalls of Data Science
• Gary Smith, Jay Cordes
• Page: 272
• Format: pdf, ePub, mobi, fb2
• ISBN: 9780198844396
• Publisher: Oxford University Press

Download The 9 Pitfalls of Data Science

German textbook pdf download The 9 Pitfalls of Data Science (English literature) 9780198844396 by Gary Smith, Jay Cordes

Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.

A Data Science Treatise: Part Two - Success Through Tinkering
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In this tutorial, you'll learn about some pitfalls you might experience when working on data science projects "in the wild". Big data for managers: Data science + business analytics training
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The 9 Pitfalls of Data Science book. Read reviews from world's largest community for readers. Data science has never had more influence on the world. Lar How (not) to use Machine Learning for time series forecasting
Jun 6, 2018 · 9 min read post, I will discuss some of the common pitfalls of machine learning for time series forecasting. with the Python numerical and scientific libraries NumPy and SciPy. Example case: Prediction of time series data. The 9 Pitfalls of Data Science - Gary Smith, Jay Cordes - Google
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Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts   Data Science in Real Life | Coursera
Learn Data Science in Real Life from Université Johns-Hopkins. Have you ever Describe common pitfalls in communicating data analyses 6. Get a glimpse 

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