Why Data Science Projects Fail by Douglas Gray
The field of artificial intelligence, data science and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven.-
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Probability and Statistics for Data Science
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DevOps for Data Science
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Data Science in Healthcare
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What's the Question?
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Test-Driven Data Analysis
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Textual and Contextual Data Analysis
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Advanced Basketball Data Science
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Introduction to Classifier Performance Analysis with R
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Research Software Engineering
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Getting (more out of) Graphics
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JavaScript for Data Science
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Supervised Machine Learning for Text Analysis in R
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Massive Graph Analytics
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An Introduction to IoT Analytics
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Deep-Learning-Assisted Statistical Methods with Examples in R
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Natural Language Processing in the Real World
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Predictive Modelling for Football Analytics
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Models Demystified
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Cybersecurity Analytics
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Big Data Analytics
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Basketball Data Science
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Mathematical Engineering of Deep Learning
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Data Science for Water Utilities
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Real World AI Ethics for Data Scientists
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Data Science and Analytics Strategy
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Hands-On Data Science for Librarians
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Data Science
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Practitioner's Guide to Data Science
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Practitioner’s Guide to Data Science
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Spatial Statistics for Data Science
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Data Science in Practice
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The Data Preparation Journey
| SKU | Unavailable |
| ISBN 13 | |
| ISBN 10 | |
| Title | Why Data Science Projects Fail |
| Author | Douglas Gray |
| Series | |
| Condition | Unavailable |
| Binding Type | |
| Publisher | |
| Year published | |
| Number of pages | |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
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