Learn how to build zoomable line charts using FusionCharts’ core JS library and its jQuery charts plugin, and get started making some beautiful data visualizations for the web.
Graphical representations of high-dimensional data sets are the backbone of exploratory data analysis. We examine 2 of the most commonly used methods: heatmaps combined with hierarchical clustering and principal component analysis (PCA).
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.
Github provides a number of open source data visualization options for data scientists and application developers integrating quality visuals. This is a list and description of the top project offerings available, based on the number of stars.
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.
Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.
Data visualization is on the rise nowadays. This step-by-step tutorial covers the process of creating your first data visualization using FusionCharts.
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?
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.
We have made tremendous progress in the field of data analysis and on the other, our technology is getting smart. IBM has taken a solid stride in the direction of Artificial Intelligence by unveiling its supercomputer IBM Watson, learn what it can do, its adopters and what it holds for the future.
Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.
We compare Gartner 2016 Magic Quadrant Advanced Analytics Platforms vs its 2015 version and identify notable changes for leaders and challengers: SAS, IBM, RapidMiner, KNIME, Dell, Angoss, and Microsoft.
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.
We review Data Scientist Valentine's Day options with several topical cartoons, including Scarledoopython, Neural net predictions, and dating algorithm adjustments.
Want to learn about the field of text mining, go on an adventure with Sherlock & Watson. Here you will find what are different sub-domains of text mining along with a practical example.
Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?
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.
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.
KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.
Databricks gives us an overview of the spark-sklearn library, which automatically and seamlessly distributes model tuning on a Spark cluster, without impacting workflow.
Gain some insight into a variety of useful datasets for recommender systems, including data descriptions, appropriate uses, and some practical comparison.
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.
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.
Data Visualization is a handy tool which can lead to interesting discoveries about the data, which otherwise wouldn’t have been possible. But, there are common mistakes which could produce the misdirecting results. Learn what are they and how you can avoid them.
New KDnuggets Cartoon looks at a creative new way of achieving even better results and breaking through Machine Learning barriers with even "deeper" Deep Learning approach.
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.
Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers
21 Must-Know Data Science Interview Questions and Answers
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