pydata ecosystem基于python的数据分析生态系统
0.
Agenda
Data Science ecosystem
Data Wrangling
Data Analysis
Data Visualization
3 Real Case Demo
Bigger Data Consideration
Spark Data Frame Demo
1.
Data Science Process
Data Collection
Databases
Applications
3rdpart data
Data Wrangling
Enrichment
ETL/Blending
Data
Intergration
Data Analysis
insights
Statistics
Visualization
Modeling
2.
Data Wrangling
Data scientists spend 80% of their time convert data into a usable form.
Clean data:handle messy or missed data
Transform and Extract data
Merge,Join and Reshape data
Time series Resampling
3.Data Analysis
Interactive Data Exploration
Rich visualzation
Satistical Modeling
4.python vs R
TIOBE Index
5.Pros and Cons
R+visualization = perfect match
R,Lingua Franca of Statistics(develop by Statistics)
R is slow
Python is multi-purpose language
Python is challenger for either visualization or essential R packages replacement
6.PyData Ecosystem
Fundamental Libs
numpy\scipy
AdvancedLibs
pandas\sympy\Scikit-lean\xray\Blaze
7.Numpy
High performance N-Arrary operation lib
高性能多维
8.pands
打包
9.Blaze
High-level user interface for databases and array computing systems
10.Spark
11.DataFrame
12.matplotlib
13.seaborn
14.Bokeh
15.IPython
PyconChina2015丁来强Pydata Ecosystem
原文：http://www.cnblogs.com/jsben/p/5018477.html