I use the suitcase dilemma as a metaphor for the types of decisions being made in the analytics technology world by customers. Companies invariably confront with the decision on the type/size/complexity of solutions to implement, and they many times initially demanded the 100% answer to their forecast needs into the mid to long range future. Be a suitcase-skeptic – don’t be too quick to purchase the largest, handle-all-cases bags. Consider in addition the frugality and simplicity of a 95+% solution, simultaneously planning for, but not implementing, the 100% case.
Data Science advances statistics from its mathematical roots to more balanced math, data, and computational foci. The challenges were foremost of data management and computation – assembling, wrangling, cleaning, reporting, and managing the data. The statistical work was far downstream from implementation of the then-new relational database system to manage the data. The ascendance of open source changed the analytics landscape fifteen years ago, with databases like PostgreSQL and MySQL, agile languages such as Python and Ruby, and the R statistical computing platform, encouraging an even greater commitment to analytics and facilitating the emergence of companies whose products were data and analytics.
A report analysis that caught my eye was a comparison of Data Scientist salaries on the intersection of experience and education, with experience levels of 1-3 years, 4-8 years, and 9+ years, and educational attainment of Masters vs PhD. Not surprisingly, there’s an over 25% difference in median salary by category of advancing experience, along with a consistent differential between Masters and PhD of roughly 10%. Makes sense to me: obviously, experience is critical and PhD’s may well bring more of the Science/Research Methodology component of DS to the table from the get-go than do most Masters-trained practitioners.