Data Science in R —— fundamental and industry applications
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.
- 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.
- Working with R： basic grammar you can find in otherplaces, trying to be different.
- Getting and Cleaning Data： read local data, using APIs , db, or odbc/jdbc and etc jobs.
- Exploratory Data Analysis： descriptive statistics, visualization, espcially in multivariate sense.
- 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.
- Regression Models and Classifications： main stream of machine learning
- Unsupervised Machine Learning: clustering and LOF
- Web Crawler and NLP: some applications
- Time Series Analysis: practical way, arima , machine learning and other way
- always changing my mind…
chong.ding83 at gmail.com
https://www.linkedin.com/in/chong-ding-38b59837/
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