Preparing for the transition - data science as a student vs in the industry
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Talk at WiDS (Women in Data Science) Oslo 2018. Link to the video recording at the end of the slides starting at 55m29s (https://youtu.be/3w41wZDOKpY?t=55m29s).
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Preparing for the transition - data science as a student vs in the industry
- 1. Ning Zhou 2018.03.08 Preparing for the Transition: Data Science as a Student v.s. in the Industry
- 2. 2 How I got started with data science Bachelor Master PhD Industry Research Industry Product Development Classroom Project Academic Project Industry Project
- 3. Classroom vs Academic vs Industry 3 Problem Data User Peer
- 4. Problem Academic ● Technical problems abstracted from business scenarios ● Prefer challenging problems that are difficult to solve 4 Industry ● Problem often arrives as a product requirement instead of a technical problem ● Prefer low-hanging fruits that can bring large impact with relatively small efforts Classroom ● Well-defined problems with clear metrics to measure success ● “Solved” problems with known solutions
- 5. Data Academic ● Open dataset with some quality assurance ● Mid to large volume ● Work with industry datasets too, but often pre-collected 5 Industry ● “Dirty” data ● Can be from very small to very large volume ● Data collection takes time Classroom ● Clean data ● Relatively “small” volume
- 6. User Academic ● Limited opportunities to test with real users ● Offline test is still the most common way to measure performance 6 Industry ● Impact on real users (no matter good or bad…) ● Online test is considered as “final” Classroom ● No real user impact ● Mostly offline test only
- 7. Peer Academic ● Smart peers from the broader research community working on similar topics 7 Industry ● Smart colleagues, but they normally work on different projects Classroom ● Smart classmates who work on the same project
- 8. Five Things I tried that didn’t help ● Team up with smart and hardworking classmates and then just be lazy ● Try random open-source models without thinking through ● “Tune metrics” instead of tuning models ● Manipulate data manually to throw out bad or difficult samples ● Procrastinate until the deadline approaches 8
- 9. Five Things I tried that helped ● Be curious about what other people are working on ● Keep cost and performance in mind ● Stay updated with the latest progress from both academia and industry ● Try things hands-on ● Write papers / technical sketches / blogs 9
- 10. Tusen takk! Questions? You can also contact me at ning@duoja.com. 10