1 Bridging the gap between data science training and the real world

 

This chapter covers

  • Approaching data analysis using a results-driven process
  • Important data science concepts using true-to-life projects
  • Focusing on pragmatic solutions when analyzing data and learning new skills

Does the following scenario sound familiar? You’ve just received a data request from a department in your organization, and you have no idea how to handle it or perhaps even exactly what you’ve been asked to do. Outside the structured experience of your initial training, the real world is messy and uncertain. You may be wondering

  • How do you use your existing skills to complete projects for demanding stakeholders?
  • How do you keep learning now that the structured training environment is no longer there?
  • How can you apply your general skills to domain-specific tasks?
  • What do you need to learn next?

Any senior data scientist will tell you that the answer to all these questions is “experience.” By completing the eight projects in this book, you will accelerate the process of getting the experience you need to succeed as a data analyst.

1.1 The data analyst’s toolkit

1.2 A results-driven approach

1.2.1 Understand the problem

1.2.2 Start at the end

1.2.3 Identify additional resources

1.2.4 Obtain the data

1.2.5 Do the work

1.2.6 Present the minimum viable answer

1.2.7 Iterate if necessary

1.3 Project structure

Summary