2016 Feb Opinions, Interviews, Reports
All (114) | Courses, Education (9) | Meetings (14) | News, Features (16) | Opinions, Interviews, Reports (31) | Publications (3) | Software (14) | Top Tweets (5) | Tutorials, Overviews (15) | Webcasts (7)
- How The Algorithm Economy And Containers Are Changing The Apps - Feb 29, 2016.
Algorithmic Intelligence has been a driving force for many today’s technology companies. Understand how these organisations are using algorithms and container services for creating value from data.
- The Force Awakens In Data – Industry Leaders Comment - Feb 29, 2016.
While Rey, saw her force come to life in less then 30min, the data industry has been waiting for ‘that’ to happen, for half a decade. However, finally, business-focused analytics and data discovery are on the rise.
- The Machine Learning Problem of The Next Decade - Feb 26, 2016.
How can businesses integrate imperfect machine-learning algorithms into their workflow?
- Text analytics: what makes your phone smarter than survey analysis - Feb 25, 2016.
Text analytics and word prediction has been broadly used for smart phones. Here, we present “next word predictor” (NWP) as an enhancement for existing survey analysis tool kits and use-cases for the same.
- Conversation with data scientist Sebastian Raschka: A New Podcast Episode - Feb 24, 2016.
In this post we present a interview of Sebastian Raschka, data scientist and author of Python Machine Learning. Who discussed about machine learning, data science, current and future trends.
- 4 Simple Ways To Use Data to Grow Your Business in 2016 - Feb 23, 2016.
With the rise of new, affordable, and easy-to-use tools, business owners have started to get a better picture with the data. Here, we introduce you to a couple of these handy analytics tools to manage data within the organization, build customer loyalty and explore it with visualisation.
- How Small is the World, Really? - Feb 22, 2016.
Social network analysis is back in the news again, with a recent Facebook project which determined that there are an average of 3.5 intermediaries between any 2 Facebook users. But this is different than "6 degrees of separation." Read on to find out why, and how.
- Employee Engagement – a Tricky Metric for Predictive Analytics - Feb 22, 2016.
Predictive analytics for workforce has developed significantly in recent times. Here we focus on an important discovery about Employee Engagement metric – why it is tricky.
- How Data Science is Fighting Disease - Feb 22, 2016.
Many organisations are starting to use Data Science as a method of tracking, diagnosing and curing some of the world’s most widespread diseases. We look at 3 common diseases, and how Data Science is used to save lives.
- Opening Up Deep Learning For Everyone - Feb 19, 2016.
Opening deep learning up to everyone is a noble goal. But is it achievable? Should non-programmers and even non-technical people be able to implement deep neural models?
- Big Data: Rising In Importance But Still Challenging, New Surveys Say - Feb 18, 2016.
Big Data is almost mainstream, and its perceived importance is on the rise. What are the continued challenges to Big Data adoption? Some new surveys provide insight.
- Big Data Is Driving Your Car - Feb 18, 2016.
Never mind driverless cars! Big Data is already hard at work in every aspect of the automotive industry, including safety, design, marketing and more. We look at where Big Data is having an impact on the cars that we are driving.
- Who do I call if I want to call Europe? - Feb 17, 2016.
Despite all obstacles, Europe built not only the biggest world economy but also a special place where people are protected like nowhere else on the planet. Here is a tiny EU programme that played a key role.
- Deep Learning and Startups: Notes on Rework Conference, San Francisco - Feb 17, 2016.
The Rework Deep Learning conference came to San Francisco this past January, and showcased both prominent deep learning researchers and startups. Get an overview of the proceedings with notes from an attendee.
- The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing - Feb 15, 2016.
Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.
- The Next Big Inflection in Big Data: Automated Insights - Feb 15, 2016.
To keep up with big data and improve our use of information, we need insightful applications that will quickly and inexpensively extract correlations while associating insights with actions.
- Visualizing Unstructured Analysis – Elections, Words, and Zika virus - Feb 15, 2016.
Unstructured data has proven to be a big analytics challenge. This week in the Data Driven Digest, we’re serving up some ingenious visualizations of unstructured data and making it talk.
- Money vs Votes in New Hampshire Primary – SuperPACs not very effective - Feb 12, 2016.
We examine the money and votes in New Hampshire 2016 Primary. Over $100 million was spent by all campaigns, with hugely varying results, and no apparent correlation between money and votes.
- Data Science Skills for 2016 - Feb 12, 2016.
As demand for the hottest job is getting hotter in new year, the skill set required for them is getting larger. Here, we are discussing the skills which will be in high demand for data scientist which include data visualization, Apache Spark, R, python and many more.
- Does Machine Learning allow opposites to attract? - Feb 11, 2016.
Most online dating sites use 'Netflix-style' recommendations which match people based on their shared interests and likes. What about those matches that work so well because people are so different - here is my example.
- 4 Reasons Why We Need More Women In Big Data - Feb 10, 2016.
Gender imbalance in the workforce has been highlighted alarmingly during the recent years. Here, we are providing you a couple of reasons, including the inherent advantage and lack of stereotype for role to hire women data scientists.
- Deep Learning is not Enough - Feb 9, 2016.
Deep Learning has real successes, but is not enough to reach artificial intelligence, according to latest KDnuggets Poll. For more complex problems, should pure neural-net approaches be combined with symbolic, knowledge-based methods?
- Top 10 TED Talks for the Data Scientists - Feb 9, 2016.
TEDTalks have been a great platform for sharing ideas and inspirations. Here, we have sifted ten interesting talks for the data scientist from statistics, social media and economics domains.
- New Tools Predict Markets with 99.9% certainty - Feb 8, 2016.
Predicting financial markets is a relatively new field of of research, it is cross-disciplinary, it is difficult and requires some insight into trading, computational linguistics, behavioral finance, pattern recognition, and learning models.
- Money does buy votes, unless you are Jeb Bush - Feb 3, 2016.
Can money buy votes? In Iowa republican caucuses Jeb Bush spent about $2,700/per vote, with little to show. However, without Jeb, there is a strong correlation between money and votes, with $210/vote on average. We also find that spending more time in Iowa does not help.
- On Why Sequels Are Bad and Red Light Cameras Aren’t As Effective - Feb 3, 2016.
Regression to the mean is a statistical phenomenon whereby extreme observations will tend to decrease (regress) towards the mean on subsequent readings. Regression to the mean is essentially a result of selection bias, learn more about it.
- Four Major Predictions for Predictive Analytics and Big Data in 2016 - Feb 2, 2016.
2016 will usher in some unmissable results of the Information Age’s latest contribution, the more effective execution of major operations across sectors with predictive analytics.
- Data scientists keep forgetting the one rule - Feb 2, 2016.
“Correlation does not imply causation”. Yet data scientists often confuse the two, succumbing to the temptation to over-interpret. And that can lead us to make some really bad decisions from data.
- Peering into the Black Box and Explainability - Feb 2, 2016.
In many domains, where data science can be a game changer, and the biggest hurdle is not collecting data or building the models, it is Understanding what they mean.
- The Top A.I. Breakthroughs of 2015 - Feb 2, 2016.
Learn about the biggest developments of 2015 in the field of Artificial Intelligence.
- AI Supercomputers: Microsoft Oxford, IBM Watson, Google DeepMind, Baidu Minwa - Feb 1, 2016.
In the world of AI, this is the equivalent of the US and USSR competing to put their guy on the moon first. Here is a profile of some of the giants locked into the AI space race.