Cool Data Scientists on Campus

Geek Chic

Hal Varian:  “Data availability is going to continue to grow. To make that data useful is a challenge. It’s generally going to require human beings to do it.”

Source: Carl Bialik, “Data Crunchers Now the Cool Kids on Campus,” The Wall Street Journal, March 1, 2013

See my list of graduate programs in data science and big data analytics

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5 Origins of Data Science

DataScience_History

Source: Impact of Big Data on Analytics

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History of Data Science (Infographic)

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Source: Capgemini

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2015 Trends in Data Science (Infographic)

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Visualising the Road to Becoming a Data Scientist

Source: Swami Chandrasekaran

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Data Science Skills: Domain Expertise, Programming, Statistics

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Source: Business Over Broadway

Based on a study of 620+ data professionals, we found that data science skills fall into three broad areas: domain expertise (in our case, business), technology/programming and math/statistics.

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Recruiting Data Scientists to Mine the Data Explosion

DigitalUniverse_WSJ

 

Wes Hunt, Chief Data Officer (CDO) at Nationwide Mutual Insurance Co. on recruiting data scientists:

Finding talent is my largest challenge. Someone who understands our business, who has quantitative skills, who has the technical skills to create the models, and who is able to persuade others that the insights they’ve come up with are ones you can trust and take action on. The hardest part is persuasion. You get the quantitative skills, but there’s a struggle in that ability to communicate effectively. We’ll often pair people together, but we’d really like to grow the talent.

When I was in marketing, we put a focus on liberal-arts-educated individuals, because abstract thinking where there are ambiguous data sets is an area where they are comfortable. Ph.D.s in psychology were a great recruiting pool. A psych Ph.D. has a fair amount of statistical training. We created a program to recruit Ph.D.s.

There’s not yet an educational discipline and curriculum that produces data scientists at the scale that would clear the market. So the way we’ve focused on it is to find people with innate curiosity and critical thinking. You can teach the other skills. On my team, I have a pathologist, a bioengineering student who trained in doing heart research, an M.B.A., and someone who is trained in traditional data architecture. I also have a landscape construction engineer and a psychology Ph.D.

 

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Hyper Job Growth for Data Science and Analytics

datascience_jobgrowth

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Salaries of Data Scientists

Covid-19 hasn’t stopped the rise of data science: A new Burtch Works survey found that the median base salaries for data scientists range from $95,500 to $300,000.

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9 Categories of Data Scientists

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DataViz:

  • Those strong in statistics: they sometimes develop new statistical theories for big data, that even traditional statisticians are not aware of. They are expert in statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques.
  • Those strong in mathematics: NSA (national security agency) or defense/military people working on big data, astronomers, and operations research people doing analytic business optimization (inventory management and forecasting, pricing optimization, supply chain, quality control, yield optimization) as they collect, analyse and extract value out of data.
  • Those strong in data engineering, Hadoop, database/memory/file systems optimization and architecture, API’s, Analytics as a Service, optimization of data flows, data plumbing.
  • Those strong in machine learning / computer science (algorithms, computational complexity)
  • Those strong in business, ROI optimization, decision sciences, involved in some of the tasks traditionally performed by business analysts in bigger companies (dashboards design, metric mix selection and metric definitions, ROI optimization, high-level database design)
  • Those strong in production code development, software engineering (they know a few programming languages)
  • Those strong in visualization
  • Those strong in GIS, spatial data, data modeled by graphs, graph databases
  • Those strong in a few of the above. After 20 years of experience across many industries, big and small companies (and lots of training), I’m strong both in stats, machine learning, business, mathematics and more than just familiar with visualization and data engineering. This could happen to you as well over time, as you build experience. I mention this because so many people still think that it is not possible to develop a strong knowledge base across multiple domains that are traditionally perceived as separated (the silo mentality). Indeed, that’s the very reason why data science was created.
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