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讲义
R 代码、Python Notebooks
实验室材料
高级部分
数据采集——数据整理、清洗、采样，得到合适的数据集；
数据管理——快速可靠地访问数据；
探索性数据分析——产生假设和建立直觉；
预测或统计学习；
交流——通过可视化、故事和可解释的总结来总结结果。
课程讲义
Lecture 1: Introduction (Sep. 03, 2019)
Lecture 2: Data and Data Exploration (Sep. 04, 2019)
Lecture 3: Pandas and Web Scraping (Sep. 11, 2019)
Lecture 4: Introduction to Regression (Sep. 16, 2019)
Lecture 5: Linear Regression (Sep. 18, 2019)
Lecture 6: Multiple Linear Regression, Polynomial Regression (Sep. 23, 2019)
Lecture 7: Model Selection and Regularization (Sep. 25, 2019)
Lecture 8: Regularization and EDA (Sep. 30, 2019)
Lecture 9: Visualization for Communication (Oct. 02, 2019)
Lecture 10: Logistic Regression (Oct. 07, 2019)
Lecture 11: Logistic Regression 2 (Oct. 09, 2019)
Lecture 12: KNN Classification & Imputation (Oct. 16, 2019)
Lecture 14: PCA (Oct. 23, 2019)
Lecture 15: Decision Trees (Oct. 28, 2019)
Lecture 16: Bagging, & Random Forest (Oct. 30, 2019)
Lecture 17: Boosting Methods (Nov. 04, 2019)
Lecture 18: Neural Networks 1 – Perceptron and MLP (Nov. 06, 2019)
Lecture 19: NN 2: Anatomy of NN, design choices (Nov. 11, 2019)
Lecture 20: NN 3: Back Propagation (Nov. 13, 2019)
Lecture 21: NN 4: Regularization methods (Nov. 18, 2019)
Lecture 22: Visualization for Model Interpretation (Nov. 20, 2019)
Lecture 23: Experimental Design & Testing I (Nov. 25, 2019)
Lecture 24: Experimental Design & Testing II (Dec. 02, 2019)
主题和R、Python代码实操
Activation Function
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
AdaBoost
Lecture 17: Boosting Methods
Lecture 17: Boosting Methods [Notebook]
Adaboost And Xgboost
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
Array Reshape
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Bagging
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 16: Bagging, & Random Forest
Batching
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
Bayesian
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Beautiful Soup
Lab 2: Pandas and Scraping
Beautifulsoup
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Bias
Lecture 8: Regularization and EDA
Biases
Lecture 8: Regularization and EDA
Big Data
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Boosting
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
Lecture 17: Boosting Methods
Lecture 17: Boosting Methods [Notebook]
Bootstrap
Lecture 5: Linear Regression
Boundaries
Lecture 10: Logistic Regression [Notebook]
Categorical Predictors
Lecture 6: Multiple Linear Regression, Polynomial Regression
Categorical Variables
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
CI
Lecture 5: Linear Regression
Classification
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Lecture 12: KNN Classification & Imputation
Lecture 10: Logistic Regression [Notebook]
Collinearity
Lecture 6: Multiple Linear Regression, Polynomial Regression
Communication
Lecture 9: Visualization for Communication
Confidence Intervals
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lecture 5: Linear Regression
Confusion Matrix
Lecture 11: Logistic Regression 2 [Notebook]
Crawl
Lab 2: Pandas and Scraping
Cross-Validation
Lecture 11: Logistic Regression 2 [Notebook]
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Lecture 7: Model Selection and Regularization
CV
Lecture 11: Logistic Regression 2 [Notebook]
Data
Lecture 2: Data and Data Exploration
Data Cleaning
Lecture 3: Code Pandas + Beautiful Soup [Notebook]
Data Exploration
Lecture 2: Data and Data Exploration
Data Science Demo
Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]
Lecture 1: Data Science Demo [Notebook]
Data Science Process
Lecture 1: Introduction
Data Scraping
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Dataframe
Lecture 3: Pandas and Web Scraping
Decision Boundaries
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Decision Trees
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
Demo
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Descriptive Statistics
Lecture 2: Data and Data Exploration
Dictionaries
Lab 1: Python basics, YAML environments, Numpy
Dimensionality Reduction
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Lab 8: PCA
Lab 8: PCA [Notebook]
Advanced Sections 4: PCA
Lecture 14: PCA
Lecture 14: PCA [Notebook]
Dropout
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Eda
Lecture 9: Visualization for Communication
Lecture 8: Regularization and EDA
Lecture 3: Pandas and Web Scraping
Eigenvalues
Advanced Sections 4: PCA
Eigenvectors
Advanced Sections 4: PCA
Advanced Section 1: Linear Algebra and Hypothesis Testing
Eignevalues
Advanced Section 1: Linear Algebra and Hypothesis Testing
Elastic Net
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Entropy
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
Explained Variance
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Exploratory Data Analysis
Lecture 3: Pandas and Web Scraping
Feed Forward
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
Feed Forward Neural Networks
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Functions
Lab 1: Python basics, YAML environments, Numpy
Gini Index
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
GLM
Advanced Section 3: Generalized Linear Models
Advanced Section 3: Generalized Linear Models [Notebook]
Google Sites
Lab 13: Making websites! [Notebook]
Gradient Descent
Lecture 17: Boosting Methods
Lecture 17: Boosting Methods [Notebook]
Html
Lab 13: Making websites! [Notebook]
Http
Lab 13: Making websites! [Notebook]
Hypothesis Testing
Lecture 6: Multiple Linear Regression, Polynomial Regression
Advanced Section 1: Linear Algebra and Hypothesis Testing
Lecture 5: Linear Regression
Imputation
Lecture 12: KNN Classification & Imputation
Information Gain
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Interaction Terms
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lecture 6: Multiple Linear Regression, Polynomial Regression
Introduction
Lecture 1: Introduction
K-Nearest Neighbors (KNN) Regression
Lab 3: Scikit-learn for Regression [Notebook]
Keras
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
KNN
Lecture 12: KNN Classification & Imputation
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
KNN-Classification
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
KNN Imputation Classification
Lecture 12: KNN Classification & Imputation [Notebook]
Knn K-Nearest Neighbors (KNN)
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
KNN Regression
Lecture 4: Introduction to Regression
Lasso
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Lecture 8: Regularization and EDA
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Linear Algebra
Advanced Section 1: Linear Algebra and Hypothesis Testing
Linear Regression
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lecture 5: Linear Regression
Lab 3: Scikit-learn for Regression [Notebook]
Lists
Lab 1: Python basics, YAML environments, Numpy
Logistic Regression
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
Lecture 11: Logistic Regression 2 [Notebook]
Lecture 10: Logistic Regression [Notebook]
Logistics
Lecture 1: Introduction
Matplotlib
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Metrics
Lecture 11: Logistic Regression 2 [Notebook]
Mle
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
MNIST
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Model Selection
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Lecture 7: Model Selection and Regularization
Multiclass
Lecture 11: Logistic Regression 2 [Notebook]
Multilayer Perceptron
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Multinomial Regression
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Multiple Linear Regression
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lecture 6: Multiple Linear Regression, Polynomial Regression
Multiple Logistic Regression
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Neural Networks
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
NumPy
Lab 1: Python basics, YAML environments, Numpy
Lab 01: YAML Environments, Python basics, Numpy [Notebook]
OOB
Lecture 16: Bagging, & Random Forest
Out Of Bag Error
Lecture 16: Bagging, & Random Forest
Overfitting
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lecture 6: Multiple Linear Regression, Polynomial Regression
P-Values
Lecture 5: Linear Regression
Pairplot
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Pandas
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Lab 02: More Pandas [Notebook]
Lab 02: Scraping [Notebook]
Lecture 3: Code Pandas + Beautiful Soup [Notebook]
Lecture 3: Pandas and Web Scraping
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Pca
Lab 8: PCA
Lab 8: PCA [Notebook]
Advanced Sections 4: PCA
Lecture 14: PCA
Lecture 14: PCA [Notebook]
Pipeline
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Plots
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Polynomial Regression
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Lecture 6: Multiple Linear Regression, Polynomial Regression
Predictors
Lecture 4: Introduction to Regression
Principal Components Analysis
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Principle Component Analysis
Lab 8: PCA
Lab 8: PCA [Notebook]
Probabilities
Lecture 10: Logistic Regression [Notebook]
Python
Lab 01: YAML Environments, Python basics, Numpy [Notebook]
Qualitative Predictors
Lecture 6: Multiple Linear Regression, Polynomial Regression
R-Square
Lecture 4: Introduction to Regression
R^2
Lecture 4: Introduction to Regression
Random Forest
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lecture 16: Bagging, & Random Forest
Regression
Lecture 6: Multiple Linear Regression, Polynomial Regression
Regression Trees
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Regularization
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lecture 11: Logistic Regression 2 [Notebook]
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Requests
Lab 2: Pandas and Scraping
Response Variable
Lecture 4: Introduction to Regression
RF
Lecture 16: Bagging, & Random Forest
Ridge
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Ridge Regression
Lecture 8: Regularization and EDA
Roc
Lecture 11: Logistic Regression 2 [Notebook]
Scikit-Learn
Lab 3: Scikit-learn for Regression [Notebook]
Scraping
Lab 2: Pandas and Scraping
Lecture 3: Pandas and Web Scraping
Seaborn
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Simple Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Sklearn
Lecture 11: Logistic Regression 2 [Notebook]
Lecture 10: Logistic Regression [Notebook]
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Statistical Model
Lecture 4: Introduction to Regression
Statsmodels
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Stochastic Gradient Descent
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
T-Test.
Lecture 5: Linear Regression
Tensorflow
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
The Data Science Process
Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]
Lecture 1: Data Science Demo [Notebook]
Train-Test
Lecture 4: Introduction to Regression
Training
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
Training And Testing Data Splitting
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Trees
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Variable Importance
Lecture 16: Bagging, & Random Forest
Variance Vs Bias
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
Visualization
Lecture 9: Visualization for Communication
Web Pages
Lab 13: Making websites! [Notebook]
Web Scraping
Lab 2: Pandas and Scraping
Lecture 3: Pandas and Web Scraping
Website Scraping
Lab 2: Pandas and Scraping
Websites
Lab 13: Making websites! [Notebook]
Weights
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Wix
Lab 13: Making websites! [Notebook]
Www
Lab 13: Making websites! [Notebook]
YAML
Lab 1: Python basics, YAML environments, Numpy
Lab 01: YAML Environments, Python basics, Numpy [Notebook]
关于机器学习，参看1.机器学习之KNN分类算法介绍: Stata和R同步实现（附数据和代码），2.机器学习对经济学研究的影响研究进展综述，3.回顾与展望经济学研究中的机器学习，4.最新: 运用机器学习和合成控制法研究武汉封城对空气污染和健康的影响! 5.Top, 机器学习是一种应用的计量经济学方法, 不懂将来面临淘汰危险！6.Top前沿: 农业和应用经济学中的机器学习, 其与计量经济学的比较, 不读不懂你就out了！7.前沿: 机器学习在金融和能源经济领域的应用分类总结，8.机器学习方法出现在AER, JPE, QJE等顶刊上了！9.机器学习第一书, 数据挖掘, 推理和预测，10.从线性回归到机器学习, 一张图帮你文献综述，11.11种与机器学习相关的多元变量分析方法汇总，12.机器学习和大数据计量经济学, 你必须阅读一下这篇，13.机器学习与Econometrics的书籍推荐, 值得拥有的经典，14.机器学习在微观计量的应用最新趋势: 大数据和因果推断，15.R语言函数最全总结, 机器学习从这里出发，16.机器学习在微观计量的应用最新趋势: 回归模型，17.机器学习对计量经济学的影响, AEA年会独家报道，18.回归、分类与聚类：三大方向剖解机器学习算法的优缺点（附Python和R实现），19.关于机器学习的领悟与反思，20.机器学习，可异于数理统计，21.前沿: 比特币, 多少罪恶假汝之手? 机器学习测算加密货币资助的非法活动金额! 22.利用机器学习进行实证资产定价, 金融投资的前沿科学技术! 23.全面比较和概述运用机器学习模型进行时间序列预测的方法优劣！24.用合成控制法, 机器学习和面板数据模型开展政策评估的论文！25.更精确的因果效应识别: 基于机器学习的视角，26.一本最新因果推断书籍, 包括了机器学习因果推断方法, 学习主流和前沿方法，27.如何用机器学习在中国股市赚钱呢? 顶刊文章告诉你方法！28.机器学习和经济学, 技术革命正在改变经济社会和学术研究，29.世界计量经济学院士新作“大数据和机器学习对计量建模与统计推断的挑战与机遇”，30.机器学习已经与政策评估方法, 例如事件研究法结合起来识别政策因果效应了！31.重磅! 汉森教授又修订了风靡世界的“计量经济学”教材, 为博士生们增加了DID, RDD, 机器学习等全新内容！32.几张有趣的图片, 各种类型的经济学, 机器学习, 科学论文像什么样子？33.机器学习已经用于微观数据调查和构建指标了, 比较前沿！34.两诺奖得主谈计量经济学发展进化, 机器学习的影响, 如何合作推动新想法！35.前沿, 双重机器学习方法DML用于因果推断, 实现它的code是什么？
2.5年，计量经济圈近1000篇不重类计量文章，
可直接在公众号菜单栏搜索任何计量相关问题,
Econometrics Circle
数据系列：空间矩阵 | 工企数据 | PM2.5 | 市场化指数 | CO2数据 | 夜间灯光 | 官员方言 | 微观数据 | 内部数据计量系列：匹配方法 | 内生性 | 工具变量 | DID | 面板数据 | 常用TOOL | 中介调节 | 时间序列 | RDD断点 | 合成控制 | 200篇合辑 | 因果识别 | 社会网络 | 空间DID数据处理：Stata | R | Python | 缺失值 | CHIP/ CHNS/CHARLS/CFPS/CGSS等 |干货系列：能源环境 | 效率研究 | 空间计量 | 国际经贸 | 计量软件 | 商科研究 | 机器学习 | SSCI | CSSCI | SSCI查询 | 名家经验计量经济圈组织了一个计量社群，有如下特征：热情互助最多、前沿趋势最多、社科资料最多、社科数据最多、科研牛人最多、海外名校最多。因此，建议积极进取和有强烈研习激情的中青年学者到社群交流探讨，始终坚信优秀是通过感染优秀而互相成就彼此的。