Data scientists are in high demand and short supply, but they may not need a degree in computer science to get a job, according to a new report from Kaggle.
The majority of employed data scientists gained their skills through self-learning or a Massive Open Online Course (MOOC) rather than a traditional computer science degree, according to a survey from data scientist community Kaggle, which was acquired by Google Cloud.
Some 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% said that they began picking up the needed skills on their own, the 2017 State of Data Science & Machine Learning Survey report found. Some 30% got their start in data science at a university, according to the survey of more than 16,000 people in the field.
More than half of currently employed data scientists still use MOOCs for ongoing education and skill building, the report found, demonstrating the potential of these courses for helping people gain real-world skills.
Data scientist took the no. 1 spot in Glassdoor's Best Jobs in America list in 2017 and 2018 and reports a median base salary of $110,000. These professionals are few in number and high in demand, and recruiting can be difficult. However, the report offers four tips for finding qualified data scientists:
1. Target computer science studies: Some 30% of employed data scientists have an undergraduate major in computer science, compared to 18% who majored in math/statistics.
2. Poach them: Some 39% of qualified candidates (who can analyze data with code) spend at least one to two hours per week looking for a different job. And 12% of these workers are spending three to five hours per week doing so, meaning that many folks may be looking to make a career move to a new data science role.
3. Target the right titles: 59% of business analysts say they spend time looking for a data science job every week, and 53% of engineers say the same. Software developers, however, are less likely to be looking to switch to a data scientist role.
4. Open source: Some 39% of data scientists in their first year in the industry say that it's very important to them in assessing jobs to be able to publish their results, the report found. And 23% of data scientists who have been in the field for more than six years say the same.
Skillsets across data science also vary among professionals. Only 4% of employed data scientists consider themselves competent in adversarial learning and 8% in reinforcement learning, compared to 70% who say they are competent in supervised machine learning, and 39% who say so for time series analysis and unsupervised learning. Meanwhile, only 27% of employed data scientists report that they are competent in gradient boosting.
In terms of new skills to learn, 16% of respondents said they are most excited to learn TensorFlow in the next year - more than any other single tool or language, the report found.
Want to use this data in your next business presentation? Feel free to copy and paste these top takeaways into your next slideshow.
- 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. -Kaggle, 2017
- 30% of employed data scientists have an undergraduate major in computer science. -Kaggle, 2017
- 59% of business analysts say they spend time looking for a data science job every week, and 53% of engineers say the same. -Kaggle, 2017-18