Appendix C. More tools and ideas worth exploring
In data science, you’re betting on the data and the process, not betting on any one magic technique. We advise designing your projects to be the pursuit of quantifiable goals that have already been linked to important business needs. To concretely demonstrate this work style, we emphasize building predictive models using methods that are easily accessible from R. This is a good place to start, but shouldn’t be the end.
There’s always more to do in a data science project. At the least, you can
- Recruit new partners
- Research more profitable business goals
- Design new experiments
- Specify new variables
- Collect more data
- Explore new visualizations
- Design new presentations
- Test old assumptions
- Implement new methods
- Try new tools
The point being this: there’s always more to try. Minimize confusion by keeping a running journal of your actual goals and of things you haven’t yet had time to try. And don’t let tools and techniques distract you away from your goals and data. Always work with “your hands in the data.” That being said, we close with some useful topics for further research (please see the bibliography for publication details).