Big Data: Difference between revisions

Revision as of 04:07, 15 May 2018

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 )
 https://www.kaggle.com/currie32/the-importance-of-cleaning-text/notebook
4. analyze the data ( also see https://en.wikipedia.org/wiki/Data_analysis )
5. repsetn the data / visualization
6. start again.

node management
key value stores
storage management
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?

GFS

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 steps:

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

check if the data is intuitive: for example is the age accurate?; are errors due to random error , or is the error due to systematic bug? possibly still useful of not intutive

how was the data generated?: was train and test set created with an algo?; create syntetih feature: "is correct" ? possobly a signal; range of time on train range much greater ) 10X )than test set.; decode faures types, and guess what it's used for, exp in the case of anonymized data, DOB? event counts, timestamp?

techniques:

counting repeated values:

train_set.featureX.mean(15)
train_set.featureX.std(15)
train_set.featureX.value_counts().head(15)

featurex_unique = train_set.featureX.unique()
featurex_unique_sorted = np.sort(featurex_unique)

example: https://hub.coursera-notebooks.org/user/hnlrfyqblxjsscsosbsvlx/notebooks/readonly/reading_materials/EDA_video2.ipynb

Usefule functions in python

df is pandas data frame
df.types() - guesses type
 three types come out:
 object ( fraught with danger : can detect 'object' when mostly numbers but some "odd" values, like text. )
 float
 int ( can be binary 1|0 , event counts, or catagory with label encoder