- [[good tutorials]], [[learning log]] # Bayesian statistics and modeling - [Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science](https://github.com/lei-zhang/BayesCog_Wien) - by Lei Zhang; uses [[R language|R]], intro to [[Stan]], [[Markov chain Monte Carlo|MCMC]] - [An Introduction to Bayesian Data Analysis for Cognitive Science](https://vasishth.github.io/bayescogsci/book/) - by [[Shravan Vasishth]] # Causal inference and experimental design - [A Crash Course in Causality: Inferring Causal Effects from Observational Data | Coursera](https://www.coursera.org/learn/crash-course-in-causality) - by [[Jason Roy]] - [Causal Inference for The Brave and True](https://matheusfacure.github.io/python-causality-handbook/landing-page.html) - by [[Matheus Facure Alves]] - [Data science for economics (covers advanced topics like double machine learning and GRF)](https://madina-k.github.io/dse_mk2021/landing-page.html) - by [[Madina Kurmangaliyeva]] - [Stanford Stat 263/363: Experimental Design](https://statweb.stanford.edu/~owen/courses/363/) - by Art B. Owen, recommended by [[Dean Eckles]] - Dean's recommendations - Angrist & Pischke, Imbens & Rubin, Gerber & Green's Field Experiments - Morgan & Winship are the usual ones I suggest - Scott Cunningham's book is also good for going a bit deeper on applying some particular techniques. - [The Effect: An Introduction to Research Design and Causality | The Effect](https://theeffectbook.net/index.html) # Causal forests and trees - [Estimation of Heterogeneous Treatment Effects](https://gsbdbi.github.io/ml_tutorial/hte_tutorial/hte_tutorial.html#hte_2:_causal_forests_and_the_r-learner) # Computer science - [Coursera Mathematical Thinking in Computer Science](https://www.coursera.org/learn/what-is-a-proof) # Deep learning - [Introduction to Deep Learning — STAT 157, Spring 19 documentation](https://c.d2l.ai/berkeley-stat-157/index.html) - [Dive into Deep Learning — Dive into Deep Learning 0.17.2 documentation](https://d2l.ai/index.html) # Linear algebra - MIT18.06 linear algebra by [[Gilbert Strang]] - [Gilbert Strang lectures on Linear Algebra (MIT) - YouTube](https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D) - [Linear Algebra lemma course](https://www.lem.ma/books/AIApowDnjlDDQrp-uOZVow/landing) - [[Strang 2016 intro to linear algebra]] - [[Savov no bullshit guide to linear algebra]] - [[Boyd intro to applied linear algebra]] - [The-Art-of-Linear-Algebra/The-Art-of-Linear-Algebra.pdf at main · kenjihiranabe/The-Art-of-Linear-Algebra · GitHub](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra/blob/main/The-Art-of-Linear-Algebra.pdf) # Machine learning - [DataCamp: Linear Classifiers in Python](https://learn.datacamp.com/courses/linear-classifiers-in-python) - by Mike Gilbert # Mixed-effects/hierarchical/multi-level modeling - # Natural language processing - [ML book](https://book.mlcompendium.com/) - [Stanford CS124 From Languages to Information - youtube](https://www.youtube.com/channel/UC_48v322owNVtORXuMeRmpA/playlists) - [CS124 - From Languages to Information (Fall 2021)](https://web.stanford.edu/class/cs124/) - [[Jurafsky speech and language processing]] - [NLTK Book](https://www.nltk.org/book/) - [Info 256. Applied Natural Language Processing](https://people.ischool.berkeley.edu/~dbamman/info256.html) - [Codecademy resources](https://www.codecademy.com/paths/natural-language-processing/tracks/nlp-welcome-to-the-natural-language-processing-skill-path/modules/welcome-to-the-natural-language-processing-skill-path/informationals/nlp-intro-helpful-resources) - [Working With Text Data — scikit-learn 1.0.1 documentation](https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html) # Probability - Harvard Stat101 by [[Joseph Blitzstein]] - [Introduction to Probability | edX](https://www.edx.org/course/introduction-to-probability) - [Statistics 110: Probability - YouTube](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) - [[Blitzstein 2019 intro to probability]] - [Statistics 110: Probability](https://projects.iq.harvard.edu/stat110/home) - MIT6-012 Intro to Probability - [MIT RES.6-012 Introduction to Probability, Spring 2018 - YouTube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6) - [Introduction to Probability | Supplemental Resources | MIT OpenCourseWare](https://ocw.mit.edu/courses/res-6-012-introduction-to-probability-spring-2018/) - MIT18.05 Intro to probability and statistics - [Probability and statistics-MIT - YouTube](https://www.youtube.com/playlist?list=PLl8XY7QVSa4aUyZAtL2Hlf_mx3LaSix9B) - [Introduction to Probability and Statistics | Mathematics | MIT OpenCourseWare](https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/) - [[Casella 2022 statistical inference]] # Python - [DataCamp Python Object Oriented Programming](https://campus.datacamp.com/courses/object-oriented-programming-in-python/oop-fundamentals?ex=1) - [RealPython REST API](https://realpython.com/api-integration-in-python/?utm_source=notification_summary&utm_medium=email&utm_campaign=2021-07-28) - [RealPython collections](https://realpython.com/python-collections-module/?__s=f2xcdrkjqht99p741s74) ## pandas - [Real Python pandas](https://realpython.com/lessons/modifying-values-labels-indices/) # Computational social science - [Courses that use Bit by Bit](https://www.bitbybitbook.com/en/teaching/) - [CSC2552 Topics in Computational Social Science: AI, Data, and Society Syllabus](https://www.cs.toronto.edu/~ashton/csc2552/) - by Ashton Anderson # R - [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html) - by [[Hadley Wickham]], Danielle Navarro, Thomas Lin Pedersen - [R for Data Science](https://r4ds.had.co.nz/) - by [[Hadley Wickham]] # Web development - [Svelte Tutorial for Beginners by Net Ninja - YouTube](https://www.youtube.com/playlist?list=PL4cUxeGkcC9hlbrVO_2QFVqVPhlZmz7tO) # Others - [Generalized Additive Models in R · A Free Interactive Course](https://noamross.github.io/gams-in-r-course/) --- # Courses - [Coursera Computational Social Science Specialization](https://www.coursera.org/specializations/computational-social-science-ucdavis) - [Codecademy Fundamental Math for Data Science](https://www.codecademy.com/learn/paths/fundamental-math-for-data-science) - [Codecademy Exploratory Data Analysis Python](https://www.codecademy.com/learn/eda-exploratory-data-analysis-python) - [Machine Learning for Healthcare | Electrical Engineering and Computer Science | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/) - causal inference --- # Courses to organize - Mike's courses