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Added more detailed descriptions to each notebook in the categories kaggle, aws, and spark.

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Donne Martin 2015-04-03 06:34:32 -04:00
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@ -7,27 +7,27 @@ Continually updated IPython Data Science Notebooks geared towards processing big
IPython Notebooks used in [kaggle](https://www.kaggle.com/) competitions.
* [titanic](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/kaggle/titanic.ipynb)
* [titanic](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/kaggle/titanic.ipynb): Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning.
## aws
IPython Notebooks demonstrating Amazon Web Services functionality.
* [aws commands index](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb)
* [s3cmd](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3cmd)
* [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put)
* [s3distcp](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3distcp)
* [mrjob](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#mrjob)
* [redshift](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#redshift)
* [kinesis](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#kinesis)
* [lambda](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#lambda)
* [s3cmd](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3cmd): Interacts with S3 through the command line.
* [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put): Uploads multiple files to S3 in parallel.
* [s3distcp](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3distcp): Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster.
* [mrjob](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#mrjob): Supports MapReduce jobs in Python 2.5+ and runs them locally or on Hadoop clusters.
* [redshift](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#redshift): Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP).
* [kinesis](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#kinesis): Streams data in real time with the ability to process thousands of data streams per second.
* [lambda](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#lambda): Runs code in response to events, automatically managing compute resources.
## spark
IPython Notebooks demonstrating spark and HDFS functionality.
* [spark](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/spark.ipynb)
* [hdfs](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/hdfs.ipynb)
* [spark](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/spark.ipynb): Open-source in-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms.
* [hdfs](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/hdfs.ipynb): Reliably stores very large files across machines in a large cluster.
## python-core