Machine learning is Big Data being used at its most extreme level, processing vast and disparate data sets at a machine level to find patterns buried within, producing insights beyond human recognition.
Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today’s biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the
Looking for the perfect podcast for your morning commute or during your downtime? Here’s a list of the best podcasts in data (in alphabetic order). Data Skeptic Unusual Angles Data Skeptic takes a different take on how we review data—thanks to some healthy skepticism, listeners come out with unusual information and knowledge.
A few months ago, Airbnb ran a great post about how its trust and safety data scientists build machine learning models to protect users from fraud by predicting bad actors. As the piece illustrated using Game of Thrones, a highly nuanced model is required to determine something like whether someone
“I don’t think that you should approach big data as a solution in search of a problem”- Interview with Skimlinks Maria Mestre
I completed a PhD in signal processing at Cambridge developing models of user behaviour using brain data. After the PhD I joined Skimlinks as a data scientist, where I model online user behaviour and work on much larger datasets. My main role is implementing large-scale machine learning models processing terabytes
“I often warn data analysts not to underestimate the power of small data” – Interview with Data Mining Consultant Rosaria Silipo
Rosaria has been a researcher in applications of Data Mining and Machine Learning for over a decade. Application fields include biomedical systems and data analysis, financial time series (including risk analysis), and automatic speech processing. She is currently based in Zurich, Switzerland. What project have you worked on do you
“Open source and public cloud are the most impactful shifts I have seen.” – Interview with Google Cloud Platform’s William Vambenepe
William Vambenepe is the Lead Project Manager for Big Data at Google Cloud Platform. Dataconomy interviewed him about his career path, his current role and how he sees the industry changing. You’ve worked for some of the biggest names in the industry (HP, Oracle, Google), what stands out to you
Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling
Jeff Palmucci has been writing code professionally since he was 11 years old. A serial entrepreneur, Jeff has started several companies. He was a Founder and the VP of Software Development for Optimax Systems, a developer of scheduling systems for manufacturing operations. Optimax was acquired by i2 Technologies where he
Unstructured text data represents the biggest data set available to enterprises, yet most are unable to process the vast amount of data they collect to get any meaningful insight. Up to 80 percent of data available to enterprises is unstructured data, and comes in a variety of forms, such as
Narrative Science has been a regular feature on Dataconomy over the past year, from Chief Scientist Kris Hammond’s post about the impact of artificial intelligence on banking, to the launch of their Quill Connect application for processing unstructured text data from social media. I think for AI in general, the goal is not to