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 eBook

Free english ebooks download The 9 Pitfalls of Data Science  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.

Big Data | 7 Pitfalls | Strategies for Big Data Success
        How do you achieve success with Big Data and avoid pitfalls? The data scientist is in high demand right now, but often, companies are 
        The 9 Pitfalls of Data Science | Angus & Robertson
        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 30 Minutes: Data Modeling the Stock Market Today
        Eventbrite - The Data Incubator, a Pragmatic Institute company presents Data Science in 30 Minutes: Data Modeling the Stock Market Today - Common Pitfalls to Avoid with Jess Stauth - Thursday, Sat, Aug 10 9:00 AM.
        Buy The 9 Pitfalls of Data Science Book Online at Low Prices in
        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  
        Response to'Pitfalls of big data'.
        (2)2 Department of Statistics, Data Science and Epidemiology, Swinburne (9)9 Centre for Adolescent Health, Murdoch Children's Research 
        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.
        Cordes - Amazon.com
        The 9 Pitfalls of Data Science. by Gary Smith and Jay Cordes · Kindle · $18.99$ 18.99 $22.99$22.99. Other format: Hardcover 
        The 9 Pitfalls of Data Science (English Edition) eBook  - Amazon
        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: Nature and Pitfalls - IEEE Computer Society
        I also summarize common pitfalls about the concepts of data science, data . can understand.,Figure 2 shows empirically the data development gaps between 
        The 9 Pitfalls of Data Science by Gary Smith | WHSmith
        Shop for The 9 Pitfalls of Data Science from WHSmith. Thousands of products are available to collect from store or if your order's over £20 we'll deliver for free.
        Common data science pitfalls (article) - DataCamp
        In this tutorial, you'll learn about some pitfalls you might experience when working on data science projects "in the wild".
        Big data in sleep medicine: prospects and pitfalls in phenotyping.
        2017 Feb 16;9:11-29. doi: 10.2147/NSS. Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and

Items