The 9 Pitfalls of Data Science 1st edition by Gary Smith, Jay Cordes 0192582763 9780192582768

The 9 Pitfalls of Data Science 1st edition by Gary Smith, Jay Cordes – Ebook PDF Instant Download/DeliveryISBN:  0192582763 9780192582768 

Full download The 9 Pitfalls of Data Science 1st edition after payment.

Product details:

ISBN-10 :  0192582763 

ISBN-13 :  9780192582768

Author : 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.

The 9 Pitfalls of Data Science 1st Table of contents:

1. Using Bad Data
Earthquakes on the rise
Stay indoors on the first of the month
Check your data twice, analyze once
Hospital readmissions
The young die young
Let them eat cake
Law of small numbers
Small teams rule (and stink)
A can’t-miss opportunity
Zero warming?
If you can’t convince them, confuse them
Where are the bad mutual funds?
Boaty McBoatface
Sabermetrics
Good data
2. Putting Data Before Theory
Data mining
The evolution of data mining
The Texas Sharpshooter Fallacy
Cell phones don’t cause cancer
Google Flu
Target’s pregnancy predictor
Fighting crime with Facebook
The set-aside solution
Data mining heart-attack data
Tweet, tweet
The Voleon Group
England loves teal
Simpson’s Paradox
Step away from stepwise
Bitcoin babble
Theory before data
3. Worshiping Math
The Dr. Fox effect
Math rules
Fat tails
Predicting the Super Bowl
Our dirty little secret
Nonlinear models
Living with uncertainty
TradeALot
Statistical overconfidence
Principal Components Regression
Blinded by math
4. Worshiping Computers
Anthropomorphization
Black boxes
The computer poker competition
Monte Carlo simulations
Just checking
The Office
Deep neural networks
Making sense out of words
The illusion of understanding
Winograd Schemas
Seeing the world through pixels
Stop
Computers are useful, not omniscient
5. Torturing Data
Fishing expeditions
Flipping ten heads in a row
P-hacking
Tweets and heart attacks
Nutritional studies
A new study shows …
Robo-Tester
Aspartame doesn’t cause cancer
ESP-hacking
The One Million Dollar Paranormal Challenge
Data abuse
6. Fooling Yourself
nOCD
Only one move
Steve’s story
Frank’s story
Don’s story
Unsearched pennies
The Octagon
Wishful thinking
Leaps
It had to win somewhere
Unintentional clowns
7. Confusing Correlation with Causation
What causes what?
The power of time
Is correlation enough?
Tanks and clouds
The Retinator
Poker tendencies
Making peace with losses
Bargaining power
A random example
Pop-Tarts and beer
Identifying useful relationships
8. Being Surprised by Regression Toward the Mean
Unleashing the Potential
Seeing regression clearly
Ability and performance
Paying for regression
Don’t buy a portfolio from a devious data analyst
Baseball players
It can’t fail
Carrots versus sticks
Regression toward the mean is everywhere
Living with regression
9. Doing Harm
The allure of AI
Jeopardy!
Backgammon
Checkers
Chess and Go
Should we welcome our new computer overlords?
Job applications
Hi-tech redlining
Voter name crosschecks
OKCupid’s experiments
Misleading graphs
A really, really bad graph
The art of graphing
Fake news
Be careful what you wish for
Unintended consequences
I’ve been hacked!
The Streisand effect
Our fundamental right to privacy
Freedom of expression
The Golden Rule
Case Study: The Great Recession

People also search for The 9 Pitfalls of Data Science 1st:

disadvantages of data science
    
the problem with data science
    
the big data problem
    
the disadvantages of big data
    
data science pitfalls

Tags:

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