Data science digest #44 (18 - 24 May 2015)

General info

• The Best Big Data And Business Analytics Companies To Work For In 2015
• How Do I Become a Data Scientist? / Data Science Aspects
• Why are Eight Bits Enough for Deep Neural Networks?
•   R vs Python, why each is better - A report on a free-wheeling Australian meetup discussing "Why R is Better" and "Why Python is Better". What do you think?
• Benchmarking Random Forest Implementations
•  9 popular ways to perform Data Visualization in Python
•  Infographic – Quick Guide to learn Python for Data Science
• Deep Learning Pioneer Pushing GPU Neural Network Limits - Back in the late 1980s, while working in the Adaptive Systems Research Department at AT&T Bell Labs, deep leaning pioneer, Yann LeCun, was just starting down the path of implementing brain-inspired machine learning concepts for image recognition and processing—an effort that would eventually lead to some of the first realizations of these technologies in voice recognition for calling systems and handwriting analysis for banks.
• The Five Elements of Data Science Process
• How Machine Learning Is Eating the Software World - Marc Andreessen famously said in 2011 that software was eating the world. Four years later, that trend has accelerated, only now it appears that machine learning technology is on the cusp of eating software, and that algorithms will take over the world, with a little help from their friends: the APIs.
•  10 Python Machine Learning Projects on GitHub - Here is a list of top Python Machine learning projects on GitHub.
•  9 Python Analytics Libraries - Python & data analytics go hand in hand. Here is a list of 9 Python data analytics libraries.
• Impala Needs Your Contributions
•  Big Data: текущая реальность
•  Как создать искусственный интеллект? История вторая. Алгоритмы интеллектуального поиска и хранения информации
•  Искусственный интеллект, большие данные и дезинформация технологий: интервью профессора Беркли

Theory, machine learning algorithms and code examples

•  Top 10 data mining algorithms in plain English
•  The Unreasonable Effectiveness of Recurrent Neural Networks
•   Survival Analysis with Plotly: R vs. Python
•  My favorite R bug
• Choosing a Learning Algorithm in Azure ML - Machine Learning libraries seek to put state-of-the-art tools into the hands of data scientists, offering dozens of algorithms, each with their strengths and weaknesses. But choosing the right ML algorithm can be daunting for both beginner and experienced data scientists alike. The nature of the data partially drives the decision, constraining the choice to a class of algorithms, say, classification or regression. But often the final choice of algorithm is a black-box mixture of trial-and-error, personal experience and arbitrary selection.
•  Протокол разработки предсказательных моделей, предназначенных для решения бизнес-задач - В отличие от моделей, основное назначение которых заключается в установлении взаимоотношений между предикторами и некоторой переменной-откликом и, как следствие, наиболее распространенных в академической среде, предсказательные модели особенно популярны в мире бизнеса. Это не удивительно, поскольку возможность делать предсказания в отношении критических для бизнеса явлений и процессов дает конкурентное преимущество, а нередко лежит и в основе самого бизнеса (Google, Amazon, Netflix, и т.д.).
•   Пример векторной реализации нейронной сети с помощью Python

Online courses, training materials and literature

•  edX & UTAustinX: Linear Algebra - Foundations to Frontiers (starts June 3, 2015) - Learn the mathematics behind linear algebra and link it to matrix software development.
•  edX & MITx: The Analytics Edge (starts June 2, 2015) - Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
•  edX & BerkleyX: Introduction to Big Data with Apache Spark (starts June 1, 2015) - Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data.
•  Reinforcement Learning Course
•  35 Free Online Books on Machine Learning - This article presents a comprehensive list of 35 free books on machine learning (& related fields) which are freely available online (in pdf format) for self-paced learning.

Videos, podcasts

•  Most Viewed Data Mining Videos on YouTube - The top Data Mining YouTube videos by those like Google and Revolution Analytics covers topics ranging from statistics in data mining to using R for data mining to data mining in sports.
•  Partially Derivative: Episode 23: Political Science Rulez - This week Chris overcompensates for his love of political science while Jonathon continues to be unimpressive.
•   Hadoop 101: Top 10 Hadoop Learning Videos
•  Predictive Analytics Demystified

Data engineering

• SQL and Hadoop: It's complicated - With the 1.0 release of Apache Drill and a new 1.2 release of Apache Hive, everything you thought you knew about SQL-on-Hadoop might just have become obsolete.
• NoSQL Databases: comparing MongoDB, HDInsight, and DocumentDB - KDnuggets team compares 3 major NoSQL databases: MongoDB, DocumentDB, and HDInsight in terms of data models, scalability, availability, query types, and support for transactions.
•  Мой опыт внедрения Apache Cassandra
•  Object Storage — Ближайшее будущее систем хранения данных

Reviews

• Top stories for May 17-23: 7 Methods for Data Dimensionality Reduction; Will the Real Data Scientists Please Stand Up? (KDNuggets.com)
• Weekly Digest - May 25 (DataScienceCentral.com)
• Issue 36 - May 22nd 2015 (DataElixir.com)
• Big Data News 18 May 2015 (MyDataMine.com)
• Stuff The Internet Says On Scalability For May 22nd, 2015 (HighScalability.com)

Previous digest: Data science digest #43 (4 - 19 May 2015)

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