Developed at MIT’s Sloan School of Management in 1950s system dynamics is a methodological approach to model the behavior of complex systems, where change in one component leads to change in others (like the dominos effect with feedback loops added). This approach is widely applied in industries such as healthcare, disease research, public transportation, business management and revenue forecasting. The most famous application of system dynamics probably is in… Continue
Added by Mab Alam on April 23, 2018 at 5:30pm —
Summary: Not everyone wants to invest the time and money to become a data scientist, and if you’re mid-career the barriers are even higher. If you still want to be deeply involved in the new data-driven economy and well paid, the growth rate and opportunities as a data engineer or business analyst need to be on your radar screen.
Added by William Vorhies on April 23, 2018 at 3:41pm —
In this article, couple of implementations of the support vector machine binary classifier with quadratic programming libraries (in R and python respectively) and application on a few datasets are going to be discussed.
The next figure describes the basics of Soft-Margin SVM (without kernels).
SVM in a nutshell
- Given a (training) dataset consisting of positive and negative class instances.
- Objective is to find…
Added by Sandipan Dey on April 23, 2018 at 9:30am —
The value analytics brings to a business is inversely related to the time it takes to create said analysis. In a traditional world of quarterly lookbacks, an analyst’s output may be interesting, but its ability to drive real relevant change is hindered by time and effort. The fundamentals that were once present may have all changed.
This is why real-time analytics are a breakthrough for a business. If you can take… Continue
Added by Taylor Barstow on April 23, 2018 at 3:00am —
Data scientists spend 80% of their time preparing and cleaning their data. They spend the other 20% of their time complaining about preparing and cleaning their data.
This was posted by Kirk Borne on his Twitter account. Not sure who created the cartoon. Do we all spend 80% on our time on something, and the remaining 20% on something else? In my case, I spend 20% of my time writing articles (usually research articles that the layman can understand, and sometimes articles like… Continue
Added by Vincent Granville on April 22, 2018 at 4:00pm —
These are not business questions, but soft questions that should make any PhD candidate relaxed, even intrigued, and open to talk freely. There is no wrong answer, these are open questions, but some answers could hint that the candidate is still in his/her PhD bubble, feeling superior, not flexible, and unable to see the big picture behind the apparently innocent question. These questions were asked discretely, none of the responders knew about my PhD mathematical background. …
Added by Vincent Granville on April 21, 2018 at 3:30pm —
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
- SQL + Notebooks + Charts. All in one platform. …
Added by Vincent Granville on April 21, 2018 at 6:30am —
Four pictures were posted recently on Data Science Central, and have immediately become popular. They are designed as one-page tutorials on some specific (basic or advanced) topics. Click on the links below to find those related to the subjects that you are interested in.
Four Great Pictures Illustrating Machine Learning Concepts
Added by Vincent Granville on April 20, 2018 at 1:00pm —
The chief data officer (CDO) is beginning to be recognized as the lynchpin for tackling one of the most important problems in enterprises today – leading the transformation to a data-driven culture. Often with a budget of less than $10M, one of the biggest challenges and opportunities for CDOs is making the self-service opportunity a reality by bringing corporate data… Continue
Added by Jay Bourland on April 20, 2018 at 6:00am —
To say that AI is big data is to overstate things a bit. And yet, without big data, AI wouldn’t be where it is today. In the last few decades, the two technologies have advanced in lock-step. Largely because without big data, however clever the AI programmers were, they couldn’t get past the theoretical stage.
Mainly, this is down to what big data is used for. Through data, it is possible to train AI and thereby give them the opportunity to learn things. The more data is available,… Continue
Added by Alaine Gordon on April 20, 2018 at 12:00am —
Added by Krishna Pera on April 19, 2018 at 10:30am —
Here is our selection of featured articles and resources posted since Monday.
Added by Vincent Granville on April 19, 2018 at 9:30am —
When our customers ask us what the best data warehouse is for their growing company, we consider the answer based on their specific needs. Usually, they need nearly real-time data for a low price without the need to maintain data warehouse infrastructure. In this case, we advise them to use…
Added by Luba Belokon on April 19, 2018 at 5:30am —
For people working in Artificial Intelligence, the term “Human-in-the-Loop” is familiar i.e. a human in the process to validate and improve the AI. There are many situations where it applies, as many as there are AI applications. However. there are still some distinct different ways it can be deployed even within the same application.
Contact Center Example
Let’s take for example the automation of a contact center. A… Continue
Added by Dan Somers on April 19, 2018 at 5:30am —
I described here a strange type of function, that is nowhere continuous but relatively easy to integrate using probabilistic arguments. I call it the fractional part of parameter p of a function g(x), and it is denoted as g(x, p). We focus here on g(x) = exp(x). It is obtained by removing a number of terms (usually infinitely many) in the Taylor series of g(x). For instance, by removing all… Continue
Added by Vincent Granville on April 19, 2018 at 5:00am —
Analysis-paralysis - sounds familiar? Almost, as if you are driving on ice, engine is loud, wheels are spinning - but you not really moving forward.
Here are strategies that may help you dealing with this serious data science condition:
- Realize that chasing perfection is chasing shadows. And simply stop doing…
Added by Goran Dragosavac on April 19, 2018 at 1:00am —
Appropriate data, audience understanding & building a good story for meaningful & powerful visual dashboards that influence & engage the audience for actionable insights; is the key. Here is an infographic is about storytelling with visualization:
Added by Chirag Shivalker on April 18, 2018 at 11:00pm —
Statistical Analysis is a way of collecting, presenting and exploring large amounts of data in order to discover underlying patterns and trends. It can be especially useful in banking, manufacturing or retail where knowing the future patterns might greatly benefit the businesses. Not without reason, it resembles and can cooperate with blockchain - a new tech… Continue
Added by James Mason on April 18, 2018 at 2:30pm —
Cambridge Analytica’s wholesale scraping of Facebook user data is big news now, and people are “shocked” that personal data is being shared and traded on a massive scale on the internet. But the real issue with social media is not harm to individual users whose information was shared, but sophisticated and sometimes subtle mass manipulation of social and political behavior by bad actors, facilitated by deceit, fraud, and amplification of lies that spread easily through societal… Continue
Added by Peter Bruce on April 18, 2018 at 9:00am —
As a provider of social networking services (social media) should filter out what data can be accessed by third parties. A sign "Like" for some people may not be so important, but do you know that based on that sign I can know who can be said to be influential (central person). It does not depend on the number of friends or followers, but…
Added by Jeefri A. Moka on April 18, 2018 at 8:30am —