Gleanings from observed technical misunderstandings between business leaders and data scientists (and among data scientists themselves) so dramatic that one could start wondering whether there is something wrong with data science as it is being practiced.
Analytics & Big Data will be involved in every aspect of our lives and we should handle the ethical dilemmas wisely to let innovation contribute more to our lives.
Despite their confidentiality, machine learning models which have public-facing APIs are vulnerable to model extraction attacks, which attempt to "steal the ingredients" and duplicate functionality. The paper at hand investigates.
A gentle reminder as to why we need Data Science, reasons for which even you may have been guilty of offending at some point. A basic topic, to be sure, making it all the more important.
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
A look at beer features to determine whether a specific brew might be better served (pun intended) by being classified under a different style. kNN analysis supported with in-post plots and linked iPython notebook.
By now, we all have realised the power of IoT, Mobile Apps, Big Data and Analytics. Now it’s time to use this power in every possible way for complete well being of everyone in the world. Let’s read this interesting article on Women Health Care Mobile Apps and Data Analytics.
Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.
Respected Data Scientist Daniel Tunkelang shares some insight into data recycling, using data from other contexts to bootstrap your initial statistical models until you can collect live data.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.
The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.
You read that Data Scientist is “The Sexiest Job of The 21st Century”, but there are other jobs profiles and opportunities in Data Science – read about these roles, responsibilities, skills, salary prospects and market demand (also pretty sexy!).
An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.
We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.
The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!
Current Deep Learning successes such as AlphaGo rely on massive amount of labeled data, which is easy to get in games, but often hard in other contexts. You can't play 20 questions with nature and win!
Once upon a time, Artificial Intelligence (AI) was the future. But today, human wants to see even beyond this future. This article try to explain how everyone is thinking about the future of AI in next five years, based on today’s emerging trends and developments in IoT, robotics, nanotech and machine learning.
“Enterprise applications, Cloud, Cognitive computing and IBM Watson”, Yes, you guessed it right. This article talks about highlights of 2016 World of Watson conference organised at Las Vegas,NV.
Why polling has failed in US Presidential election? The home price index offers an apt comparison inasmuch as sample selection is problematic, equally snagging both election predictions and home price futures.
With employers trying to keep up with current data science trends, are data scientists just renamed data analysts? Part 1 of an investigation focuses on the top level numbers and pretty visualisations to highlight key differences.
Many companies seem to go through a pattern of hiring a data science team only for the entire team to quit or be fired around 12 months later. Why is the failure rate so high?
The shocking and unexpected win of Donald Trump of presidency of the United States has once again showed the limits of Data Science and prediction when dealing with human behavior.
Data Science for startups based on data: Minimum Valuable Model, a new concept to avoid a full scale 95% accurate data science model. Want to know more about MVM? Have a look at this interesting article.
Read the second and final part of this overview of the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.
There might be several different ways to think around machine intelligence startups; too narrow of a framework might be counterproductive given the flexibility of the sector and the facility of transitioning from one group to another. Check out this categorization matrix.
Are you an R user considering learning Python? Here's some insight into what you may be up against, and what, specifically, you may find frustrating. But don't worry, it's not all terrible.
The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.
Continuous improvement for IoT through AI / Continuous learning
Trump, Failure of Prediction, and Lessons for Data Scientists
| 
