Overview of the most interesting materials on data analysis and machine learning No. 2 (June 16 - 23, 2014)

In the next review of the most interesting materials on the topic of data analysis and machine learning, quite a lot of attention is paid to the popular set of machine learning algorithms Deep Learning and its practical application. Several articles are devoted to what there are ways for your own development as a specialist in data analysis and machine learning. A few articles also cover topics such as Data Engineering and look at such popular products as Cassandra and Apache Kafka. But this issue begins with an overview of online courses starting in the near future related to the topic of data analysis and machine learning.

Data Science Online Courses (MOOC) Coming Soon

• Machine Learning  (Coursera - Stanford University)
  One of the most famous Machine Learning courses, taught by Stanford University professor Andrew Ng. The course began on June 16 and will last 10 weeks. The course is quite simple and clear, it does not require any special knowledge for its successful completion, while it covers quite a lot of Machine Learning areas. You can still have time to register for this session of the course, having managed to pass the first test.
• Mathematical Biostatistics Boot Camp 1  (Coursera - Jonhs Hopkins University)
  The first part of the course on biostatistics from Johns Hopkins University. It began on June 16 and will last 7 weeks. It is an unofficial addition to the specialization of Data Science from the same university. Well covers the basics of statistics and probability theory. Again, you can still manage to register for this session of the course, having managed to pass the first test.
• Introduction to Data Science  (Coursera - University of Washington)
  A course on the basics of Data Science from the University of Washington. The course starts on June 30 and will last 8 weeks. One of the most popular online courses on the basics of Data Science.
• SABR101x Sabermetrics 101: Introduction to Baseball Analytics  (edX - Boston University)
  Although the course began in early May, it is still not too late to join it, since the deadline for passing tests for all modules is July 18. The course explains many aspects of Data Science and Big Data based on an analysis of sports statistics (in this case, baseball).

Data Analysis and Machine Learning Materials

• A series of materials on the popular machine learning technique Deep Learning:
  • Possible problems with the practical use of Deep Learning  [EN] The
    material is devoted to potential problems that may be encountered by someone who uses Deep Learning algorithms in machine learning.
  • Lecture on the practical application of Deep Learning  [EN]
    The technique of machine learning Deep learning has recently gained popularity. In the next video, Adam Gibson explains the details of this technology at a fairly simple level for beginners.
  • Foundations of the Learning Deep  [to EN]
    An excellent collection of articles on the basics of Deep Learning.
• Preparing data for analysis using the Pandas library  [EN]
  Typically, data for analysis is initially raw and requires additional processing. This material will be interesting to those who use Python SciPy when analyzing data. The article talks about the practical application of the Pandas data processing and analysis library.
• RStudio product family  [EN]
  An article about the RStudio product line and its capabilities in data analysis.
• Data Science Startups Ideas  [RU]
  A set of potentially interesting ideas for startups in the field of Data Science.
• Machine Learning with Scikit-learn  [EN]
  An excellent overview of Python machine learning library features Scikit-learn.
• Kaggle competition does not teach you how to machine learning  [EN]
  An interesting point of view on the question of the relationship between the competition on Kaggle and real-life problems in machine learning. Thoughts are quite controversial, although it certainly makes sense to get acquainted with them.
• List of materials for preparing for an interview for a data analysis specialist position  [EN]
  A good collection of articles on data analysis. It will also be extremely useful before preparing for an interview for a position as a data analysis specialist.
• Summer season in machine learning  [EN] The
  summer is usually the holiday season, but it also means that more time can be devoted to machine learning competitions. This article lists interesting opportunities for developing your data analysis and machine learning skills in the summer.
• The Best Algorithm for Machine Learning  [EN]
  Another useful article by MachineLearningMachinery.com, asking a question popular in the data analysis community about which machine learning algorithm is the best and is it right to pose the question in this way.
• KDnuggets data analysis tools popularity rating  [EN]
  Analysis of the popularity of various tools in the field of Data Mining and Data Science from one of the most popular resources on this topic.
• Build an ML Portfolio  [EN] This
  article provides very valuable advice on the importance of creating your own small Machine Learning Portfolio. This can be an important aspect in developing your data analysis career.
• Essential Machine Learning Equipment  [EN]
  Useful article about the approaches you need to apply to your equipment in data analysis and machine learning.
• Discussion of the free version of SAS  [EN]
  SAS releases a free version of its product. The article discusses the details of this version.
• Cassandra architecture and performance of this product  [EN] A
  fresh overview of the popular NoSQL Cassandra solution and comparison of its performance with other leaders of NoSQL solutions such as MongoDb, Couchbase, HBase.
• Apache Kafka: a new generation of distributed messaging systems  [EN]
  Overview of the new exchange Apache Kafka messaging system.
• What hinders your development in the topic of data analysis  [EN]
  An excellent article that discusses the problems and obstacles to your development in the field of data analysis and machine learning.
• Overview of the book Practical Data Science with R  [EN]
  Overview of the new machine learning book Practical Data Science with R, as well as other literature on the subject.
• Self-development plan in the field of machine learning  [EN]
  Material that talks about how to competently build your self-development path in the subject of machine learning.

Previous issue:  Overview of the most interesting materials on data analysis and machine learning No. 1 (June 9 - 16, 2014)