Video Statistics and Probability Courses

Videos of Probability and Statistics Courses

-compiled by Dr. M. Hlynka, University of Windsor
Last update: July, 2020 .

SECTIONS

INTRODUCTORY STATISTICS

INTRO MATHEMATICAL STATISTICS

MATHEMATICAL STATISTICS

QUEUEING

STOCHASTIC MODELS

DATA MINING AND MACHINE LEARNING

REGRESSION

MULTIVARIATE STATISTICS

TIME SERIES

BROWNIAN MOTION

BAYESIAN METHODS

BIOSTATISTICS

FINANCIAL MATH

LEARNING R

OTHER TOPICS

INTRODUCTORY STATISTICS

Against All Odds. 32 videos. Professionally produced.
Level: Very elementary.
https://learner.org/series/against-all-odds-inside-statistics/

Jeremy Balka's Statistics website. Basically a course in elementary statistics. Recommended.
Level: Elementary.
http://www.jbstatistics.com/

Statistics lectures.com 86 very short videos.
Level: elementary, clearly presented, always with an example, and a very quick intro to statistics
http://www.statisticslectures.com/topics/statistics/

Collaborative Statistics, by Illowsky and Dean. 12 chapters in 6 hours. Comes with a free online (or purchasable bound copy) text.
Level: elementary.
https://archive.org/details/CollaborativeStatistics_Lecture_Videos

Sets, Counting, and Probability. Paul G. Bamberg. Harvard. Math E102. 2005.
Level: Introductory
http://www.extension.harvard.edu/open-learning-initiative/sets-counting-probability

STAT 401A - Statistical Methods for Research, Iowa State University, by Jarad Niemi, 17 videos. 4 hours. Well done.
Level: Introductory applied.
http://www.youtube.com/playlist?list=PLFHD4aOUZFp10lCLRcEA0rjiitZxzQoCB

Khan Academy. Probability and Statistics.
Level: elementary
https://www.khanacademy.org/math/probability

Intro Statistics. Philip Stark. UC Berkeley.
Level: Introductory.
http://www.stat.berkeley.edu/~stark/SticiGui/index.htm

Statistics. Professor Leonard. Elementary. OK.
https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0

Excel Techniques - Data Analysis. Fordham Univ. 14 videos of about 6 minutes each.
Level: Elementary.
https://www.youtube.com/playlist?list=PLAA23D133BCDB1178

Fundamentals of Statistics and Computation for Neuroscientists. Mainly elementary, but some more advanced topics.
https://www.youtube.com/channel/UCH4dGK2uaTQcErmsyoJY9Dg/playlists

INTRO MATHEMATICAL STATISTICS

Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik. Includes a text and some videos.
Level: intro math stat.
http://www.probabilitycourse.com

Math 131A: Introduction to Probability and Statistics. UCIrvine. 16 videos, 24 hours. Michael Cranston.
Level: Intro math stat
http://www.youtube.com/playlist?list=PLqOZ6FD_RQ7n6XnvxxsWfxFtYf0Xj479J

Probability for Life Science. UCLA. (The late) Herbert Enderton. 2010.
Level: Intro.
http://freevideolectures.com/Course/105/Math-3C-Probability-for-Math-Science

Opinionated Lessons in Statistics, by Bill Press, University of Texas, 49 lectures of 30 minutes each. Mainly elementary with some advanced topics - 3 lectures on MCMC, a lecture on principal components and a lecture on permutation tests.
Level: undergraduate
http://www.youtube.com/playlist?list=PLUAHeOPjkJseXJKbuk9-hlOfZU9Wd6pS0

Statistics 110: Probability. Harvard University. Joe Blitzstein. 40 hours.
Level: Intro. and intermediate undergraduate math stat
https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo

Probability. Ben Garside, 255 videos of about 10 minutes each.
Level: Introductory
https://www.youtube.com/playlist?list=PLAvgI3H-gclbyWnE9WrSq68_HRW9rOZUw

Probability Methods in Civil Engineering by Prof. Rajib Maity, Department of Civil Engineering, IIT Kharagpur.
Level: Introductory. Includes two lectures on copulas
http://www.youtube.com/watch?v=PIjVdOTgjN8&list=PLB8D1F86227F088BB

Probability and Statistics, IIT Kharagpur, Somesh Kumar, 40 one hour lectures.
Level: undergrad intro math stat course
http://freevideolectures.com/Course/3009/Probability-and-Statistics

Probabilistic Systems Analysis and Applied Probability, John Tsitsiklis. MIT 6.041 / 6.431 as taught in Fall 2010, 25 lectures. Includes 3 lectures on Markov processes.
Level: undergraduate
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/video-lectures/

Probabilistic Systems Analysis and Applied Probability, John Tsitsiklis. MIT 6.041 / 6.431 as taught in Fall 2013.
Level: undergraduate
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/

Statistics and Probability Theory. MH Faber. ETH Zurich
Level: Intro. math stat
http://www.yovisto.com/collection/2111#.VCQ9TPldV8F

Probability and Random Processes, IIT Kharagpur, Mrityunjoy Chakraborty. 40 one hour lectures. Later lessons include spectral analysis.
Level: undergrad intro math stat course.
http://freevideolectures.com/Course/2324/Probability-and-Random-Processes

Introduction to Statistics. University of London. 2009. 3.5 hours
Level: Elementary (compressed)
http://videolectures.net/cernstudentsummerschool09_cowan_is/

Probability and Random Variables EE230, Middle East Technical University. 14 weeks of lectures.
Level: undergraduate, intro math stat
http://www.youtube.com/playlist?list=PLuiPz6iU5SQ8ra5kjxx770vk_famaeuvz

MATHEMATICAL STATISTICS

Mathematical Statistics I - UCCS MathOnline Course 481. 34 hours. Greg Morrow. U. Colorado, Colorado Springs.
Level: Advanced undergrad
https://www.youtube.com/playlist?list=PL9520222F814EE9B6

Linear Models - UCCS MathOnline Course. 28 videos of 75 minutes each. 32 hours. Greg Morrow. U. Colorado, Colorado Springs.
Level: Grad
https://www.youtube.com/playlist?list=PLF16A3260236ECE17

Statistical Inference, IIT Kharagpur, Somesh Kumar, 40 one hour lectures.
Level: advanced undergrad math stat course
http://freevideolectures.com/Course/3259/Statistical-Inference

Probability Foundation for Electrical Engineers. Dr. Krishna Jagannathan,Department of Electrical Engineering,IIT Madras. 49 one hour videos. Includes measure theoretic probability.
https://www.youtube.com/playlist?list=PLbMVogVj5nJQqGHrpAloTec_lOKsG-foc

Estimation Theory. Anish Chand Turlapaty. University of Maryland Eastern Shore. 73 videos.
Level: Graduate
https://www.youtube.com/watch?v=IgCL_AoD_0U&index=1&list=PLuolQmYLOmvd-ZFlTkvwVUjsOrBqNVqJH

Asymptotic Behaviour of Estimators, by Ben Lambert, 23 videos, 2 hours, 7 minutes. Proof of the CLT. Convergence in probability discussion.
Level: graduate
https://www.youtube.com/playlist?list=PLwJRxp3blEvZBAn3bwAAtdJqotRPBWBlP

QUEUEING

Queueing Theory. MAP6264, MAP6265, Robert Cooper, Florida Atlantic Univ. 29 lectures. V. good.
Level: Graduate
https://www.youtube.com/channel/UCJbnVTvSB6gEt4kgOLOMQMA/playlists

Queueing. Onno Boxma. V. good. 20 one hour sessions.
Level: graduate
http://www.networkmaths.ie/videos/list_videos.php?course=queue
http://www.networkmaths.ie/courses/syllabus-que
http://www.networkmaths.ie/courses/reading-que

Introduction to Queueing Theory. by Sam Ou, 14 videos, 3 hours, 59 minutes
Level: graduate
https://www.youtube.com/playlist?list=PLWMqMAYxtBM_qjCvaPXTNuSpzGkqbMTrY

Performance Evaluation of Computer Systems by Prof.Krishna Moorthy Sivalingam, Department of Computer Science and Engineering, IIT Madras.
Level: advanced undergrad, grad
http://www.youtube.com/playlist?list=PL10481A90D3C5C3E6

Queueing Theory For Computer Scientists, Raj Jain. Washington University, St. Louis. 8 videos. 3 hours, 28 minutes. First video has an echo, but sound improves in later videos.
Level: Advanced undergrad or graduate
https://www.youtube.com/playlist?list=PLoCUlAGhY8dJ-ssZauVYUo-LEWx03F2_h

STOCHASTIC MODELS

Stochastic Models. STA4821 Robert Cooper, Florida Atlantic Univ. 27 lectures. V. good.
Level: Advanced undergraduate/ graduate.
http://vimeo.com/album/211356

Introduction to Markov Processes, Christof Schütte, 14 lectures
Level: grad course
http://www.networkmaths.ie/courses/syllabus-mar
http://www.networkmaths.ie/courses/reading-mar
http://www.networkmaths.ie/videos/list_videos.php?course=mar

Stochastic Processes by Dr. S. Dharmaraja, Department of Mathematics, IIT Delhi. 39 videos of 50 minutes each.
Level: Grad
http://www.youtube.com/playlist?list=PLbMVogVj5nJSdK0QYFHb-dNHwMstca4OM

Discrete Stochastic Processes, Robert Gallager. MIT. 6.262 As Taught In Spring 2011, 25 lectures.
Level: Graduate
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/

IE-325 Stochastic Models, Bilkent University. Savas Dayanik. 40 lectures of 50 minutes each.
Level: Advanced undergrad; grad
http://video.bilkent.edu.tr/course_videos.php?courseid=11

DATA MINING AND MACHINE LEARNING

Learning from Data. A machine learning course. CalTech. Yaser Abu-Mostafa.
Level: advanced undergrad, grad.
http://work.caltech.edu/telecourse.html

Introduction to machine learning. with Trevor Hastie and Rob Tibshirani, Stanford. 15 hours. Uses their own book An Introduction to Statistical Learning with Applications in R (free downloadable or for sale in hard cover). V. Good.
http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

Machine Learning. Andrew Ng. Stanford Univ. Complete course. V. good.
Level: Undergrad/Grad
https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599
Course syllabus and lecture notes
http://www.stanford.edu/class/cs229/

Data Mining and Machine Learning. CAP6673. Taghhi Khoshgoftaar, Florida Atlantic Univ., 24 lectures.
Level: Graduate
http://vimeo.com/album/1505953

Statistics 202: Statistical Aspects of Data Mining, Summer 2007. David Mease. Stanford. Google.
http://www.stats202.com/original_index.html

Machine Learning, Nando de Freitas, UBC, 2013. 23 hours
Level: Graduate
http://www.youtube.com/playlist?list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&feature=view_all
http://www.cs.ubc.ca/~nando/540-2013/lectures.html

Machine Learning. Mathematical Monk. 160 vidoes of 15 minutes each. Nice. Includes Bayesian methods, MLE's, hidden Markov chains, logistic regression, importance sampling, MCMC.
Level: graduate
http://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA

Machine Learning Summer School. Cambridge. 2009. Lectures by a variety of adademics. (not really a unified course)
Level: grad
http://mlg.eng.cam.ac.uk/mlss09/index.html

Machine Learning (Goldman Sachs sponsored) in 4 lectures. Larry Wasserman and John Lafferty
http://tepper.cmu.edu/prospective-students/masters/masters-in-computational-finance/curriculum/sample-lectures/goldman-sachs-lecture-series

Machine Learning: 10-601. Carnegie Mellon. Tom Mitchell, Aarti Singh. Mixture of videos and other material.
Level: grad
http://www.cs.cmu.edu/~aarti/Class/10601/lectures.shtml

Basics of probability and statistics for machine learning. Mikaela Keller
3 hours. Good presentation.
Level: undergrad/grad
http://videolectures.net/bootcamp07_keller_bss/

Big Data Fundamentals. by Raj Jain. 7 videos, 2 hours
Level: Graduate
https://www.youtube.com/playlist?list=PLoCUlAGhY8dJhkBaU-wDGfsq7pE24PhQG

Kernel Methods, Bernhard Schölkopf. Nice lectures. 4 hours.
Level: advanced.
http://videolectures.net/mlss2011_scholkopf_kernel/

Introduction to Kernel Methods, by Liva Ralaivola. 3.5 hours.
Level: intermediate
http://videolectures.net/bootcamp2010_ralaivola_ikm/

Pattern Recognition (aka machine learning, multivariate statistics). Universitat Heidelberg. Fred Hambrecht. 46 lectures. Full course.
Level: Grad
http://www.youtube.com/watch?v=J66RrnZZkOw&list=PLuRaSnb3n4kRDZVU6wxPzGdx1CN12fn0w

Course on Information Theory, Pattern Recognition, and Neural Networks. David MacKay (University of Cambridge). 16 lectures.
Level: Medium undergrad
http://videolectures.net/course_information_theory_pattern_recognition/

Conditional Random Fields. Charles Elkan, Department of Computer Science and Engineering, UC San Diego. 6 hours. Nice.
Level: Graduate
http://videolectures.net/cikm08_elkan_llmacrf/

REGRESSION and ECONOMETRICS

Jeremy Balka's Statistics website. Basically a course in elementary statistics, but he has a nice section giving an intro to simple linear regression. Recommended.
http://www.jbstatistics.com/

Regression Analysis, IIT Kharagpur, Soumen Maity, 40 one hour lectures.
Level: graduate/ advanced undergraduate, applied statistics
http://freevideolectures.com/Course/3091/Regression-Analysis

Online tutorial for Regression Modeling with Actuarial and Financial Applications, by Edward W. (Jed) Frees. 4 hours.
https://ewfreesres.github.io/RegressModel/index.html

Econometric Modelling, IIT Kharagpur, Rudra P. Pradhan, 40 one hour lectures. Includes regression, logit, probit, time series.
Level: graduate/ advanced undergraduate, applied statistics
http://freevideolectures.com/Course/3028/Econometric-Modelling

Linear Models UCCS MathOnline. Greg Morrow. Lecture 1 is missing. Lectures 2-28.
Level: advanced graduate.
https://www.youtube.com/watch?v=If4DO4EUrWc&list=PLF16A3260236ECE17

A full course in econometrics - undergraduate level - part 1 by Ben Lambert, 199 videos
A full course in econometrics - undergraduate level - part 2 by Ben Lambert, 72 videos, 7 hours
https://www.youtube.com/playlist?list=PLwJRxp3blEvZyQBTTOMFRP_TDaSdly3gU
https://www.youtube.com/playlist?list=PLwJRxp3blEvb7P-7po9AxuBwquPv75LjU

A graduate course in econometrics, by Ben Lambert, 74 videos, 6 hours
https://www.youtube.com/playlist?list=PLwJRxp3blEvaxmHgI2iOzNP6KGLSyd4dz

ECON 306 Econometrics. Penn State. Dan Goldstein. 141 videos.
Level: 3rd year undergrad.
http://vimeo.com/album/1810869/page:1/sort:preset/format:thumbnail

Statistics with R: Courtney Brown. Emory University. Political Science Course. Applied Stat. Half regression. Great speaker, great anecdotes. Fascinating story of 1960 Kennedy/Nixon election in Lecture 1 at time 37:20
Level: advanced undergraduate, graduate
https://www.youtube.com/watch?v=2-kw1MlOS1U&list=PL7mT7MDlWGhXS54yIGEjmck7Rmqp_WBBR

R Tutorials: Econometrics in R, by econometricsacademy, 16 videos; 3 hours, 30 minutes
Level: graduate, applied
https://www.youtube.com/playlist?list=PLRW9kMvtNZOiXeaU8JaFIu-Rj1G6UmMsz

Analysis of Variance. U of Georgia.
Level: Grad or undergrad.
http://jonathantemplin.com/teaching/academic-courses/analysis-of-variance/

Control of Manufacturing Processes (SMA 6303) at MIT. 2008. David Hardt, Duane Boning. Seems to be about one third on ANOVA.
http://ocw.mit.edu/courses/mechanical-engineering/2-830j-control-of-manufacturing-processes-sma-6303-spring-2008/index.htm

Econometrics Academy. Ani Katchova. Includes regression, time series, principal components, factor analysis, logit, probit analysis. Looks good.
Level: Graduate
https://sites.google.com/site/econometricsacademy/

Economics 421/521 - Econometrics - Winter 2011. Mark Thoma.
Level: undergrad/grad
https://www.youtube.com/watch?v=WK03XgoVsPM&list=PLD15D38DC7AA3B737

Andy Field's lectures
Regression, ANOVA and more.
https://www.youtube.com/playlist?list=PLEzw67WWDg81vYKqY1iD3JHSsDKV6j98E Also
http://www.statisticshell.com/html/woodofsuicides.html

MULTIVARIATE METHODS

Applied Multivariate Analysis. by Dr. Amit Mitra, Dr. Sharmishtha Mitra, Department of Mathematics and Science, IIT Kanpur.
Level: Graduate
http://www.youtube.com/playlist?list=PL3DFCC23FCE3C7EFB

Applied Multivariate Statistical Modeling. IIT Kharagpur. Dr J Maiti.
Level: Graduate
http://nptel.ac.in/courses/110105060/

Psychology 943/930. Fundamentals of Multivariate Modeling, Fall 2012 (UNL, University of Nebraska-Lincoln)
Level: Grad
http://jonathantemplin.com/fundamentals-multivariate-modeling-fall-2012-unl/

Dr. Dawg. 611 Statistics course. Includes Factor analysis, regression, ANOVA, MANOVA, nonparametrics. 44 vidoes of 7 minutes each.
https://www.youtube.com/user/TAMUCDrDawg

Stochastic Hydrology. by Prof. P. P. Mujumdar, Department of Civil Engineering, IISc Bangalore
40 videos, 38 hours. Time series, multivariate methods. (Don't let the course title deceive you.)
Level: graduate, applied
https://www.youtube.com/playlist?list=PLC2E6D572A3227164

Factor Analysis and SEM. by Ben Lambert, 19 short videos, Total time: 1 hour, 45 minutes
Level:Grad
https://www.youtube.com/playlist?list=PLwJRxp3blEvaOTZfSKXysxRmi6gXJf5gP

Principal Component Analysis and Factor Analysis, by econometricsacademy, 5 videos, 2 hours
Level: Graduate
https://www.youtube.com/playlist?list=PLRW9kMvtNZOjaStLK9ldf_Yc8MB6TkCUx

TIME SERIES

Time Series Analysis. ritvikmath. Quite good. I like the lecture style. Sometimes refers to topics that have not been fully discussed. 34 videos averaging 9 minutes each.
https://www.youtube.com/playlist?list=PLvcbYUQ5t0UHOLnBzl46_Q6QKtFgfMGc3

Applied Time Series with R, by Quant Education, 21 videos, 3 hours, 11 minutes
Level: advanced undergraduate
http://www.youtube.com/playlist?list=PLRj8HuwfWZEvp05ZjtZry245w4XVGoaei

Time Series Econometrics. Ralf Becker. Good presentation. About 3 hours.
Level: Grad.
https://www.youtube.com/watch?v=FJ1VRD6Xmcg&list=PLW7MJJThJQQtZHdqLPZK1_0uff_a5LonB

Time Series ARIMA Models (Autoregressive Integrated Moving Average Models) at econometricsacademy, by Ani Katchova, 5 videos, 1 hour, 50 minutes
Level: advanced undergraduate
http://www.youtube.com/playlist?list=PLRW9kMvtNZOg2_cx8e_zFcOQzstLIHgw8

Time Series and Forecasting, by Jason Delaney, 8 videos, 3 hours, 5 minutes
Level: Advanced undergraduate
http://www.youtube.com/playlist?list=PL1A0EF77860E94EB3

Time Series in R Session 1.1, by Ryan Womack. There are 6 videos on time series in R.
Level: advanced undergraduate
http://www.youtube.com/watch?v=QHsmAM6nktY

Whats New in Econometrics? Time Series. NBER Summer Institute 2008. James Stock, Harvard, Mark Watson, Princeton.
Level: Advanced. Assumes previous knowledge of time series.
http://www.nber.org/SI_econometrics_lectures.html

BROWNIAN MOTION

Introduction to Brownian Motion and the Ito Integral, Robert Simione, 5 videos of 10 minutes each.
Level: advanced undergraduate, graduate
https://www.youtube.com/playlist?list=PLC7A868A384F6EFF0

Brownian motion by stepbi, l2 videos, 17 minutes
Level: advanced undergrad, grad
https://www.youtube.com/playlist?list=PLB5AFBF93C7CFE48F

Stochastic Diff Eqn with R, by Quant Education, 13 videos, 1 hour, 23 minutes
Level: graduate
https://www.youtube.com/playlist?list=PLRj8HuwfWZEsXW2pzAwAWYC8EZbD2ieOq

BAYESIAN METHODS

STAT 544 - Introduction to Bayesian statistics at Iowa State University, by Jarad Niemi, 28 videos, 8 hours. Looks good.
Level: graduate
http://www.youtube.com/playlist?list=PLFHD4aOUZFp0Xhzd5j1nWnExD54xJfnJX

Bayesian Lectures. Deirdre Toher. 22 ten minute lectures.
Level: Grad/ advanced undergrad
http://www.youtube.com/watch?v=X3dpewhUiMc&list=UUTqZYOHuGOqJkwaUmZthsPg

Bayesian statistics: a comprehensive course. 55 videos 6 hours.
Level Graduate
https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm

BIOSTATISTICS

Intro Biostatistics course. U of Wisconsin. Various Topics, Various speakers. 30 one-hour lectures.
Level: Introductory
https://www.youtube.com/user/BiostatisticsMCW/videos

Introduction to Biostatistics. Yale University. James Dziura
Level: Intro
https://medicine.yale.edu/ycci/education/phd/onlinecourses/biostatistics.aspx

IMP Principles of Clinical Research. Yale University. Various instructors.
Level: Intermediate for MD's (assumes some medical background and terminology)
https://medicine.yale.edu/ycci/education/phd/onlinecourses/clinicalresearch.aspx

Introduction to Epidemiology. Raywat Deonandan. U of Ottawa. 2014. Good personality.
Level: Med students
http://www.youtube.com/playlist?list=PL26T3rqZf-b2vtPrrjcuzrE_YpPF7fRPi

Biostatistics and Epidemiology. UCSDTraumaBurn. 4 hours
Level: Med students
http://www.youtube.com/playlist?list=PL8NkkvvWlbpQJsQogbwo_ylI5YlIGwkst

3rd Short Course on Statistical Genetics and Genomics. University of Alabama at Birmingham. 2013.
Level: Grad
http://www.soph.uab.edu/ssg/nigmsstatgen/third#
If one changes "third" to "first","second","fourth", one will find similar short courses, perhaps with some different speakers.

Statistics Corner, Terry Shaneyfelt. Ten 5 minute videos. Includes odds ratios and relative risk.
Level: Introductory for medical practioners.
https://www.youtube.com/watch?v=llXEGuxvh28&index=1&list=PLM2dV1wp3vruwTFJEe4340slAybmRaQ6d

FINANCIAL MATH

ACT 460 / STA 2502 - Stochastic Methods for Actuarial Science University of Toronto Course , Prof. S. Jaimungal. 2013. Includes Brownian motion and Ito integrals
Level: Advanced undergrad/grad
http://www.utstat.utoronto.ca/sjaimung/courses/sta2502/main.htm

MMF1928H / STA 2503F - Pricing Theory I / Applied Probability for Mathematical Finance University of Toronto Course , Prof. S. Jaimungal. 2013. Mainly a math finance course.
Level: Grad.
http://www.utstat.toronto.edu/sjaimung/courses/mmf1928/content2013.htm

MA520 - Financial Mathematics, by Phil Byrne. Twelve 50 minute videos.
Level: Intermediate undergrad
https://www.youtube.com/playlist?list=PL1BaGV1cIH4UXkZgrX2TtXhvUQW6_4hZF

University of Chicago Financial Math. 2012. Greg Lawler, Robert A. Fefferman, Mark Hendricks.
http://www-finmath.uchicago.edu/admissions/lecture_videos.shtml

Quantitative Finance. Nathan Whitehead discusses Paul Wilmott's text. Twenty 10 minute videos.
Level: advanced undergrad.
https://www.youtube.com/watch?v=vZXCq2fn-Z0&list=PL8DC1113ADB63358E

COMP510 - Computational Finance - 2007 HKUST. 30 hours. Steven Skiena
Level: Grad or advanced undergrad
https://www.youtube.com/playlist?list=PL9E205B8FAAD530E1

LEARNING R

Texas A&M R video Tutorial. Short simple and good.
Level: Undergrad
http://dist.stat.tamu.edu/pub/rvideos/

Computing for Data Analysis: (Intro to R.) Roger Peng.
Level: Grad
https://www.youtube.com/user/rdpeng/playlists

Statistics with R for newbies, by Ahmetrics, Variety of R techniques. 58 videos. 9 hours.
Level: advanced
http://www.youtube.com/playlist?list=PL926147BD8DE746D6

Videos on Data Analysis with R
http://jeromyanglim.blogspot.ca/2010/05/videos-on-data-analysis-with-r.html

OTHER TOPICS

Texas A&M SAS Tutorial. Basic and good.
Level: undergrad
http://dist.stat.tamu.edu/flash/SAS/

Exploratory Data Analysis (EDA). Boris Steipe. 2011.
http://bioinformatics.ca/workshops/2011/exploratory-data-analysis-and-essential-statistics-using-r

LISA, at Virginia Tech University. There are MANY tutorial videos here on various topics, some quite good.
Level: elementary to advanced.
http://www.lisa.stat.vt.edu/?q=short_courses

Six Sigma, IIT Kharagpur, T. P. Bagchi, 40 one hour lectures. Quality Control.
Level: graduate/ advanced undergraduate, applied statistics
http://freevideolectures.com/Course/3013/Six-Sigma

Probabilistic Graphical Models. Daphne Koller. Stanford University. 9 week course. Bayesian networks, Markov networks, MCMC. Good.
Level: Graduate
https://www.coursera.org/course/pgm

Probability Primer. Mathematical Monk. 42 videos of 15 minutes each. Nice.
Level: Measure theory probability.
http://www.youtube.com/watch?v=Tk4ubu7BlSk&list=PL17567A1A3F5DB5E4

Mathematical Foundations of Computer Networking. S. Keshav. U of Waterloo. Includes linear programming, queueing, intro probability. 62 videos, 39 hours
Level: graduate
http://blizzard.cs.uwaterloo.ca/keshav/mediawiki-1.4.7/index.php/Lecture_videos

Martingale. A single lecture by Hans Gerber of Gerber-Shiu fame.
http://www.personal.psu.edu/afs1/gerber_m/

Nonparametric Statistics for Experiments, by Daniel Houser One 90 minute summary. Good speaker.
Level: medium undergrad
http://newmedia.ufm.edu/gsm/index.php?title=Houserstatistics

Random Matrix Theory. Pierpaolo Vivo. 2013. University of Paris. 6 hours. Good speaker. Nice format.
Level: Graduate
http://videolectures.net/netadis2013_vivo_random_matrices/

Introduction to Data Science. Bill Howe. University of Washington. 30 hours?
Level: Grad
https://class.coursera.org/datasci-001/lecture

Model Thinking, by Scott E. Page. 30 hours
Level: Grad
https://class.coursera.org/modelthinking-006/lecture

Operations Management Video Series, by Lisa Bussom - Widener University. 142 videos. 3 hours, 44 minutes. Speaks well.
Level: Advanced undergrad
https://www.youtube.com/playlist?list=PLwcu5sBq_fM4RkvjDoEOAHPmwCRppS4jX

Computer Systems Performance Analysis. Raj Jain. Washington Univ. St. Louis. Includes measurement, modelling, queueing, regression, experimental design, simulation. 49 videos. 20 hours.
Level: Graduate
https://www.youtube.com/playlist?list=PLoCUlAGhY8dLc1Qgo-8WO9IuJvg9xdt9G

Data Science. Series of colloquia by various speakers. USC and NIH.
https://bigdatau.ini.usc.edu/data-science-seminars

Deep Learning (DLSS) Summer School,Montreal 2017
Series of lectures by different speakers at high level, including topics such as machine learning, neural networks.
http://videolectures.net/deeplearning2017_montreal/

"Coursera is a for-profit educational technology company offering massive open online courses (MOOCs) founded by computer science professors Andrew Ng and Daphne Koller from Stanford University. Coursera works with universities to make some of their courses available online. Courses are generally free, but there are ways to generate revenue, including certification fees, introducing students to potential employers and recruiters (with student consent), tutoring, sponsorships and tuition fees." (wikipedia) In statistics there is a large list of courses.
https://www.coursera.org/courses?orderby=upcoming&cats=stats

EdX is similar to Coursera but with a smaller selection of courses.
https://www.edx.org/course-list/allschools/statistics-data-analysis/allcourses