What You Need to Know about the EU’s 2018 General Data Protection Regulation
Data is everything. In fact, thieves are now targeting consumer and business information rather than financial assets, simply because there is so much more value in data. Find out more about how GDPR is changing the way we all work with data in the midst of this Information age.
A great way to get in on the action.
No one is ready for GDPR
I read an article recently stating that no one is ready for GDPR, not even the regulators, This seems to be the case, despite a flurry of 'you have to give us more permissions' emails.https://www.theverge.com/2018/5/22/17378688/gdpr-general-data-protection-regulation-eu
Unsupervised investments: Top AI Venture Funds
This article lists the top VC investing in AI as well as the features and characteristics that made them the best fit for entrepreneurs
If you want to see the full list (although not completely updated) here is the link:https://medium.com/@Francesco_AI/unsupervised-investments-i-a-guide-to-ai-investors-d1a6326f71ea
Big Data Strategy: tips and practices
This paper illustrates tips and tools to run a data science practice within an organization. It will also give some tools to understand the stage of data science maturity of the company.
A modified version of this paper has been presented and accepted at an international conference very recently and will be available in a few months time in the " Advances in Intelligence Systems and Computing" series.
What should primary and secondary school educators teach in the age of AI?
I saw another article on my news feed today about what skills the next generation will need to succeed...and which there is little point learning because AI will make the jobs involving those skills redundant. What do people on here think? And why?
What you are too afraid to ask about Artificial Intelligence Part II
This article follows the first piece on machine learning describing how AI interacts with neuroscience, as well as how hardware and chips are getting created and modified to be more efficient for specific AI algorithm
I highly recommend reading the work of Numenta, which gives a new perspective on neuroscience and the biological approach to AI
Imagining Myself as a Data Engineer
Here I am again. A layperson trying to make sense of the complex landscape of data science. And today I'm thinking about data engineering.When I was a schoolboy I wanted to become a mechanical engineer. I loved taking things apart, seeing what each part did, and then putting the machine back together again. The problem was that often when I’d put all the pieces back then the object no longer functioned as it once had. It rarely functioned at all!So I didn’t take up an apprenticeship as an engineer.Nowadays - in a world built from data – I wonder if there are modern schoolboys (equivalent to me with my train sets and clocks back in the distant past) taking data architecture apart for fun and putting it back together?If so, how will they know whether they are gifted, or – like me with physical objects – the opposite, before they decide whether or not to embark on a career as a data engineer? In an invisible world, will it be as clear to them as it was to me that the thing they have ended up with is not the same as what they started with?And, if not, how is this being - or how could this be - addressed in education and training? Answers on a postcard, please. Or even better you could comment below
Kaggle is a great place to start. Plenty of datasets, challenges and competitions https://www.kaggle.com/datasets
Congratulations to Francesco Corea, the winner of the March Data Science Foundation Contributor Competition
Congratulations to Francesco Corea, the winner of the March Data Science Foundation Contributor Competition. The March Contributors Competition is now closed, the winner of the £50 Amazon gift voucher is Francesco Corea. Thank you all for submitting article and papers. The April competition is now open.
Thanks Chris for the shout-out
Advances in Data Science 2018: Final Speakers & Discussion Themes
The University of Manchester's Data Science Institute is delighted announce the conference’s final speaker line up and the themes to be discussed our upcoming Advances in Data Science Conference on May 21st and May 22nd in Manchester. “Focusing on Gaussian processes, Deep learning, latent variable models, subspace learning, network models, spatio-temporal models and longituinal data, we will explore the ways in which these methodologies can be used to address challenges faced by those working in the key application areas: Health - Security - Criminology - Discrimination/Bias - Politics - Demographics - Urban Planning - Global Challenges - Social Media – Conservation” The full conference schedule will be available on the Advances in Data Science website on March 30th 2018.
The speaker line-up for the 2018 Advances in Data Science Conference Manchester has been announced and booking are being taken.
Human Resources for Big Data Professions: A systematic Classification of Job Roles and Required Skill Sets
An excellent paper, well written and well presented. Looking forward to seeing more like this from Andrea De Mauro et al