Onur Akpolat has put together A curated list of awesome big data frameworks and resources. The list is very extensive and includes: NoSQL databases, machine learning libraries, frameworks, filesystems and more.
On a similar note, Joseph Misiti has compiled a large list of machine learning specific resources. The list is titled, Awesome Machine Learning, and it includes resources for various languages, NLP, visualization, and more.
Both lists are on Github, so if you notice something missing from the list, feel free to add it. Contributions are welcome.
It is back-to-school time, and here are some papers to keep you busy this school year. All the papers are free. This list is far from exhaustive, but these are some important papers in data science and big data.
- PageRank – This is the paper that explains the algorithm behind Google search.
- MapReduce – This paper explains a programming model for processing large datasets. In particular, it is the programming model used in hadoop.
- Google File System – Part of hadoop is HDFS. HDFS is an open-source version of the distributed file system explained in this paper.
These are 2 of the papers that drove/started the NoSQL debate. Each paper describes a different type of storage system intended to be massively scabable.
- Random Forests – One of the most popular machine learning techniques. It is heavily used in Kaggle competitions, even by the winners.
Are there any other papers you feel should be on the list?
This is a wonderful talk by Max DeMarzi (he has a very informative blog as well). If you are new to NoSQL or Graph Databases, I highly recommend this video.
One comment stuck out for me:
You’re never gonna run out of nodes when you get to half a trillion…
That is a really big number, but I wonder how many years that statement will stand. If you have any thoughts, please leave a comment.
ChiSC: Max DeMarzi – Is Your Problem a Graph Problem? from 8th Light on Vimeo.
Michael Koploy wrote 3 Secrets for Aspiring Data Scientists about what it takes to enter a career as a data scientist. He lays out 3 steps:
- Sharpen Your Scientific Saw – Hone your math and science skills
- Learn the Language of Business – Data Scientists need to explain the data in business terms
- Keep Adding to Your Technical Toolbelt – Learn all the tools you can (NoSQL, Excel, Hadoop,…)
The article is a nice read. http://blog.softwareadvice.com/articles/bi/3-career-secrets-for-data-scientists-1101712/
10gen, the company behind MongoDB, will be offering some free webinars this fall. This webinar series is targeted at using MongoDB with Java. 10gen has been running successful webinars for a long time, so I would high recommend any/all of the following sessions.
Kristof Kovacs put together an excellent comparisons of the different NoSQL products. Here are the products it covers.