\We asked our staff data scientist what motivates him, and here’s what he said:
My passions:
- Data Science research but not in an academic or corporate environment.
- Developing new, synthetic metrics (to measure yield or for data reduction), and robust, simple, scalable techniques to handle big, unstructured, messy, flowing data — avoiding the curse of big data.
- Offering awards to winners in our competitions.
- Delivering state-of-the-art, business-oriented knowledge, as open-source intellectual property, for free.
- Unifying all analytic fields (currently acting as independent silos): machine learning, business analytics, predictive modeling, data mining, operations research, quant, computer science, econometrics, statistics, and so on.
- Developing solid automated data science solutions for the non-expert, or for black-box computations and predictions.
- Growth hacking. Developing the largest, most useful, and most successful community, for analytic practitioners, with strong, diversified revenue streams and viral growth (powered by computational marketing), using a lean start-up approach (involving massive vendor outsourcing and self-funding).
- Delivering true data science training (and certification with our partners) using a new paradigm: free apprenticeship, 6-month long, online, for self-learners. Our current intern (nuclear physicist, post-doc from Columbia University and EPFL – Lausanne, Switzerland) is one of our former candidates. Emphasis is on stuff that you can’t learn in traditional (often outdated) university curricula.
- Debunking myths about the lack of analytic talent, being evangelist and the voice of data science, promoting horizontal over vertical knowledge, and warning about fake data science and other would-be data scientists.
- Defining data science, big data, and helping companies identify real talent.
- Selling data offered via API services, for instance stock price forecasts.
- Writing books, especially with a self-publishing platform such as Lulu.com.
We invite you to share your passions with us, in the comment section below.
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Additional Reading
- The 10 Best Books to Read Now on IoT
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science
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