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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Gary Smith is the Fletcher Jones Professor of Economics at Pomona College. He received his Ph.D. in Economics from Yale University and was an Assistant Professor there for seven years. He has won two teaching awards and written (or co-authored) more than 80 academic papers and twelve books including Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie With Statistics, What the Luck? The Surprising Role of Chance in Our Everyday Lives, and Money Machine: The Surprisingly Simple Power of Value Investing. His research has been featured by Bloomberg Radio Network, CNBC, The Brian Lehrer Show, Forbes, The New York Times, Wall Street Journal, Motley Fool, Newsweek, and BusinessWeek. Jay Cordes is a data scientist who enjoys tackling challenging problems, including how to guide future data scientists away from the common pitfalls he saw in the corporate world. He's a recent graduate from UC Berkeley's Master of Information and Data Science (MIDS) program and graduated from Pomona College with a mathematics major. He has worked as a software developer and a data analyst and was also a strategic advisor and sparring partner for the winning pokerbot in the 2007 AAAI Computer Poker Competition world championship.
Inhaltsangabe
1: Pitfall #1: Using Bad Data 2: Pitfall #2: Putting Data Before Theory 3: Pitfall #3: Worshiping Math 4: Pitfall #4: Worshiping Computers 5: Pitfall #5: Torturing Data 6: Pitfall #6: Fooling Yourself 7: Pitfall #7: Confusing Correlation with Causation 8: Pitfall #8: Being Surprised By Regression Toward the Mean 9: Pitfall #9: Doing Harm 10: Case Study: The Great Recession
1: Pitfall #1: Using Bad Data 2: Pitfall #2: Putting Data Before Theory 3: Pitfall #3: Worshiping Math 4: Pitfall #4: Worshiping Computers 5: Pitfall #5: Torturing Data 6: Pitfall #6: Fooling Yourself 7: Pitfall #7: Confusing Correlation with Causation 8: Pitfall #8: Being Surprised By Regression Toward the Mean 9: Pitfall #9: Doing Harm 10: Case Study: The Great Recession
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