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• Building Zoomable Line Charts in jQuery - Feb 25, 2016.
  
  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.

  Data Visualization, FusionCharts, Javascript

• Tree Kernels: Quantifying Similarity Among Tree-Structured Data - Feb 23, 2016.
  
  An in-depth, informative overview of tree kernels, both theoretical and practical. Includes a use case and some code after the discussion.

  Pages: 1 2 3

  Decision Trees, Graph Mining, Web Mining

• A comparison between PCA and hierarchical clustering - Feb 23, 2016.
  
  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).

  Clustering, Data Visualization, Life Science, PCA, Qlucore

• 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.

  Duncan Watts, Facebook, Small World

• Top 10 Data Visualization Projects on Github - Feb 22, 2016.
  
  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.

  D3.js, Data Visualization, GitHub, Matthew Mayo, Open Source, Top 10

• 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.

  Ebola, Enlitic, Healthcare, MJFF

• 21 Must-Know Data Science Interview Questions and Answers, part 2 - Feb 20, 2016.
  
  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.

  Pages: 1 2 3

  Anomaly Detection, Data Science, Data Visualization, Overfitting, Recommender Systems

• Getting Started with Data Visualization - Feb 19, 2016.
  
  Data visualization is on the rise nowadays. This step-by-step tutorial covers the process of creating your first data visualization using FusionCharts.

  Data Visualization, FusionCharts, Javascript

• 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?

  Caffe, Deep Learning, Feature Engineering, Open Source, TensorFlow

• Data Lake Plumbers: Operationalizing the Data Lake - Feb 18, 2016.
  
  Gain insight into data lakes, their benefits, when they are appropriate, and how to operationalize them. How do they compare to the data warehouse?

  Data Lake, Data Warehouse, ETL, Hadoop

• 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.

  Big Data, Cars, IoT

• How IBM Watson is Taking on The World - Feb 18, 2016.
  
  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.

  Artificial Intelligence, DeZyre, IBM, Watson

• Amazon Machine Learning: Nice and Easy or Overly Simple? - Feb 17, 2016.
  
  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.

  Amazon, Classification, Machine Learning, MLaaS

• Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers - Feb 16, 2016.
  
  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.

  Advanced Analytics, Dell, Gartner, IBM, Knime, Magic Quadrant, RapidMiner, SAS

• 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.

  Academics, arXiv, Deep Learning, ICLR, Neural Networks, Yann LeCun, Yoshua Bengio, Zachary Lipton

• Data Scientist Valentine’s Day Collection - Feb 13, 2016.
  
  We review Data Scientist Valentine's Day options with several topical cartoons, including Scarledoopython, Neural net predictions, and dating algorithm adjustments.

  Cartoon, Humor, Valentine's Day

• Elementary, My Dear Watson! An Introduction to Text Analytics via Sherlock Holmes - Feb 12, 2016.
  
  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.

  Dato, NLP, Sherlock Holmes, Text Analytics

• Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn - Feb 12, 2016.
  
  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?

  Deep Learning, Google, Matthew Mayo, Python, scikit-learn, TensorFlow

• 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.

  Apache Spark, CrowdFlower, Data Science, Python, Skills, SQL

• 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.

  Love, Machine Learning, Online Dating, Recommendations

• 21 Must-Know Data Science Interview Questions and Answers - Feb 11, 2016.
  
  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.

  Pages: 1 2 3

  Bootstrap sampling, Data Science, Interview Questions, Kirk D. Borne, Precision, Recall, Regularization, Yann LeCun

• Auto-Scaling scikit-learn with Spark - Feb 11, 2016.
  
  Databricks gives us an overview of the spark-sklearn library, which automatically and seamlessly distributes model tuning on a Spark cluster, without impacting workflow.

  Apache Spark, Databricks, Open Source, scikit-learn

• 9 Must-Have Datasets for Investigating Recommender Systems - Feb 11, 2016.
  
  Gain some insight into a variety of useful datasets for recommender systems, including data descriptions, appropriate uses, and some practical comparison.

  Datasets, Lab41, Recommender Systems

• 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.

  Big Data, Hiring, Women

• 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.

  Data Science, Hans Rosling, Social Networks, Statistics, TED

• Avoid These Common Data Visualization Mistakes - Feb 8, 2016.
  
  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.

  Data Visualization, Mistakes

• Cartoon: Deeper Deep Learning - Feb 1, 2016.
  
  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.

  Cartoon, Deep Learning

• 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.

  AI, Baidu, Chris Pearson, DeepMind, Google, IBM Watson, Microsoft, Minwa