Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…Continue
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.
Featured Resources and Technical ContributionsContinue
Added by Vincent Granville on April 22, 2017 at 6:00am — No Comments
Have your seen the 1996 movie Twister, based on tornadoes disrupting the neighborhoods? A group of people were shown trying to perfect the devices called Dorothy which has hundreds of sensors to be released in the center of twister so proper data can be collected to create a more advanced warning system and save people.
Today if we apply the same…
Added by Sandeep Raut on April 22, 2017 at 4:30am — No Comments
Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data. With the advent of Big Data and sophisticated data mining techniques, the number of variables encountered is often tremendous making variable selection or dimension reduction techniques imperative to produce models with acceptable accuracy and generalization. The temptation to build an ecological model using all available information (i.e., all variables) is hard to…Continue
Added by Valiance Solutions on April 21, 2017 at 9:20pm — No Comments
Almost everyone loves to spend their leisure time to watch movies with their family and friends. We all have the same experience when we sit on our couch to choose a movie that we are going to watch and spend the next two hours but can't even find one after 20 minutes. It is so disappointing. We definitely need a computer agent to provide movie recommendation to us when we need to…
Added by NYC Data Science Academy on April 21, 2017 at 11:00am — No Comments
Contributed by David Letzler.
In 2009 the National Governors’ Association and the Council of Chief State School Officers resolved to develop a set of national education standards known as the Common Core. These were intended to unify what had been to that point a set of highly localized state education standards. The hope of the Common Core was that a…Continue
Added by NYC Data Science Academy on April 21, 2017 at 10:30am — No Comments
Any time series classification or regression forecasting involves the Y prediction at 't+n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence “we cannot make use future…Continue
Added by Rohit Walimbe on April 21, 2017 at 6:00am — No Comments
Poor data governance often leads to a failed data state. Too many times companies, and even silos within a company, ignore the importance of data governance. Such neglect leads to increased cost, decreased compliance, and even a complete failure of a data solution.
A key data governance principal to adhere to is data usage. This is a simple one. If your…Continue
Here is our new selection of featured articles and resources posted since Monday.Continue
Added by Vincent Granville on April 20, 2017 at 8:54am — No Comments
According to IBM, the world generates 2.5 quintillion bytes of data every day. A decent chunk of those quintillion bytes is made up of people asking the experts how to break into and excel in the dynamic, lucrative field of data science. An even larger chunk of those bytes consists of convoluted, contradicting answers to that question.
This is, on one hand, a great thing. Multiple prominent data science innovators are out there giving you free advice on your most pressing questions,…Continue
Added by Lauren Delapenha on April 20, 2017 at 6:30am — No Comments
This is a guest post by Lauren Delapenha. She is an editor at DiscoverDataScience.com, a one-stop resource for learning about the rapidly-evolving field of data science through comprehensive education and career guides.
According to IBM, the world generates 2.5 quintillion bytes of data every day. A decent chunk of those quintillion bytes is made up of people asking the experts how to break into and excel in the dynamic, lucrative field of data science. An even…Continue
Added by Shay Pal on April 19, 2017 at 4:30pm — No Comments
Added by Ashish Sukhadeve on April 19, 2017 at 9:30am — No Comments
If you are considering a Business Intelligence solution, you ought to give some consideration to the concept of Smart Data Visualization and review your prospective solution to determine its capabilities in that regard. Smart Data Visualization provides many benefits to the organization and to the business users, who will leverage the selected BI tools to gather, analyze, share…Continue
Added by Arbuda Dave on April 19, 2017 at 2:30am — No Comments
Added by Rekha Joshi on April 18, 2017 at 12:30pm — No Comments
A while back, I was reading an article posted on Facebook, about Clovis people found alive and well living in Florida, with a picture featuring tribesmen (see below.) The quality of the picture was poor, and the URL was very suspicious: baynews9.com.ddwg.clonezone.link, as to make it appear that it was from Baynews9.com. It turned out that the picture (and thus the whole story) was fake: these people are real…Continue
This article was posted by Bob E. Hayes on Customer think. Bob, PhD is Chief Research Officer at Appuri. He a scientist, blogger and author on CEM and data science.
Data scientists have a variety of different skills that they bring to bear on Big Data projects. These skills cut across Subject Matter Expertise, Technology, Programming, Math & Modeling and Statistics. One valuable…Continue
Added by Emmanuelle Rieuf on April 18, 2017 at 9:00am — No Comments
Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI’s failures as good fodder for YouTube videos. But those failures are starting to add up. It’s time to take a hard look at the weaknesses in AI and where that’s leading us.
Added by William Vorhies on April 18, 2017 at 8:04am — No Comments
We expect that data scientists and analysts should be objective and base their conclusions on data. Now while the name of the job implies that “data” is the fundamental material that is used to do their jobs, it is not impossible to lie with it. Quite the opposite – the data scientist is affected by unconscious biases, peer pressure, urgency, and if that’s not enough – there are inherent risks in the process of data analysis and interpretation that lead to lying. It happens all the time…Continue
When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases.…Continue
Data modeling has been a fixture of enterprise architecture since the 1970’s with ANSI defined conceptual, logical, and physical data schema. As data models developed, so did the availability of templates for business use. Retail banks use similar data models, as do other industries. A shared approach to data modeling advanced the discussion and planning of solutions.
Growth in unstructured data has led to development of tools to search data to identify context or bring…Continue
Added by Paul Stanton on April 16, 2017 at 2:00pm — No Comments