Statistical Computing(统计计算)

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 Thursday 10:00 — 12:35 from March 03 to June 16, 2022, at Room 222, Shahe Building No. 3

Literature

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

TopicsSlides
Introduction
Data manipulation | Data Frames with “dplyr”
Using Packages and Coding Style in R
Loops and Functions | R Code
Vectorization and List Arithmetics
Distributions and Random Numbers | R Code
Basic R Graphics | Advanced R Graphics| R Code
Data Import and Export | R Code
Debugging Profiling and Packaging | R Code

Python Programming

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 StatisticiansSlides
L01.2: Python from ScratchSlides
L01.3: Python Functions and ModulesSlides
L02.1: Python Builtin Data StructuresSlides
L02.2: Data Wrangling with PandasSlides Video1 Video2 Video3
L02.3: Manipulating DataFrames with PandasSlides Video
L03.1: Pandas Data VisualizationSlides Video1 Video2
L03.2: Statistical Data VisualizationSlides Video1 Video2
L03.3: Interactive Data VisualizationSlides Video
L04.1: Reading and Cleaning Excel FilesSlides
L04.2: Groups and pivot tables with PandasSlides
L04.3: Strings and Custom Functions in PandasSlides
L05.1: Fundamental Modules for Statistical ModellingSlides Video1 Video2
L05.2: Python for Statistical ModellingSlides Video1 Video2 Video3 Video4

Part II: Optimization and Simulation

Optimization
R Code: Root Finding
Matrix Derivatives
R Code: Multidimensional Newton’s Method
R Code: Comparison
R Code: Nelder Mead
Simulation
Simulation: Exercises
R Code: Lectures
R Code: Exercises
Markov chain Monte Carlo
R Code (Markov Chains)
Bayesian Inference with MCMC
The importance of log scale
Bayesian Linear Models | R implementation
Bayesian Exercises | Solutions

Part III: Topics in Matrices and Data Mining

Computational Linear Algebra | R Code
Classification of Handwritten Digits
Handwritten Digits Data
Web Scraping with R | PDF

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