# Data-Science-in-R
Data Science in R —— fundamental and industry applications
### Background
ABOUT ME :
I started using R from 2012, for some text mining jobs. For these years, I have been working in interenet companies including human resources(NASDAQ:ZPIN), games(NASDAQ: CYOU) and some entrepreneurship project. Now , I am in one of the largest technology companies in the world, TENCENT .
OBJECT:
To build a framework of how to use R to deal with common analysis jobs, and how to combine models or APIs build by R to the whole big-data architecture of the enterprise.
### CATALOG
1. The Data Scientist’s Toolbox： of course R, and some of my view about big data landscape, includes python, spark, hadoop, GitHub , markdown etc. And ideas like reproducible Research.
2. Working with R： basic grammar you can find in otherplaces, trying to be different.
3. Getting and Cleaning Data： read local data, using APIs , db, or odbc/jdbc and etc jobs.
4. Exploratory Data Analysis： descriptive statistics, visualization, espcially in multivariate sense.
5. Statistical Inference and Variance Analysis： they are a important part of statistics , but , seems not that important in nowadays data sciences. Let's see what we can do.
6. Regression Models and Classifications： main stream of machine learning
7. Unsupervised Machine Learning: clustering and LOF
8. Web Crawler and NLP: some applications
9. Time Series Analysis: practical way, arima , machine learning and other way
10. always changing my mind…
### CONTACT
chong.ding83 at gmail.com
https://www.linkedin.com/in/chong-ding-38b59837/
### LOG
2015-4-16 Angrew Ng 大神的ML课程也是很赞的哈！想学ML的小白推荐哈！
2016-4-11 最近即将启动一次重大更新！整合我在鹅厂内部课程和这一年的相关积累
2016-6-29 时间序列分析的基础内容添加完了
2017-6-21 从印象笔记迁移代码
2017-12-23 Switch to English
data葱的R语言数据科学项目_Data-Science-in-R.zip （227个子文件） 
cameras.csv 9KB tdata1.csv 4KB boardgame.csv 253B
~$ong Ding.docx 162B .DS_Store 18KB
k-means.html 697KB
R base plot.jpg 127KB timeSeriseAll.md 26KB base_plot.md 18KB r_programming_3.md 13KB advanced_plot.md 10KB exploratory_data_analysis_3.md 10KB kmeans-r-python.md 9KB reproducible_research.md 7KB backtest_1_single.md 7KB ggplot2.md 7KB exploratory_data_analysis_2.md 6KB k-means.md 6KB lattice.md 5KB getting_data_.md 5KB environment.md 4KB exploratory_data_analysis_1.md 4KB week1.md 3KB igraph.md 2KB rmarkdown.md 2KB README.md 2KB views.md 1KB README.md 415B
006typesOfErrors.pdf 1.68MB 019predictingWithTrees.pdf 1.65MB BoostingTutorial.pdf 1.23MB rmarkdown-cheatsheet.pdf 1017KB 011dataSlicing.pdf 833KB 008crossValidation.pdf 501KB boosting(adaboost).pdf 199KB
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