Youtube.com is the second most accessed website in the world (surpassed only by its parent, google.com). It has a whopping 1 billion unique views a month. [1, 2] It is a force to be reckoned with. In the video sharing platform, there are many brilliant and hard-working content creators producing high-quality and free educational videos that students and academics alike can enjoy. I made a survey on Youtube content that could be useful for those interested in learning Statistics, and I listed and categorized them below.
Truth be told, this post is a glorified Google search in many respects. In any case, I had intended for a long time to gather this information as to facilitate the often laborious task of finding pertinent resources for learning statistical science in a non-static format (i.e., videos) that is easily accessible, high-quality, instructive and free.
Another motivation had to do with my teaching obligations. In this fall, I will teach a graduate course in Stats with R. To this end, I considered becoming a content creator myself, as to allow students to access the course's content from the convenience of their homes. In this process, I found some excellent statistical courses on Youtube. Some were really useful in terms of their organization, others in terms of content, interesting explanations, pedagogical skills, availability of materials, etc. Altogether, searching for resources was a very instructive experience, whose fruits should be shared.
Importantly, in this process, I learned that youtube is not short of 'introductory course on ___.' Not of Statistics, Probability or R, anyways. Which is a good thing. And often, you even see these three together. Also in abundance, are courses on the ABC's of probability theory, classical statistics (i.e., up to ANOVA, ANCOVA), and on basics of applied statistics (e.g., Econometrics, Biostatistics, and Machine Learning). Indeed, Machine Learning (mostly through Data Science) is really well represented on Youtube.
Due to the sheer amount of channels, I organized them into three broad categories: use of R as statistical software, use of other statistical software, and lecture format only. I also listed each channel's content/topic, whether authors provided slides, code, additional materials online (with links), and relevant remarks.
1. Learning Statistics with R
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Mike Marin | [Intro] Basic Stats in R | R | Yes, good materials | University British Columbia |
| Michael Butler | [Intro] to R and Stats, Modern | R | Yes | Good intro to R + Exercises |
| EZLearn | [Intro] Basic Stats in R | R | Exercises w/ solutions | - |
| Renegade Thinking: Courtney Brown | [Intro] Undergraduate Stats | R | Yes | Good Lectures |
| Barton Poulson | [Intro] Classical Stats, Programming & Solved Exercises | R, Python, SPSS | Yes | Gives intro to Python, R, SPSS and launching an OLP |
| Ed Boone | [Intro] Basic R and SAS | R & SAS | Yes | - |
| Bryan Craven | [Intro] Basic Stats in R | R | - | |
| Lynda.com | [Intro] Basics of R and Descriptives | R | Yes | OLP |
| Bryan Craven | [Intro] Basic Stats in R | R | No | - |
| Laura Suttle (Revolution) | [Intro] R tour for Beginners | R | No | - |
| Phil Chan | [Intro] Classical and Bio-stats | R, SPSS, Eviews | No | - |
| Gordon Anthony Davis | [Intro] R Programming Tutorial | R | No | Thorough intro for beginners |
| Nathaniel Phillips | [Intro] R Programming Tutorial | R | Yes | Videos as a pedagogical tool for his R book |
| David Langer | [Intro] Basics of R | R | No | Excellent pedagogical Skills |
| MrClean1796 | [Intro] Math, Physics and Statistics, lecture & R | R | No | - |
| Brian Caffo | Advanced & Bio-Stats, Reproducible Research | R | Yes, Coursera and GitHub | Professor of Bio-statistics, Johns Hopkins Univ. |
| Abbass Al Sharif | In-depth Machine Learning | R | Yes | Excellent lectures and resources |
| James Scott | Advanced Stats | R | Yes, and GitHub | Several Course Materials on GitHub |
| Derek Kane | Machine Learning | R | Yes | Excellent Videos, Fourier Analysis, Time series Forecasting |
| DataCamp | Programming, DataViz, R Markdown [free] | R | Yes, paid. 9$ for students | - |
| Maria Nattestad | DataViz | R | Personal Website | Plotting in R for Biologists |
| Christoph Scherber | Mixed, GLM, GLS, Contrasts | R | Yes | - |
| Librarian Womack | Time Series, DataViz, BigData | R | Yes, Course and .R | Materials online |
| Jarad Niemi | R Workflow, Bayesian, Statistical Inference | R | Yes | - |
| Justin Esarey | Bayesian, Categorical and Longitudinal Data, Machine Learning | R | Yes, lots and lots | Political Scientist |
| Jeromy Anglim | Research Methods | R | Blog:Psych & Stats, GitHub | + Rmeetups and Notes on Gelman, Carlin, Stern, and Rubin |
| Erin Buchanan | Under- & post-graduate Stats, SEM | R, G*Power, Excel | Yes | Excellent pedagogical strategies |
| Richard McElreath | From Basic to Advanced Bayesian Stats | R and Stan | Yes, lots | Book lectures |
| edureka | Data Science | R, Hadoop, Python | Yes, online learning plattaform | R Intro w/ Hadoop [free] |
| Learn R | R programming, stats on webiste | R, Python | Yes, and One R Tip A Day | On website, lots of starter's code |
| Data School | Machine Learning, Data Manipulation (dplyr) | Python, R | Yes, dplyr | Machine Learning with Hastie & Tibshirani |
| Econometrics Academy | Statistics (via Econometrics) | R, STATA, SPSS | Yes | OLP, Excellent Materials and Resources |
| Jalayer Academy | Basic Stats + Machine Learning | R, Excel | No | Also Lectures |
| Michael Levy | Authoring from R, Markdown, Shiny | R | No | - |
| Melvin L. | Machine Learning, R Programming, PCA, DataViz | R, Python, Gephi | No | Interesting Intro for Spark |
| OpenIntroOrg | Intro to Stats/R plus Inference, Linear Models, Bayesian | R | Yes, Coursera and OpenIntro | Coursera Courses, Resources in SAS |
| Mike Lawrence | Tidyverse, Wrangling & DataViz, plus Bayesian Inference | R, Stan, rstan | Yes, w/ Lit too | And relevant repos on GitHub |
2. Learning Statistics with other software
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Jonathan Tuke | Basic Stats | Matlab | No | - |
| Saiful Yusoff | PLS, Intro to MaxQDA | SmartPLS, MaxQDA | Yes | BYU |
| James Gaskin | SEM, PLS, Cluster | SPSS, AMOS, SmartPLS | Yes | BYU |
| Quantitative Specialists | Basic Stats | SPSS | No | Upbeat videos |
| RStatsInstitute | Basic Stats | SPSS | No | Instructor at Udemy |
| how2stats | Basic Stats, lecture and software demonstrations | SPSS | Yes | Complete Classical Stats |
| BrunelASK | Basic Stats | SPSS | - | |
| The Doctoral Journey | Basic Stats | SPSS | Yes | - |
| StatisticsLectures | Basic Stats, lecture format | SPSS | Yes | discontinued, but thorough basic stats |
| Andy Field | Classical Stats, lecture and software demonstrations | SPSS | Yes, registration needed | Used heavely in Social Sciences |
| Quinnipiac University:Biostatistics | Classical Stats | SPSS | No | - |
| The RMUoHP Biostatistics | Basic and Bio-Stats | SPSS, Excel | No | - |
| PUB708 Team | Classical Statistics | SPSS, MiniTab | No | - |
| Professor Ami Gates | Classical Stats | SPSS, Excel, StatCrunch | Yes | - |
| H. Michael Crowson | Intro and Basic Stats in several Softare | SPSS, STATA, AMOS, LISREAL | Yes? | - |
| Math Guy Zero | Classical Stats + SEM | SPSS, Excel, PLS | No | Lots of materials |
| BayesianNetworks | Bayesian Statistics, SEM, Causality | BayesianLab | Yes | - |
| Khan Academy | Programming 101 | Python | Yes | - |
| Mike's SAS | Short intro to SAS, SPSS | SAS, SPSS | No | - |
| Christian A. Wandeler | Basic Stats | PSPP | No | - |
3. Lectures on statistics
| Youtube channel | Content | Software | Online Materials? | Remarks |
|---|---|---|---|---|
| Stomp On Step 1 | [Intro] Bio-Stats, Basic | Lectures | Yes | USMLE |
| Khan Academy | [Intro] Basic Stats, lecture format | Lectures | Yes | - |
| Joseph Nystrom | [Intro] Basic Stats | Lectures | Yes | Active & unorthodox teaching |
| Statistics Learning Centre | [Intro] Basic Stats | Lectures | Yes | Register to access materials |
| Brandon Foltz | [Intro] Basic Stats | Lectures | soon | Excellent visuals |
| David Waldo | [Intro] Probability Theory | Lectures | No | - |
| Andrew Jahn | [Intro] Basic Stats | Lectures | No | FSL, AFNI and SPM [Neuro-immaging] |
| Professor Leonard | [Intro] Stats and Maths | Lectures | No | Excellent pedagogical skills |
| ProfessorSerna | [Intro] Basic Stats | Lectures | No | - |
| Harvard University | [Intro] Thorough Introduction to Probability Theory and Statistics | Lectures | No | In-depth |
| Victor Lavrenko | Machine Learning, Probabilistic, Cluster, PCA, Mixture Models | Lectures | Yes, very complete | Excellent Content, and lots of it |
| Jeremy Balka's Statistics | Graduate-level Classical Stats, Lecture | Lectures | Yes, very thorough | Excellent altogether, p-value vid great! |
| Methods Manchester Uni | Discussion on a wide variety of methods, SEM | Lectures | Yes | Methods Fair |
| Steve Grambow | Series on Inference | Lectures | Yes | Great Lectures on Inference [DUKE] |
| Statistics Corner: Terry Shaneyfelt | Statistical Inference | Lectures | Yes | from a clinical perspective |
| Michel van Biezen | Complete Course of Stats | Lectures | Yes, 1, 2, 3 | Thorough and complete, plus Physics and Maths |
| Oxford Education | Bayesian statistics: a comprehensive course | Lectures | Yes | - |
| Nando de Freitas | Machine Learning | Lectures | Yes, also here and here | - |
| Alex Smola | Machine Learning | Lectures | Yes, slides and code | - |
| Abu (Abulhair) Saparov | Machine Learning | Lectures | Yes | Taught by Tom Mitchell and Maria-Florina Balcan |
| Geoff Gordon | Machine Learning, Optimization | Lectures | Yes | - |
| MIT OpenCourseWare | Probability Theory, Stochastic Processes | Lectures | Yes, here, and here | - |
| Alexander Ihler | Machine Learning | Lectures | Yes, along w/ many others classes | - |
| Royal Statistical Society | Important Statistical issues | Lectures | Yes | Interesting topics |
| Ben Lambert | Graduate and Advanced Stats | Lectures | No | Asymptotic Behaviour of Estimators, SEM, EFA |
| DeepLearning TV | Machine (and Deep) Learning | Lectures | No | Excellent pedagogical skills |
| Mathematical Monk | Machine Learning, and Probability Theory | Lectures | No | - |
| Matthew McIntosh | Probability Theory, Generalized Linear Models, underlying mathematics | Lectures | No | Excellent mathematical derivations |
Final Remarks
These collection of channels listed here are not supposed to be exhaustive. If I have neglected a youtube channel that you think should figure in this list, please let me know via the contact form below and I shall include it. Thank you very much!
postscriptum [21/09/2016]
I am delighted with the reaction this post received on social media, which is mainly due to being published on R-bloggers (Thank you Tal Galili) As of today, it has received 450 shares and 500 likes [from both here and here].