CGSS

• **家庭追踪调查 http://www.isss.pku.edu.cn/cfps/sjzx/gksj/index.htm

  • [CFPS Public Data] CFPS 2008（预调查）
  • [CFPS Public Data] CFPS 2009（预调查）
  • [CFPS Public Data] CFPS 2010
  • [CFPS Public Data] CFPS 2011
  • [CFPS Public Data] CFPS 2012
  • [CFPS Public Data] CFPS 2014
  • [CFPS Public Data] CFPS 2016
  • [CFPS Public Data] CFPS 2018

• CGSS项目组发布CGSS2017年的家户调查数据 https://mp.weixin.qq.com/s/3i5TDzEmbACfGL9shK-TWw

  • http://cnsda.ruc.edu.cn/index.php
  • **综合社会调查（2017）
  • **综合社会调查（2015）
  • **综合社会调查（2012)

• **家庭收入调查（CHIP）http://www.ciidbnu.org/chip/index.asp

• **健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS） http://charls.pku.edu.cn/

旨在收集一套代表**45岁及以上中老年人家庭和个人的高质量微观数据，用以分析我国人口老龄化问题，推动老龄化问题的跨学科研究。CHARLS全国基线调查于2011年开展，覆盖150个县级单位，450个村级单位，约1万户家庭中的1.7万人。这些样本以后每两到三年追踪一次，调查结束一年后，数据将对学术界展开。

• 2017年网民社会意识调查

• **教育追踪调查（2014-2015学年）

  • 分析学生问卷中“打架”
  • http://cnsda.ruc.edu.cn/index.php?r=projects/view&id=61662993

• **政府采购网

  • http://htgs.ccgp.gov.cn/GS8/contractpublish/search

• American Time Use Survey

  • http://www.bls.gov/tus/data.htm

• Opportunity Insights Economic Tracker Data Downloads
  The Opportunity Insights Economic Tracker (https://tracktherecovery.org) combines anonymized data from leading private companies – from credit card processors to payroll firms – to provide a real-time picture of indicators such as employment rates, consumer spending, and job postings across counties, industries, and income groups. https://github.com/OpportunityInsights/EconomicTracker

• requests
• beautifulsoup
• pyquery
  • 五步写爬虫 | 采集大众点评数据采集实战 https://mp.weixin.qq.com/s/U5O4KI9knAjt5t8L-u27iw
• selenium
• pyppeteer https://github.com/pyppeteer/pyppeteer
  • 爬虫神器 Pyppeteer 介绍及爬取某商城实战 https://mp.weixin.qq.com/s/pIU7PTAhN9oQJQGzr85dfA

select an element in the page to inspect it.

Kaggle数据竞赛

• Daily News for Stock Market Prediction: Using 8 years daily news headlines to predict stock market movement
  • https://www.kaggle.com/aaron7sun/stocknews
  • https://www.kaggle.com/shreyams/stock-price-prediction-94-xgboost

• dowhy https://github.com/microsoft/dowhy
  • https://microsoft.github.io/dowhy/example_notebooks/dowhy_estimation_methods.html

• causalinference https://github.com/laurencium/causalinference
  • Assessment of overlap in covariate distributions
  • Estimation of propensity score
  • Improvement of covariate balance through trimming
  • Subclassification on propensity score
  • Estimation of treatment effects via matching, blocking, weighting, and least squares