破解

懒得勤快

今天无意中在网上发现这个网站,提供了常用软件的破解版本,如 TeamViewer、PDF Expert、绘声绘影等,免去了高额的软件费。支持一下。

C/C++

C: 2019年度语言

有点意外,但似乎又是意料之中,毕竟现在依然对性能有极其强烈的开发需求。

安装 JetBrain Mono 字体

Data Science

Data Science resources

推荐了一些比较优秀的数据科学学习资源。

Introductory

James, Witten, Hastie & Tibshirani (2013) “An Introduction to Statistical Learning, with Applications in R” Springer.

Thomas (2018) “Mathematics for Machine Learning

Irizarry (2019) “Introduction to Data Science: Data Analysis and Prediction Algorithms with R”

Welling (2010) “A First Encounter with Machine Learning

Daumé III (2017) “A Course in Machine Learning

R Programming

Wickham & Grolemund (2017) “R for Data Science: Import, Tidy, Transform, Visualize, and Model Data” O’Reilly.

Wickham (2nd ed., 2019) “Advanced R” Chapman & Hall/CRC Press.

Wickham (2nd ed., 2015) “ggplot2: Elegant Graphics for Data Analysis

Lovelace, Nowosad & Muenchow (2019) “Geocomputation with R” CRC Press.

Python Programming

Downey (2nd ed., 2014) “ThinkStats: Exploratory Data Analysis in Python” O’Reilly.

Adhikari & DeNero “Computational and Inferential Thinking: The Foundations of Data Science”

Sklearn basics (Jupyter notebook)

Plotting and Visualization in Python (Jupyter notebook)

More Advanced

Hastie, Tibshirani & Friedman (2nd ed., 2009) “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”

Goodfellow, Bengio & Courville (2016) “Deep Learning” MIT Press.

McElreath (2015; 2nd ed. 2020) “Statistical Rethinking: A Bayesian Course with Examples in R and Stan” YouTube videos

Wikle, Zammit-Mangion & Cressie (2019) “Spatio-Temporal Statistics with R” Chapman & Hall/CRC Press.

Collins II (2003) “Fundamental Numerical Methods and Data Analysis

Leskovec, Rajaraman & Ullman (3rd ed., 2020) “Mining of Massive Datasets” CUP.

Hyndman & Athanasopoulos (2nd ed., 2018) “Forecasting: Principles and Practice” OTexts.

Blitzstein & Hwang (2nd ed., 2019) “Introduction to Probability” CRC Press.

Petersen & Pedersen (2012) “The Matrix Cookbook

Courses

fast.ai (Jeremy Howard & Rachel Thomas)

Deep Learning Specialization (Andrew Ng, Coursera)

Intro to Hadoop and MapReduce (Udacity)

Statistical Learning (Trevor Hastie & Rob Tibshirani, Stanford Online)

Linear Algebra (Gilbert Strang, MIT OCW)

Free Datascience books

I’ve been impressed in recent months by the number and quality of free datascience/machine learning books available online. I don’t mean free as in some guy paid for a PDF version of an O’Reilly book and then posted it online for others to use/steal, but I mean genuine published books with a free online version sanctioned by the publisher. That is, “the publisher has graciously agreed to allow a full, free version of my book to be available on this site.” Here are a few in my collection:

While we are on the subject, I would be remiss of me not to recommend D.J. Patil’s free minibooks/essays. While they are not the thick comprehensive tomes of those above, they are definitely worth the time to read.

Finally, this is work in progress (just 3 chapters to date) but is one to watch: Network Science by A.-L. Barabasi.

Update [12/27/12]: adding in some additions from a hacker news discussion and the comments below (thanks guys):

List of Data Science/Big Data Resources

Index

Data Science Introduction

Data Processing

Data Analysis

Fundamentals

Network Analysis

Statistics

Data Mining

Machine Learning

Data Science Application

Information Retrieval

Data Visualization

Uncategorized

MOOCs about Data Science