Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others.
Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool.
You'll learn how to:
Determine which data sources to use for collecting information
Assess data quality and distinguish signal from noise
Build basic data models to illuminate patterns, and assimilate new information into the models
Cope with ambiguous information
Design experiments to test hypotheses and draw conclusions
Use segmentation to organize your data within discrete market groups
Visualize data distributions to reveal new relationships and persuade others
Predict the future with sampling and probability models
Clean your data to make it useful
Communicate the results of your analysis to your audience
Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.
Chapter 1 Introduction to Data Analysis: Break it down
Acme Cosmetics needs your help
The CEO wants data analysis to help increase sales
Data analysis is careful thinking about evidence
Define the problem
Your client will help you define your problem
Acme’s CEO has some feedback for you
Break the problem and data into smaller pieces
Now take another look at what you know
Evaluate the pieces
Analysis begins when you insert yourself
Make a recommendation
Your report is ready
The CEO likes your work
An article just came across the wire
You let the CEO’s beliefs take you down the wrong path
Your assumptions and beliefs about the world are your mental model
Your statistical model depends on your mental model
Mental models should always include what you don’t know
The CEO tells you what he doesn’t know
Acme just sent you a huge list of raw data
Time to drill further into the data
General American Wholesalers confirms your impression
Here’s what you did
Your analysis led your client to a brilliant decision
Chapter 2 Experiments: Test your theories
It’s a coffee recession!
The Starbuzz board meeting is in three months
The Starbuzz Survey
Always use the method of comparison
Comparisons are key for observational data
Could value perception be causing the revenue decline?
A typical customer’s thinking
Observational studies are full of confounders
How location might be confounding your results
Manage confounders by breaking the data into chunks
It’s worse than we thought!
You need an experiment to say which strategy will work best
The Starbuzz CEO is in a big hurry
Starbuzz drops its prices
One month later...
Control groups give you a baseline
Not getting fired 101
Let’s experiment for real!
One month later...
Confounders also plague experiments
Avoid confounders by selecting groups carefully
Randomization selects similar groups
Your experiment is ready to go
The results are in
Starbuzz has an empirically tested sales strategy
Chapter 3 Optimization: Take it to the max
You’re now in the bath toy game
Constraints limit the variables you control
Decision variables are things you can control
You have an optimization problem
Find your objective with the objective function
Your objective function
Show product mixes with your other constraints
Plot multiple constraints on the same chart
Your good options are all in the feasible region
Your new constraint changed the feasible region
Your spreadsheet does optimization
Solver crunched your optimization problem in a snap
Profits fell through the floor
Your model only describes what you put into it
Calibrate your assumptions to your analytical objectives
Watch out for negatively linked variables
Your new plan is working like a charm
Your assumptions are based on an ever-changing reality
Chapter 4 Data Visualization: Pictures make you smarter
New Army needs to optimize their website
The results are in, but the information designer is out
The last information designer submitted these three infographics
What data is behind the visualizations?
Show the data!
Here’s some unsolicited advice from the last designer
Too much data is never your problem
Making the data pretty isn’t your problem either
Data visualization is all about making the right comparisons
Your visualization is already more useful than the rejected ones
Use scatterplots to explore causes
The best visualizations are highly multivariate
Show more variables by looking at charts together
The visualization is great, but the web guru’s not satisfied yet
Good visual designs help you think about causes
The experiment designers weigh in
The experiment designers have some hypotheses of their own
The client is pleased with your work
Orders are coming in from everywhere!
Chapter 5 Hypothesis Testing: Say it ain’t so
Gimme some skin...
When do we start making new phone skins?
PodPhone doesn’t want you to predict their next move
Here’s everything we know
ElectroSkinny’s analysis does fit the data
ElectroSkinny obtained this confidential strategy memo
Variables can be negatively or positively linked
Causes in the real world are networked, not linear
Hypothesize PodPhone’s options
You have what you need to run a hypothesis test
Falsification is the heart of hypothesis testing
Diagnosticity helps you find the hypothesis with the least disconfirmation
You can’t rule out all the hypotheses, but you can say which is strongest
You just got a picture message...
It’s a launch!
Chapter 6 Bayesian Statistics: Get past first base
The doctor has disturbing news
Let’s take the accuracy analysis one claim at a time
How common is lizard flu really?
You’ve been counting false positives
All these terms describe conditional probabilities
You need to count
1 percent of people have lizard flu
Your chances of having lizard flu are still pretty low
Do complex probabilistic thinking with simple whole numbers
Bayes’ rule manages your base rates when you get new data
Michael Milton likes books. Before his first day of high school wrestling, he checked out a stack of books on technique from the library and practiced on his not-terribly-enthusiastic little sister. Then he spent the first few minutes of tryouts kicking the butts of other newbies, until the experienced wrestlers realized how much fun it would be to kick his. Within a few months, he became a decent wrestler, but he always stayed a bit ahead of the other newbies because of those books.
His life has consisted of gleefully going through that process over and over again in completely unrelated fields. Naturally, he's a Head First fanatic.
Until recently Michael spent most of time looking at databases to help nonprofit organizations figure out how to make more money. He has a degree in philosophy from New College of Florida and one in religious ethics from Yale University. When he's not in the library or the bookstore, you can find him in-line skating, taking pictures, and brewing beer.
The best thing about this book is how it uses real-world marketing questions to unlock the concepts and techniques behind practical data analysis.
That's a reverse -- and far superior -- approach to conventional reference books, which provide definitions and concepts, but no practical guidance on how to apply them (it's like trying to learn to write by being handed a dictionary).
The problems in each chapter are presented in the form of a memo from a higher-up who wants an answer to a vaguely-stated question. The chapter breaks down the vague memo into specific, actionable tasks, and shows you how to use different statistical tools to coax insights out of a collection of data.
What's more, the book encourages the reader to apply critical thinking skills at each stage of analysis -- probably the most important quality a good data analyst can have.
The style makes for a nice balance between the dense examples-only format of O'Reilly's Cookbook series, and the definition-laden format of a conventional reference book.
The only ding against the book is that it doesn't appear to have been given a proofreader's eye before publication.
It's peppered with errors in the form of numerous typos, sloppy arithmetic, and careless, rote copying-and-pasting of formulas and text blocks that weren't appropriately edited before reuse.
These problems will lure some readers off-track by reversing the meaning of some equations, and bringing into question how some results were obtained, since following the badly-edited examples clearly won't yield anything close to the correct answers.
So readers will have to apply their critical thinking skills just to parse some of the presented material so it makes sense, and not take all of it at face value.
The spate of errors is the only thing that prevented me from giving this book 5 stars.
Bottom Line Yes, I would recommend this to a friend