2016 Feb Tutorials, Overviews

• 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.

  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

• 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.

  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

• 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

• 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

• Privacy – what is it? - Feb 16, 2016.
  Bothered about the “big brother” knowing everything about you? We are explaining what exactly the privacy means in this data driven world, what are the different types, the major concerns and its limitation.

  Big Data, Privacy

• Bayes Theorem for Computer Scientists, Explained - Feb 16, 2016.
  Data science is vain without the solid understanding of probability and statistics. Learn the basic concepts of probability, including law of total probability, relevant theorem and Bayes’ theorem, along with their computer science applications.

  Bayes Theorem, Probability, Statistics

• Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results - Feb 12, 2016.
  Get a handle on ensemble methods from voting and weighting to stacking and boosting, with this well-written overview that includes numerous Python-style pseudocode examples for reinforcement.

  Boosting, Ensemble Methods, Machine Learning, Python

• 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

• 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.

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

• Change in Perspective with Process Mining - Feb 9, 2016.
  Process mining is focused on the analysis of processes, and is an excellent tool in particular for the exploratory analysis of process-related data. Understand how effectively use it as an exploratory analysis tool, which can rapidly and flexibly take different perspectives on your processes.

  Data Exploration, Data Science, Process Mining

• 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

• Apache Spark: RDD, DataFrame or Dataset? - Feb 3, 2016.
  There are now 3 Apache Spark APIs. Here’s how to choose the right one.

  Apache Spark, API, Dataset, Java, RDD, Scala