Synopses & Reviews
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.
Based on the latest research in neurobiology, cognitive science, and learning theory, this book combines words and pictures in a playful, mixed-media style that not only helps readers understand data analysis, but also remember it.
About the Author
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.
Table of Contents
; Advance Praise for Head First Data Analysis; Praise for other Head First books; Author of Head First Data Analysis; How to Use This Book: Intro; Who is this book for?; We know what you're thinking; We know what your brain is thinking; Metacognition: thinking about thinking; Here's what WE did; Read Me; The technical review team; Acknowledgments; Safari® Books Online; Chapter 1: Introduction to Data Analysis: Break it down; 1.1 Acme Cosmetics needs your help; 1.2 The CEO wants data analysis to help increase sales; 1.3 Data analysis is careful thinking about evidence; 1.4 Define the problem; 1.5 Your client will help you define your problem; 1.6 Acme's CEO has some feedback for you; 1.7 Break the problem and data into smaller pieces; 1.8 Now take another look at what you know; 1.9 Evaluate the pieces; 1.10 Analysis begins when you insert yourself; 1.11 Make a recommendation; 1.12 Your report is ready; 1.13 The CEO likes your work; 1.14 An article just came across the wire; 1.15 You let the CEO's beliefs take you down the wrong path; 1.16 Your assumptions and beliefs about the world are your mental model; 1.17 Your statistical model depends on your mental model; 1.18 Mental models should always include what you don't know; 1.19 The CEO tells you what he doesn't know; 1.20 Acme just sent you a huge list of raw data; 1.21 Time to drill further into the data; 1.22 General American Wholesalers confirms your impression; 1.23 Here's what you did; 1.24 Your analysis led your client to a brilliant decision; Chapter 2: Experiments: Test your theories; 2.1 It's a coffee recession!; 2.2 The Starbuzz board meeting is in three months; 2.3 The Starbuzz Survey; 2.4 Always use the method of comparison; 2.5 Comparisons are key for observational data; 2.6 Could value perception be causing the revenue decline?; 2.7 A typical customer's thinking; 2.8 Observational studies are full of confounders; 2.9 How location might be confounding your results; 2.10 Manage confounders by breaking the data into chunks; 2.11 It's worse than we thought!; 2.12 You need an experiment to say which strategy will work best; 2.13 The Starbuzz CEO is in a big hurry; 2.14 Starbuzz drops its prices; 2.15 One month later...; 2.16 Control groups give you a baseline; 2.17 Not getting fired 101; 2.18 Let's experiment for real!; 2.19 One month later...; 2.20 Confounders also plague experiments; 2.21 Avoid confounders by selecting groups carefully; 2.22 Randomization selects similar groups; 2.23 Your experiment is ready to go; 2.24 The results are in; 2.25 Starbuzz has an empirically tested sales strategy; Chapter 3: Optimization: Take it to the max; 3.1 You're now in the bath toy game; 3.2 Constraints limit the variables you control; 3.3 Decision variables are things you can control; 3.4 You have an optimization problem; 3.5 Find your objective with the objective function; 3.6 Your objective function; 3.7 Show product mixes with your other constraints; 3.8 Plot multiple constraints on the same chart; 3.9 Your good options are all in the feasible region; 3.10 Your new constraint changed the feasible region; 3.11 Your spreadsheet does optimization; 3.12 Solver crunched your optimization problem in a snap; 3.13 Profits fell through the floor; 3.14 Your model only describes what you put into it; 3.15 Calibrate your assumptions to your analytical objectives; 3.16 Watch out for negatively linked variables; 3.17 Your new plan is working like a charm; 3.18 Your assumptions are based on an ever-changing reality; Chapter 4: Data Visualization: Pictures make you smarter; 4.1 New Army needs to optimize their website; 4.2 The results are in, but the information designer is out; 4.3 The last information designer submitted these three infographics; 4.4 What data is behind the visualizations?; 4.5 Show the data!; 4.6 Here's some unsolicited advice from the last designer; 4.7 Too much data is never your problem; 4.8 Making the data pretty isn't your problem either; 4.9 Data visualization is all about making the right comparisons; 4.10 Your visualization is already more useful than the rejected ones; 4.11 Use scatterplots to explore causes; 4.12 The best visualizations are highly multivariate; 4.13 Show more variables by looking at charts together; 4.14 The visualization is great, but the web guru's not satisfied yet; 4.15 Good visual designs help you think about causes; 4.16 The experiment designers weigh in; 4.17 The experiment designers have some hypotheses of their own; 4.18 The client is pleased with your work; 4.19 Orders are coming in from everywhere!; Chapter 5: Hypothesis Testing: Say it ain't so; 5.1 Gimme some skin...; 5.2 When do we start making new phone skins?; 5.3 PodPhone doesn't want you to predict their next move; 5.4 Here's everything we know; 5.5 ElectroSkinny's analysis does fit the data; 5.6 ElectroSkinny obtained this confidential strategy memo; 5.7 Variables can be negatively or positively linked; 5.8 Causes in the real world are networked, not linear; 5.9 Hypothesize PodPhone's options; 5.10 You have what you need to run a hypothesis test; 5.11 Falsification is the heart of hypothesis testing; 5.12 Diagnosticity helps you find the hypothesis with the least disconfirmation; 5.13 You can't rule out all the hypotheses, but you can say which is strongest; 5.14 You just got a picture message...; 5.15 It's a launch!; Chapter 6: Bayesian Statistics: Get past first base; 6.1 The doctor has disturbing news; 6.2 Let's take the accuracy analysis one claim at a time; 6.3 How common is lizard flu really?; 6.4 You've been counting false positives; 6.5 All these terms describe conditional probabilities; 6.6 You need to count; 6.7 1 percent of people have lizard flu; 6.8 Your chances of having lizard flu are still pretty low; 6.9 Do complex probabilistic thinking with simple whole numbers; 6.10 Bayes' rule manages your base rates when you get new data; 6.11 You can use Bayes' rule over and over; 6.12 Your second test result is negative; 6.13 The new test has different accuracy statistics; 6.14 New information can change your base rate; 6.15 What a relief!; Chapter 7: Subjective Probabilities: Numerical belief; 7.1 Backwater Investments needs your help; 7.2 Their analysts are at each other's throats; 7.3 Subjective probabilities describe expert beliefs; 7.4 Subjective probabilities might show no real disagreement after all; 7.5 The analysts responded with their subjective probabilities; 7.6 The CEO doesn't see what you're up to; 7.7 The CEO loves your work; 7.8 The standard deviation measures how far points are from the average; 7.9 You were totally blindsided by this news; 7.10 Bayes' rule is great for revising subjective probabilities; 7.11 The CEO knows exactly what to do with this new information; 7.12 Russian stock owners rejoice!; Chapter 8: Heuristics: Analyze like a human; 8.1 LitterGitters submitted their report to the city council; 8.2 The LitterGitters have really cleaned up this town; 8.3 The LitterGitters have been measuring their campaign's effectiveness; 8.4 The mandate is to reduce the tonnage of litter; 8.5 Tonnage is unfeasible to measure; 8.6 Give people a hard question, and they'll answer an easier one instead; 8.7 Littering in Dataville is a complex system; 8.8 You can't build and implement a unified litter-measuring model; 8.9 Heuristics are a middle ground between going with your gut and optimization; 8.10 Use a fast and frugal tree; 8.11 Is there a simpler way to assess LitterGitters' success?; 8.12 Stereotypes are heuristics; 8.13 Your analysis is ready to present; 8.14 Looks like your analysis impressed the city council members; Chapter 9: Histograms: The shape of numbers; 9.1 Your annual review is coming up; 9.2 Going for more cash could play out in a bunch of different ways; 9.3 Here's some data on raises; 9.4 Histograms show frequencies of groups of numbers; 9.5 Gaps between bars in a histogram mean gaps among the data points; 9.6 Install and run R; 9.7 Load data into R; 9.8 R creates beautiful histograms; 9.9 Make histograms from subsets of your data; 9.10 Negotiation pays; 9.11 What will negotiation mean for you?; Chapter 10: Regression: Prediction; 10.1 What are you going to do with all this money?; 10.2 An analysis that tells people what to ask for could be huge; 10.3 Behold... the Raise Reckoner!; 10.4 Inside the algorithm will be a method to predict raises; 10.5 Scatterplots compare two variables; 10.6 A line could tell your clients where to aim; 10.7 Predict values in each strip with the graph of averages; 10.8 The regression line predicts what raises people will receive; 10.9 The line is useful if your data shows a linear correlation; 10.10 You need an equation to make your predictions precise; 10.11 Tell R to create a regression object; 10.12 The regression equation goes hand in hand with your scatterplot; 10.13 The regression equation is the Raise Reckoner algorithm; 10.14 Your raise predictor didn't work out as planned...; Chapter 11: Error: Err Well; 11.1 Your clients are pretty ticked off; 11.2 What did your raise prediction algorithm do?; 11.3 The segments of customers; 11.4 The guy who asked for 25% went outside the model; 11.5 How to handle the client who wants a prediction outside the data range; 11.6 The guy who got fired because of extrapolation has cooled off; 11.7 You've only solved part of the problem; 11.8 What does the data for the screwy outcomes look like?; 11.9 Chance errors are deviations from what your model predicts; 11.10 Error is good for you and your client; 11.11 Specify error quantitatively; 11.12 Quantify your residual distribution with Root Mean Squared error; 11.13 Your model in R already knows the R.M.S. error; 11.14 R's summary of your linear model shows your R.M.S. error; 11.15 Segmentation is all about managing error; 11.16 Good regressions balance explanation and prediction; 11.17 Your segmented models manage error better than the original model; 11.18 Your clients are returning in droves; Chapter 12: Relational Databases: Can you relate?; 12.1 The Dataville Dispatch wants to analyze sales; 12.2 Here's the data they keep to track their operations; 12.3 You need to know how the data tables relate to each other; 12.4 A database is a collection of data with well-specified relations to each other; 12.5 Trace a path through the relations to make the comparison you need; 12.6 Create a spreadsheet that goes across that path; 12.7 Your summary ties article count and sales together; 12.8 Looks like your scatterplot is going over really well; 12.9 Copying and pasting all that data was a pain; 12.10 Relational databases manage relations for you; 12.11 Dataville Dispatch built an RDBMS with your relationship diagram; 12.12 Dataville Dispatch extracted your data using the SQL language; 12.13 Comparison possibilities are endless if your data is in a RDBMS; 12.14 You're on the cover; Chapter 13: Cleaning Data: Impose order; 13.1 Just got a client list from a defunct competitor; 13.2 The dirty secret of data analysis; 13.3 Head First Head Hunters wants the list for their sales team; 13.4 Cleaning messy data is all about preparation; 13.5 Once you're organized, you can fix the data itself; 13.6 Use the # sign as a delimiter; 13.7 Excel split your data into columns using the delimiter; 13.8 Use SUBSTITUTE to replace the carat character; 13.9 You cleaned up all the first names; 13.10 The last name pattern is too complex for SUBSTITUTE; 13.11 Handle complex patterns with nested text formulas; 13.12 R can use regular expressions to crunch complex data patterns; 13.13 The sub command fixed your last names; 13.14 Now you can ship the data to your client; 13.15 Maybe you're not quite done yet...; 13.16 Sort your data to show duplicate values together; 13.17 The data is probably from a relational database; 13.18 Remove duplicate names; 13.19 You created nice, clean, unique records; 13.20 Head First Head Hunters is recruiting like gangbusters!; 13.21 Leaving town...; 13.22 It's been great having you here in Dataville!; Leftovers: The Top Ten Things (we didn't cover); #1: Everything else in statistics; #2: Excel skills; #3: Edward Tufte and his principles of visualization; #4: PivotTables; #5: The R community; #6: Nonlinear and multiple regression; #7: Null-alternative hypothesis testing; #8: Randomness; #9: Google Docs; #10: Your expertise; Install R: Start R up!; Get started with R; Install Excel Analysis Tools: The ToolPak; Install the data analysis tools in Excel;