Chapter 1. What is data science?
Chapter 3. Getting the skills
Figure 3.1. Flow of deciding which data science education route to take
Chapter 4. Building a portfolio
Figure 4.1. The flow of creating a data science project
Figure 4.2. Sample output of the offensive license plate generator neural network
Chapter 5. The search: Identifying the right job for you
Figure 5.1. Some job titles that don’t include “data science” that you may find when searching
Chapter 6. The application: Résumés and cover letters
Figure 6.1. Example résumé for an aspiring data scientist
Figure 6.2. An example cover letter with highlights showing the different components
Chapter 7. The interview: What to expect and how to handle it
Chapter 9. The first months on the job
Chapter 10. Making an effective analysis
Figure 10.1. An example slide of an analysis PowerPoint deck
Figure 10.2. The process of answering a business question with data science, devised by Renee Teate
Figure 10.3. An example analysis plan
Figure 10.4. An example summary table
Figure 10.5. Example of a visualization made during an analysis before cleaning
Figure 10.6. The same data as figure 10.5, plotted to highlight the importance of the letter T
Chapter 11. Deploying a model into production
Figure 11.1. Example process of creating a production machine learning product
Chapter 12. Working with stakeholders
Chapter 13. When your data science project fails
Figure 13.1. Two metaphors for data science: architecture and treasure-hunting
Chapter 14. Joining the data science community
Figure 14.1. Some ways to join the community that are covered in this chapter
Chapter 16. Moving up the ladder