本课程获评中央财经大学精品实验课程 (结项优秀)

We learn statistical computing from scratch with R/Python.

Time and Venue

Every Tuesday 10:00 — 12:35 Feb 14 — June 06, 2023, Shahe M218

Literature

• 康雁飞，李丰【著】（2020）统计计算，在线版本
• An Introduction to R
• R Style Guide
• Jones, Owen, Robert Maillardet, and Andrew Robinson. Introduction to scientific programming and simulation using R. CRC Press, 2014.
• Roger D. Peng, Exploratory Data Analysis with R, 2016.

Teaching Videos

• Pre-recorded teaching videos (in Chinese) are available at 哔哩哔哩(bilibili.com)

Slides

Part I: Introduction to R/Python

This part covers basic programming skills in both R and Python. Students are free to choose to use any of them.

R Programming (R slides and data in one zip file)

Jupyter Notebook	Slides
L01: Introduction	HTML
L02.1: R Builtin Data Structures	HTML
L02.2: R Functions and Packages	HTML
L02.3: R Data Import and Export	HTML
L03.1: R Visualization with Builtin Functions	HTML
L03.2: Advanced R Visualization	HTML

Python Programming (Python slides and data in one zip file)

Jupyter Notebook (鼠标右键点击另存为下载)	Slides and Teaching videos (CN)	MBA program	Statistics, Finance and accounting programs
L01.1: Introduction to Python for Economists and Statisticians	Slides	✓	✓
L01.2: Python from Scratch	Slides	✓	✓
L01.3: Python Functions and Modules	Slides	✓	✓
L02.1: Python Builtin Data Structures	Slides	✓	✓
L02.2: Data Wrangling with Pandas	Slides Video1 Video2 Video3	✓	✓
L02.3: Manipulating DataFrames with Pandas	Slides Video	✓	✓
L03.1: Pandas Data Visualization	Slides Video1 Video2	✓	✓
L03.2: Statistical Data Visualization	Slides Video1 Video2	✓	✓
L03.3: Interactive Data Visualization	Slides Video	✓	✓
L04.1: Reading and Cleaning Excel Files	Slides		✓
L04.2: Groups and pivot tables with Pandas	Slides		✓
L04.3: Strings and Custom Functions in Pandas	Slides		✓
L05.1: Fundamental Modules for Statistical Modelling	Slides Video1 Video2		✓
L05.2: Python for Statistical Modelling	Slides Video1 Video2 Video3 Video4		✓

Part II: Optimization and Simulation (R slides and data in one zip file)

Jupyter Notebook	Slides
L04.1: Distributions and Random Numbers	HTML
L04.2: Likelihood Function and Loss Function	HTML
L05: Sampling from Unkown Distributions	HTML
L06.1: Introduction to Optimization	HTML
L06.2: Newton’s Algorithm	HTML
L06.3: Hessian Free Algorithms	HTML
L07: Markov chain Monte Carlo	PDF
L08: Bayesian Inference with MCMC	PDF

Part III: Advanced Topics and Applications

Topic	Material
L09.1 Complex Model to Complex Data	PDF
L09.2 Large Scale Time Series Forecasting	PDF
L09.3 Highly-Scalable Distributed Modelling	PDF