Overview of the most interesting materials on data analysis and machine learning № 4 (June 23 - July 7, 2014)

The last issue of the review was entirely devoted to online courses on Data Science. This release of the review of the most interesting materials on data analysis and machine learning will contain links to recent materials on the subject. In this release, a number of materials are devoted to the important topic of data visualization. There are several articles that describe small practical examples of data analysis. As usual, many articles are devoted to machine learning algorithms, including several articles devoted to popular machine learning algorithms Deep Learning and Random Forest. There are also some links to interesting videos.

Data analysis and machine learning materials

• Visualization with D3.js [EN]
  Some examples of data visualization using the D3.js library
• Data science, big data and statistics. Now all together (Video lecture) [EN]
  Terry Speed, Professor Emeritus of Berkeley, talks about combining traditional statistics with data science and big data.
• Top Twitter Data Analytics [EN]
  Microblogging best data analyst.
• Markov Chains [EN]
  Fundamentals of Markov chains in simple language.
• Articles and Data Analysis Resources [EN]
  A fresh list of interesting articles from leading data analyst.
• 25 Popular Data Specialists on LinkedIn [EN]
  A list of 25 popular data specialists and their blogs on LinkedIn.
• Visualization techniques that you know from childhood [EN]
  Article about 6 simple, but useful principles of visualization.
• Watson and machine learning [EN]
  An interesting article about the potential use of IBM Watson in various areas of life.
• Why becoming Data Scientist is not so easy [EN]
  The article tries to explain that it’s not enough to complete one or two online Machine Learning courses to be Data Scientist.
• Deep Learning with Hadoop (Video Lecture) [EN]
  Tools and machine learning technologies are constantly evolving. Deep Learning Machine Learning Technology is becoming increasingly popular. In this video, Josh Patterson and Adam Gibson talk about the parallelization capabilities of Deep Belief Networks in Deep Learning using the Hadoop YARN framework and the Iterative Reduce library.
• Using the Facebook API with R [EN]
  A small example of solving a practical problem using the Facebook social network and R.
• Books on data visualization [EN]
  A large list of 35 books dedicated to data visualization.
• 12 interesting books and online resources on R [EN]
  A list of 12 useful books and online resources for learning the R programming language.
• Deploying the product to R [EN]
  Another article from the series of articles on machine learning, using the programming language R.
• Machine learning is also a competition at Kaggle [EN]
  An interesting answer to the article “Machine learning isn't Kagglelations” .
• Machine Learning Communities [EN]
  A good article that gives a brief description of the currently existing machine learning communities.
• Books on machine learning using R [EN]
  Useful list of machine learning literature using R.
• Is Data Scientist more than Data Analyst? [En]
  A small article on the comparison of two concepts such as Data Scientist and Data Analyst
• Basics of data analysis with Python [EN]
  The article is devoted to the first steps in data analysis using Pyhton and additional libraries.
• Cayley: open graph database [EN]
  A short list of the advantages of the open source Cayley graph database.
• Probabilistic models: from naive Bayes to LDA, part 1 [RU]
  Another article on the theoretical foundations of data analysis. In this case, we will discuss probabilistic models.
• List of open resources useful for machine learning. [En]
  An interesting set of links to free useful resources, as well as data sets for machine learning.
• Using Galene on LinkedIn [EN]
  The story from one of the leading engineers of LinkedIn about how the search architecture on LinkedIn changed after switching over to using the search platform Galene.
• Structured and non-structured data types [EN]
  A small article about the difference between structured and unstructured and melon types.
• Do I need a degree in order to be a Data Scientist? [En]
  Leading data analysts answer the interesting question of whether a degree is needed to be a Data Scientist.
• Google I / O 2014 - Biological Model-Based Artificial Intelligence Models (Raymond Kurzweil) (Video Lecture) [EN]
  An interesting lecture on artificial intelligence from renowned scholar and futurologist Raymond Kurzweil with Google I / O 2014.
• Cloud Storage Comparison 2014 [EN]
  Fresh infographics compared cloud storage.
• Domino - a modern data analysis platform [EN]
  A small article about the new flexible system for analyzing data Domino.
• How to start machine learning [EN]
  An excellent article about how a newbie can quickly get into the topic of machine learning and begin to deal with real practical tasks.
• Image Clustering [EN]
  Clustering similar images using MapReduce, with code examples in C # and R.
• Analyzing entries on Google+ [EN]
  A small example of analyzing entries on Google+ using the R programming language.
• SAS and Revolution R Enterprise Performance Comparison [EN]
  A small article on comparing the performance of SAS and Revolution R Enterprise from Revolution Analytics.
• Andrew Ng Talks About Deep Learning (Video Lecture) [EN]
  Stanford University professor Andrew Ng talks about Deep Learning at a machine learning meeting in Paris.
• Comparison of CART and Random Forest Algorithms (Part 1) [EN]
  The first part of the comparison of popular algorithms CART (Classification and Regression Trees) and Random Forest.
• Comparison of CART and Random Forest Algorithms (Part 2) [EN]
  Continuation of the comparison of machine learning algorithms CART (Classification and Regression Trees) and Random Forest.
• Comparing In-Memory Database and In-Memory Data Grid [EN]
  Comparison of two popular approaches to working with data.
• MongoDB with Google Cloud Platform [EN]
  A brief article about working with MongoDB on the Google Cloud Platform (Google Cloud Platform).
• Generation and visualization of multidimensional random variables using R [EN]
  A small sample of code for generating and visualizing multidimensional random variables in R.
• Data Shinobi 3 [EN]
  The continuation of a series of articles on data analysis, in the third part the question of various directions in data analysis is raised.
• Introduction to Hadoop [EN]
  Another simple and brief description of Hadoop.
• What is Deep Learning and why is there so much noise around this algorithm? [En]
  A small article on the popular set of machine learning algorithms Deep Learning.

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