# 数据科学导论
数据科学那么火，资源那么多，但找遍中文互联网都找不到系统、稍微过得去的笔记，于是就想着自己做一份。我认知中的优秀笔记是 [CS231n 中文笔记](https://zhuanlan.zhihu.com/p/21930884?refer=intelligentunit)，希望这系列笔记能达到它的质量。欢迎加星、合作、提问题、增改内容。
关于数据科学学科介绍的文章、鸡血颇多，因此不再赘述，简而言之，数据科学是计算机科学、统计学、相关领域的结合，以数据、统计思维和计算思维解决问题。而我相信体验数据科学的最佳方式，就是亲手实践。
笔记基于两门数据科学导论课，分别是加州大学伯克利校区 [DS100](http://www.ds100.org/sp17/syllabus) 与哈佛大学 [CS109](http://cs109.github.io/2015/pages/videos.html)，质量颇高。特别感谢制作这两门课程的 Joe Blitzstein、Hanspeter Pfister、Verena Kaynig-Fittkau、Joseph E. Gonzalez、Joseph Hellerstein、Deborah Nolan 和 Bin Yu。
笔记使用 Python3 和 Jupyter Notebook，于[知乎专栏](https://zhuanlan.zhihu.com/introdatascience)亦有更新。
笔记假定读者是以中文为母语、对数据科学感兴趣的入门者，故以中文为主、英语为注释进行创作，原课件为英文而作者亦无中文相关背景，故必然存在翻译纰漏，还请海涵并指出。
Given the assumption that readers are Chinese speakers and interested in Data Science, this note will be written primarily in Chinese and using English as captions.
## 知识储备
默认读者具有以下知识：
* 概率统计基础：理解方差、期望值（均值）、联合概率、条件概率等基本概念
* Python 基础：理解函数、循环、变量、列表生成式、类等基本概念，知道 pip、Jupyter Notebook、NumPy、Pandas 等工具
* 英语技能： 四级或 IELTS 6 分以上水平，能浏览英语文档及文献
* 知识技能：科学上网并使用 Google 查找解决方案
若不具备以上知识，推荐以下资源：
* [data8](http://data8.org)（Python 与统计入门）
* [Learn Python The Hard Way](https://learnpythonthehardway.org/book/)（Python 入门）
* [CS61A](http://cs61a.org)（Python 进阶）
* [Introduction to Computer Science and Programming Using Python](https://courses.edx.org/courses/course-v1:MITx+6.00.1x+2T2017/course/)（Python 进阶）
## 哲学
使用工具，并明了背后的思想；
内容自洽（self-contained），并提供深入途径；
能可视化的就不要用文字；
开源，协作。
## 笔记目录
- [x] [数据收集与整理 Data Collecting, Unboxing and Wrangling](https://nbviewer.jupyter.org/github/iewaij/introDataScience/blob/master/01.%20Data%20Collecting%2C%20Unboxing%20and%20Wrangling.ipynb)
- [x] [探索性数据分析 Exploratory Data Analysis](https://github.com/iewaij/introDataScience/blob/master/02.%20Exploratory%20Data%20Analysis.md)
- [x] [统计模型 Statistical Models](https://github.com/iewaij/introDataScience/blob/master/04.%20Statistical%20Models.md)
- [ ] 贝叶斯理论和贝叶斯方法 Bayes Theorem and Bayesian Methods
- [x] [偏差与方差 Bias and Variance](https://github.com/iewaij/introDataScience/blob/master/05.%20Bias%20and%20Variance.md)
- [ ] 线性回归 Linear Regression
- [ ] 逻辑回归 Logistic Regression
- [ ] k 最近邻 k-Nearest Neighbor
- [ ] 支持向量机 SVM
- [ ] 决策树 Decision Trees
- [ ] 随机森林 Random Forests
- [ ] 集成方法 Ensemble Methods
- [ ] 聚类 Clustering
- [ ] 深度神经网络 Deep Networks
- [ ] 最佳实践 Best Practices
- [ ] 推荐系统和并行计算 Recommendations and MapReduce
- [ ] 大数据处理 Spark
- [ ] 实验设计 Experimental Design
- [x] [用数据讲故事 Story Telling](https://github.com/iewaij/introDataScience/blob/master/03.%20Story%20Telling.md)
- [ ] 高效演讲 Effective Presentations
## 作业目录
### DS100
- [x] [总统候选人推特分析：助手还是本人？ Language in the 2016 Presidential Election](https://github.com/iewaij/introDataScience/blob/master/material/homework/DS%20100/hw2/hw2.ipynb)
- [ ] [数据清洗与探索性数据分析 Data Wrangling and Exploratory Data Analysis](https://github.com/iewaij/introDataScience/blob/master/material/homework/DS%20100/hw3/hw3.ipynb)
- [ ] [爬虫 Crawling the Web](https://github.com/iewaij/introDataScience/blob/master/material/homework/DS%20100/hw5/hw5.ipynb)
- [ ] [预测房价 Prediction on Housing Prices](https://github.com/iewaij/introDataScience/blob/master/material/homework/DS%20100/hw6/hw6.ipynb)
### CS100
- [ ] [探索性数据分析 Exploratory Data Analysis](https://github.com/iewaij/introDataScience/blob/master/material/homework/CS%20109/HW1.ipynb)
- [ ] [进一步探索性数据分析 More Exploratory Data Analysis](https://github.com/iewaij/introDataScience/blob/master/material/homework/CS%20109/HW2.ipynb)
- [ ] [预测与分类 Prediction and Classification](https://github.com/iewaij/introDataScience/blob/master/material/homework/CS%20109/HW3.ipynb)
## 实验目录
- [ ] [概率、分布与频率统计 Probability, Distributions, and Frequentist Statistics](https://github.com/iewaij/introDataScience/tree/master/material/lab/Probability%2C%20Distributions%2C%20and%20Frequentist%20Statistics)
- [ ] [用 sklearn 和 statsmodels 实现回归与逻辑回归 Regression and Logistic Regression in sklearn and statsmodels](https://github.com/iewaij/introDataScience/tree/master/material/lab/Regression%20and%20Logistic%20Regression%20in%20sklearn%20and%20statsmodels)
## Change Log
2017-09-01 完成推特分析作业
2017-09-01 完成统计模型
2017-08-17 完成用数据讲故事
2017-08-11 完成探索性数据分析
2017-08-10 完成数据收集与整理
2017-08-09 初始化
数理统计、机器学习和数据编程的学习笔记.zip （333个子文件） 
violations.csv 65.06MB inspections.csv 5.96MB violations.csv 5.77MB violations.csv 3.71MB violations.csv 3.25MB businesses.csv 3.19MB inspections.csv 2.94MB ratings.csv 2.33MB businesses.csv 1.44MB businesses.csv 1.29MB inspections.csv 1.12MB ames_train.csv 665KB businesses.csv 645KB inspections.csv 493KB movies.csv 448KB ames_test.csv 293KB links.csv 179KB tags.csv 41KB kaggle_example.csv 28KB g12.csv 2KB predictwise.csv 1KB feed_info.csv 230B feed_info.csv 228B legend.csv 120B legend.csv 67B legend.csv 64B hw7.dbc 25KB .gitignore 1KB .gitignore 1KB
D01.gz 3.02MB D11.gz 3.01MB D02.gz 2.73MB D12.gz 2.48MB
GaussMarkov.html 269KB GaussMarkov-checkpoint.html 269KB OLS.html 269KB SF_Food_Safety-checkpoint.ipynb 1.47MB SF_Food_Safety.ipynb 1.47MB SF_Food_Safety-checkpoint.ipynb 1.47MB lec7.ipynb 1004KB Lab3-Freq.ipynb 526KB Lab3-Stats.ipynb 386KB PandasAndSQL.ipynb 378KB Lab3-probability.ipynb 267KB hw2-checkpoint.ipynb 176KB hw2.ipynb 176KB hw2_solution.ipynb 103KB 02-DataScraping-checkpoint.ipynb 86KB 02-DataScraping.ipynb 85KB hw2_solution-checkpoint.ipynb 75KB hw5_solution.ipynb 74KB hw5.ipynb 70KB 01. Data Collecting, Unboxing and Wrangling-checkpoint.ipynb 60KB 01. Data Collecting, Unboxing and Wrangling-checkpoint.ipynb 60KB 01. Data Collecting, Unboxing and Wrangling.ipynb 59KB 15b-Spark.ipynb 59KB Data Collecting, Unboxing and Wrangling-checkpoint.ipynb 57KB 02-DataScraping-checkpoint.ipynb 55KB hw2-checkpoint.ipynb 54KB 02-DataScrapingQuizzes.ipynb 53KB Lab4-stats_original.ipynb 51KB Lab4-stats.ipynb 51KB hw4_solution.ipynb 48KB hw1_solution.ipynb 48KB hw4.ipynb 45KB hw1.ipynb 44KB hw1-checkpoint.ipynb 43KB hw3_solution.ipynb 32KB hw6_solution.ipynb 31KB hw6.ipynb 29KB hw3-iewaij-checkpoint.ipynb 29KB hw3-iewaij.ipynb 29KB hw3.ipynb 28KB GaussMarkov.ipynb 25KB GaussMarkov-checkpoint.ipynb 25KB OLS.ipynb 22KB OLS-checkpoint.ipynb 22KB 数据收集与整理-checkpoint.ipynb 18KB preface.ipynb 7KB preface-checkpoint.ipynb 7KB EDA.ipynb 6KB EDA-checkpoint.ipynb 6KB hw7.ipynb 5KB foodSafetyLA.ipynb 2KB foodSafetyLA-checkpoint.ipynb 2KB EDA.ipynb 2KB EDA-checkpoint.ipynb 2KB notesOnStatistics.ipynb 1KB notesOnStatistics-checkpoint.ipynb 1KB RelationalData.ipynb 1KB WhyOLS.ipynb 1KB WhyOLS-checkpoint.ipynb 1KB MatrixNotation.ipynb 1KB MatrixNotation-checkpoint.ipynb 1KB StatisticsNotation-checkpoint.ipynb 658B StatisticsNotation.ipynb 658B notesOnMachineLearning.ipynb 616B notesOnDataMan.ipynb 616B notesOnMachineLearning-checkpoint.ipynb 616B notesOnDataMan-checkpoint.ipynb 616B共 333 条
- 1
- 2
- 3
- 4
资源评论
武昌库里写JAVA
- 粉丝: 6682
- 资源: 3166
最新资源
- YOLO-yolo资源
- 适用于 Java 项目的 Squash 客户端库 .zip
- 适用于 Java 的 Chef 食谱.zip
- Simulink仿真快速入门与实践基础教程
- js-leetcode题解之179-largest-number.js
- js-leetcode题解之174-dungeon-game.js
- Matlab工具箱使用与实践基础教程
- js-leetcode题解之173-binary-search-tree-iterator.js
- js-leetcode题解之172-factorial-trailing-zeroes.js
- js-leetcode题解之171-excel-sheet-column-number.js
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
