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
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
Finding my dream "connected" home
So I'm thinking of moving house, Yes, I know that doesn't sound like a matter for DataTalk. But hear me out. What I'm wondering is, do I need to think about whether the place I move into is future-proofed in terms of the way technology and data are being integrated into the fabric of our homes? And – by extension – is this something that estate agents, the construction, building, and refurbishment industries, and even local government need to look at more closely? I've lived in early 20th century buildings where there weren't enough plug sockets to connect television, VCR and so on without recourse to a potentially- inflammatory daisy chain of adapters. I’ve lived in flats where the phone line wouldn't allow bandwidth for broadband, and the cable company wouldn’t even consider bothering to lay down an optical fibre. I’ve tried to “work from home” in digs where there wasn’t even GPRS, let alone a reliable 3G mobile signal. And I’m not talking about rural England here. I’m talking city life. So, when I finally move into my “forever home”, I want to make sure I’m not going to spend the rest of my life bewailing the fact that – for me – the Internet of Things is more like the Internet of Nothing-But-Trouble. I’ve divided my “need to have” and “nice to have” lists into two categories; fabric and location. In terms of fabric I want to make sure that my new place has plenty of hidden space for cabling. Yes, I know wifi is king right now, but with concerns about data security and hacking becoming more and more pertinent, I want to know that if the future means hardwired connections then I won’t have to spend my life tripping over – or wallpapering over – cables. And I want the rooms to be a shape which the hidden cameras and sensors which make my sentient home work can cope with…no blind spots please, no unreachable spaces. And, on that topic, I want my robots to be able to move around safely. Do I need durable, even floors and a minimum of stairs. Do I need wide doorways, sliding doors? And if wifi does remain a big thing then I’ll want to make sure I don’t have to share my network bandwidth with the street, so I’ll want outer walls with data insulation. But I don’t want to block my 3G signal…. In terms of location I want to be able to get all that optic fibre, 5G cellular coverage and so on. And I want my home internet of things to be able to work with the outside internet of things while I’m not there. So I need to learn more about the layout of the surrounding area too. Do I need to live near a telephone exchange? Do I need to think about where self-driving cars will be able to operate? Drone landing areas? Perhaps most of all, I need to think about what to do if the Internet of Things “goes down”. Do I need the back-up infrastructure of the past, such as old-school timers and switches for my heating, or will I opt for clean walls and rely on my mobile apps? Is the future one where future is overlaid onto past, or one where the past is stripped away to make room for the future? I wonder if today’s housebuilders and town planners are asking the same questions of themselves, as they get ready to greenlight and build my dream home. I hope so, because otherwise I foresee a whole lot of unhappy families spending a fortune on making their homes fit-for-purpose in the same way I recall seeing friends shelling out a fortune to rewire their homes in the past when the electricity man realised their circuits were outdated and unsafe, colleagues returning their fancy new smartphones when they discovered they weren’t very smart when they took them home to the phone-mast desert, and so on. My gut feeling is that – metaphorically - I want to keep my old chimney and fireplace, even if I’ve decided to have modern electric heaters fitted. Then I know I can always light a fire in the grate if the clock on the central heating packs up. I think a dual-layer system is the way to go. But I’m not an expert and I haven’t got a research grant to investigate. Maybe I need to look for more data...
An interesting point. I suggest that this is an issue to be considered with the next draft of the building regulations
What are the most leftfield examples of data mining for me to use in a presentation?
I recently started working in the data science sector and friends regularly ask me "what's it all about". My son even asks me what it's like to work down a data mine. After giving the usual explanations about using vast data sets and machine learning to improve outcomes for businesses, research, and medicine, I'm met with looks of veiled pity that say "It must be pretty boring." But, as you and I know, it isn't. It's the future. So what are the real-world examples I should be giving to really make my party chatter fizz? Is it the potential to become a poker millionnaire? The opportunity to find out what makes the ideal husband? Or the ideal mix of gin and tonic? Please, lend me your wisdom...before my data-ignorant friends fall asleep on me!
Students launch Machine Learning Society at Imperial
Undergraduates Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) have set up the Imperial College Machine Learning Society to get students involved in and inspired by technology that’s going to change the world.It is interesting that this society was planned by two undergraduate students and that the first event attracted 250 attendees, with over half of them being PhD students.
Your First Machine Learning Project in Python Step-By-Step
Do you want to do machine learning using Python, but you’re having trouble getting started?In this post, you will complete your first machine learning project using Python.In this step-by-step tutorial you will:Download and install Python SciPy and get the most useful package for machine learning in Python.Load a dataset and understand it’s structure using statistical summaries and data visualization.Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
Good tutorial Saroj, thanks for sharing it