Big Data

Overview

What is data science ? what is the day to day?

1. Be given a problem 
2. examine the data, decide on what to collect. also see /Exploratory Data Analysis (EDA)
3. clean the data ( much of the time is spend here )
4. analyze the data ( also see https://en.wikipedia.org/wiki/Data_analysis )
5. repsetn the data / visualization
6. start again.

1. node management
2. key value stores
3. storage management
4. job management

Key aspects:

• Integration
• Analysis
• Visualization
• Work Load Optimization
• Security
• Governance

Key Values Stores

list:
http://www.metabrew.com/article/anti-rdbms-a-list-of-distributed-key-value-stores

http://www.project-voldemort.com/voldemort/

https://en.wikipedia.org/wiki/Redis

Storage

Oracle Cluster File System (OCFS)

Old?

• https://oss.oracle.com/projects/ocfs2/
• https://oss.oracle.com/projects/ocfs/dist/documentation/RHAS_best_practices.html

Hadoop

• get key value with hbase (no sql)
• sql with hive

Examples

Log data

Hadoop Analysis of Apache Logs Using Flume-NG, Hive and Pig
http://cuddletech.com/blog/?p=795

http://www.elasticsearch.org/ - also Elastic Search

JP GOES Sea Surface temperature data

"Geostationaary Operational Environmental Satellites (GOES) 6km Near Real-Time Sea Surface Temperature (SST) Documentation"

ftp://podaac-ftp.jpl.nasa.gov/allData/goes/L3/goes_6km_nrt/docs/goes_sst_doc.html

http://podaac-w10n.jpl.nasa.gov/w10n/allData/goes/L3/goes_6km_nrt/americas/2016/

what is the format of this data?

Learning Progress and Recognition

https://courses.cognitiveclass.ai/certificates/493c0df647484b2082c76328e46feaa5

https://courses.cognitiveclass.ai/courses/course-v1:BigDataUniversity+ML0101EN+2016_T3/courseware/407a9f86565c44189740699636b4fb85/d82ba5edac4f40efa334fff96b944b34/

deep learning https://campus.datacamp.com/courses/deep-learning-in-python/basics-of-deep-learning-and-neural-networks?ex=1

Exploratory Data Analysis (EDA)

what you do when you first get a data set.

what is it: looking into the data, understanding it, getting comfortable with it.

will hep you generate features and build accxureate models

make hypothesis and and have insights

exploring data results in intuition about that data.

careful eda results in insight in to data

visualization is key

visualization -> idea , idea -> visualization

find magic features ,like the promos use versus sent.. the "diff: was 80% acruate.

the point behind EDA is that you _DO NOT_ start making a model. YOu start by trying to understand the data, get an intuitive feel for the data, and to possibly generate some insight.

some stesp:

domain knowledge.
 google your topic, learn a bit about it.
 populate a data dictionary for example

check if the data is inuitive
 for example is the age accurate?

how was the data collected?

tools

Open data - Sources

http://konect.uni-koblenz.de/

https://www.kaggle.com/datasets

Reference

• how do the big guys do it?

• map of tools http://insightdataengineering.com/blog/pipeline_map.html

• "Big data dudes"

• http://scikit-learn.org/stable/

Also See

• On Thinking

• https://github.com/tdhopper/Data-Science-Conference-Bingo

Text Classification with TensorFlow Estimators

https://opendatascience.com/text-classification-with-tensorflow-estimators/