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
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.
Table of Contents
Chapter 1 introduction to data analysis: Break it down
Chapter 2 experiments: Test your theories
Chapter 3 optimization: Take it to the max
Chapter 4 data visualization: Pictures make you smarter
Chapter 5 hypothesis testing: Say it ain’t so
Chapter 6 bayesian statistics: Get past first base
Chapter 7 subjective probabilities: Numerical belief
Chapter 8 heuristics: Analyze like a human
Chapter 9 histograms: The shape of numbers
Chapter 10 regression: Prediction
Chapter 11 error: Err Well
Chapter 12 relational databases: Can you relate?
Chapter 13 cleaning data: Impose order
Appendix I: leftovers: The Top Ten Things (we didn't cover)
Appendix II: install R: Start R up!