Skip to main content

So You Want to Work in Tech: How Do You Make the Leap?

  • Chapter
  • First Online:
Non-Academic Careers for Quantitative Social Scientists

Part of the book series: Texts in Quantitative Political Analysis ((TQPA))

  • 163 Accesses

Abstract

In this chapter, I share practical tips for making the leap from academia to a career in the technology sector. I argue that there are two primary shifts in mindset required to make the leap and be successful: a shift from ideation to execution and creating value and understanding that the core function of your new job is engineering. I focus on developing skills and tools, crafting a résumé, and preparing for interviews in order to make your transition smoother.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

Download references

Resources

SQL

Git

CLI

Python

  • Google’s Python Class https://developers.google.com/edu/python

  • Automate the Boring Stuff with Python: Practical Programming for Total Beginners by Al Sweigart

  • Effective Python: 90 Specific Ways to Write Better Python, 2nd ed. By Brett Slatkin

Machine Learning

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd ed. by Aurélien Géron

  • An Introduction to Statistical Learning: with Applications in R, 2nd ed. by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

  • Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

  • Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

Big Data

  • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

  • Learning Spark: Lightning-Fast Data Analytics. 2nd ed. By Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee

Additional Resources

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Barnes, M. (2023). So You Want to Work in Tech: How Do You Make the Leap?. In: Jackson, N. (eds) Non-Academic Careers for Quantitative Social Scientists. Texts in Quantitative Political Analysis. Springer, Cham. https://doi.org/10.1007/978-3-031-35036-8_15

Download citation

Publish with us

Policies and ethics