Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal, Information Age and insideBIGDATA. To read more posts from Kayla, visit her personal tech blog, Productivity Bytes.
Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.
Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world.
Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.
When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.
Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.
Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.
We still have a long way to go before the gender representation becomes more equalized, but the field at large indicates hopeful trends about women working in the role or desiring to do so in the future.
Predictive analytics are useful for doing all those things and more, and could increase the overall competitiveness of individual companies or entire sectors.
The question has probably come up of whether it’s ever okay to offer your data-related knowledge to people or organizations for free. Does taking that approach ever benefit you?
7 Simple Data Visualizations You Should Know in R