Subscribe to DSC Newsletter

Top languages and tools according to Data Professionals

Packt Publishing, publisher of software learning resources, has revealed the results of its 2018 Skill Up Developer Skills survey in a new report.From what programming languages, frameworks, and libraries are most used, to job satisfaction, the report offers a snapshot of what matters to software developers in 2018. 

In the Skill Up Survey, both app and web developers have spoken of the importance of Machine Learning and other cutting edge data techniques to their future success.

 

Here are some of the key findings from Packt’s report on Data Science:

 

  • Standing proud, Python has ascended to be the number one language of data. It has over double the uptake of its traditional rival R, which lags behind it in third place. Python’s ease of use, powerful tools and libraries, and use outside of the data field make it almost mandatory to know and use in 2018.

  • After old classic Excel, eight of the top ten most used data tools are derived from or utilize Python. This is where we see one of the key strengths that has caused Python’s rise to dominance - the great power and variety of the tools to pair with it. Only in 10th place does an R Library make a showing, in the form of ggplot2.

  • Weighted by frequency, in the next 12 months, data specialists said they are planning on learning:

  • Pushing Machine Learning algorithms further and further is going to be one of the key challenges for every data professional over the next year and beyond. For some this will mean getting deeper into the complexities of incredibly sophisticated AI systems.

  •  Natural Language Processing is currently one of the most important areas in data science. When you consider the rise of conversational UI, as well as the importance of interpreting text whether that’s for understanding customer sentiment or healthcare research, it’s easy to see why it’s such a valuable area.

  •  Data professionals were the group most likely to view Blockchain as revolutionary. This makes sense, especially in the context of 2018’s anxiety around data. With Blockchain, data is more secure; distributed ledgers give you greater visibility on where data has come from, when it was gathered. This is good news from both an analytics and a trust perspective.

Hot Topics

 

  • 66% of respondents said that they were incorporating deep learning techniques into their data analysis. Having embraced the power and potential of machine learning, the industry is now pushing even further into neural networks and machine intelligence outside of the lab.

 

  • Over half of all respondents still think that reigning Cloud provider AWS is the best service to use for Big Data.

  • By far, respondents say the worst part of data analysis is cleaning data. Over half say it’s the worst part of their jobs!

Views: 1799

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2018   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service