Working as a Data Scientist

Presented in the "Becoming a Data Scientist" webinar conducted by Information System for Business (ISB) Student Union of Universitas Ciputra, Surabaya.

This session was recorded and uploaded to Youtube (spoken in Bahasa Indonesia): https://www.youtube.com/watch?v=TesKEjT1lcU

Transcript

1. Working as a Data Scientist Elvyna Tunggawan
2. A few things about me • Took an undergraduate degree in Information System ◦ Graduated from Universitas Multimedia Nusantara, Serpong in 2016 • Worked as (Associate) Data Scientist in Airy Indonesia (June 2016 - Feb 2020) • Currently a master student at The University of Auckland, New Zealand
3. How could I become a data scientist? Disclaimer It was not a common career path for an IS graduate Not to mention that “data scientist” was not a buzzword yet!
4. What I knew back then • Database systems • Programming • (A bit) statistics • (A bit) data mining • Data warehousing • Business intelligence
5. How does the daily work of a data scientist look like?
6. Source: R-bloggers
7. Source: Analytics India Mag
8. Source: Microsoft - TDSP
9. More importantly, data scientists transform data into business values.
10. Source: Reddit
11. Sample work Source: Airy ♥ Science - Medium
12. What skills should I prepare?
13. Familiar with this venn diagram? Source: Towards Data Science
14. Structured Query Languages • Window function • Query optimization
15. Programming Familiarity with Linux environment is a plus!
16. Data Visualization What can you tell from these pie charts? Much better, right? Source: The Next Web
17. Visualization can be misleading Source: The Next Web
18. Data Visualization Tools
19. Statistics If you have to choose a single number to represent the distribution below, what summary statistic will you choose? “Average” could refer to different things! Is it mean, median, or mode?
20. We can take wrong decisions without understanding stats
21. Machine learning Source: xkcd • Supervised learning • Unsupervised learning • Semi-supervised learning • Reinforcement learning
22. We need more than technical skills! Curiosity Critical thinking Growth mindset Communication skill
23. Any challenges?
24. Data quality!
25. Source: Forbes
26. People don’t really know what they need. They ask questions, but not the right one. Source: reinerbotha.com
27. Uncertainties - business evolves quickly!
28. Communication • Explain the statistical and math terms in an interpretable way • Business people do not really care about the technical stuffs you do!
29. What are the career options?
30. Industry vs. Academia vs. Freelance • Research • Teaching • Service • Check upwork.com / freelancer.com • Make money from data science competitions • Be an entrepreneur ◦ I’d suggest you have a prior industry experience • Data scientist • Data engineer • Machine learning engineer • Data analyst • Business intelligence analyst • Statistician
31. Thank you! Reach me on Twitter: @vexenta