7 Things We Learned About Data Science in 2020 7 Things We Learned About Data Science in 2020

7 Things We Learned About Data Science in 2020

For years at Newfire Global Partners, we've been sourcing data, building models, and operationalizing machine learning insights for future-thinking companies. Here are the seven most important things (backed by research) that we've learned about data science throughout this unprecedented year.

It’s been a pivotal year for the data science industry. Framed as ‘the sexiest job’ of 2020, data science has proven its applicability to a variety of industries – from healthcare to agriculture.

The constantly maturing market requires cohesive end-to-end data science solutions endowed with diverse expertise. For years at Newfire Global Partners, we’ve been sourcing data, building models, and operationalizing machine learning insights for future-thinking companies. Here are the seven most important things (backed by research) that we’ve learned about data science throughout this unprecedented year:

 

#1 – Python Is King

A comparative analysis among 65,000 professional developers has shown that Python remains preferable for 66% of developers. It is more loved than Java, R, and C#!

Python’s extensive selection of frameworks and simple syntax makes it easier for programmers to focus on problem-solving, rather than tinkering with complex algorithm testing.

Due to the rapidly growing library ecosystem and collaborative community, Python’s popularity for machine learning and data science is projected to continue through 2021 as well.

 

#2 – Data Management is Time Consuming

While moving from hype to maturity, data professionals have much work to do before actually delivering actionable insights. And a lot of this work is about ingesting, cleansing, and loading chunks of data.

The fact that data wrangling still takes a lot of time negatively impacts overall job satisfaction as well as productivity. We expect an ‘effectiveness gap’ will emerge so that the industry can start working on a solution.

#3 – A T-Shaped Skill Set Is a Must

A data scientist’s routine requires a very diverse skill set – from math and modeling to visualization best practices and proper DevOps deployment. Having a strong handle on these various components will be a must-have skill for a thriving data scientist. The ability to create across disciplines is as important as fluency in core technology.

Nearly half of the CIOs in a Gartner survey said they were in the market for employees with AI skills, yet 37% of those same respondents found such qualifications hard to recruit. In fact, slowed hiring for AI was cited as the biggest barrier to adoption in MIT Sloan & BCG Henderson Institute study. About 80% of respondents said they lacked the needed skills to manage AI programs.

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