1 What makes a successful data scientist?

This chapter covers

  • Learning what is expected of data scientists
  • Examining the challenges of a data scientist’s career progression

Data science (DS) is driving a quantitative understanding of the world around us. When the technologies to aggregate large quantities of data are paired with inexpensive computing resources, data scientists can discover patterns through analysis and modeling at scales that were not possible just decades earlier. This quantitative understanding of the world through data is being used to predict the future, drive consumer behavior, and make critical business decisions. The scientific process used to improve our understanding of the world allows us to craft solutions based on testable and repeatable results.

Leadership is the ability to amplify your capabilities by influencing, nurturing, directing, and inspiring people around you to produce more significant impact than what can be achieved as an individual. There are opportunities to lead as a technical individual contributor and as a people manager.

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Leadership is the ability to amplify your capabilities by influencing, nurturing, directing, and inspiring people around you to produce more significant impact than an individual can achieve. There are opportunities to lead as a technical individual contributor and as a people manager.

1.1 Data scientist expectations

1.1.1 The Venn diagram a decade later

1.1.2 What is missing?

1.1.3 Understanding ability and motivation: Assessing capabilities and virtues

1.2 Career progression in data science

1.2.1 Interview and promotion woes

1.2.2 What are (hiring) managers looking for?

Summary

References

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