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  1. Interview with Nick Chamandy, statistician at Google
  2. You and Your Researchvideo
  3. Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained
  4. A Survival Guide to Starting and Finishing a PhD
  5. Six Rules For Wearing Suits For Beginners
  6. Why I Created C++
  7. More advice to scientists on blogging
  8. Software engineering practices for graduate students
  9. Statistics Matter
  10. What statistics should do about big data: problem forward not solution backward
  11. How signals, geometry, and topology are influencing data science
  12. The Bounded Gaps Between Primes Theorem has been proved
  13. A non-comprehensive list of awesome things other people did this year.
  14. Jake VanderPlas writes about the Big Data Brain Drain from academia.
  15. Tomorrow’s Professor Postings
  16. Best Practices for Scientific Computing
  17. Some tips for new research-oriented grad students
  18. 3 Reasons Every Grad Student Should Learn WordPress
  19. How to Lie With Statistics (in the Age of Big Data)
  20. The Geometric View on Sparse Recovery
  21. The Mathematical Shape of Things to Come
  22. A Guide to Python Frameworks for Hadoop
  23. Statistics, geometry and computer science.
  24. How to Collaborate On GitHub
  25. Step by step to build my first R Hadoop System
  26. Open Sourcing a Python Project the Right Way
  27. Data Science MD July Recap: Python and R Meetup
  28. git 最近感悟
  29. 10 Reasons Python Rocks for Research (And a Few Reasons it Doesn’t)
  30. Effective Presentations – Part 2 – Preparing Conference Presentations
  31. Doing Statistical Research
  32. How to Do Statistical Research
  33. Learning new skills
  34. How to Stand Out When Applying for An Academic Job
  35. Maturing from student to researcher
  36. False discovery rate regression (cc NSA’s PRISM)
  37. Job Hunting Advice, Pt. 3: Networking
  38. Getting Started with Git
  1. Machine Learning, Big Data, Deep Learning, Data Mining, Statistics, Decision & Risk Analysis, Probability, Fuzzy Logic FAQ
  2. A Funny Thing Happened on the Way to Academia . . .
  3. Advice for students on the academic job market (2013 edition)
  4. Perspective: “Why C++ Is Not ‘Back’”
  5. Is Fourier analysis a special case of representation theory or an analogue?
  6. The Beauty of Bioconductor
  7. The State of Statistics in Julia
  8. Open Source Misfeasance
  9. Book review: The Signal and The Noise
  10. Should the Cox Proportional Hazards model get the Nobel Prize in Medicine?
  11. The most influential data scientists on Twitter
  12. Here is an interesting review of Nate Silver’s book. The interesting thing about the review is that it doesn’t criticize the statistical content, but criticizes the belief that people only use data analysis for good. This is an interesting theme we’ve seen before. Gelman also reviews the review.—–Simply Statistics
  13. Video : “Matrices and their singular values” (1976)
  14. Beyond Computation: The P vs NP Problem – Michael Sipser—-This talk is arguably the very best introduction to computational complexity .
  15. What are some of your personal guidelines for writing good, clear code?
  16. How do you explain Machine learning and Data Mining to non CS people?
  17. Suggested New Year’s resolution: start a blog:  A blog forces you to articulate your thoughts rather than having vague feelings about issues; You also get much more comfortable with writing, because you’re doing it rather than thinking about doing it; If other people read your blog you get to hear what they think too. You learn a lot that way. || Set aside time for your blog every day. Keep notes for yourself on bloggy subjects (write a one-line gmail to yourself with the subject “blog ideas”).
  18. The most influential data scientists on Twitter
  19. Tips on job market interviews
  20. The age of the essay
  1. Grad Student’s Guide to Good Coffee+Grad Student’s Guide to Good Tea
  2. Favorite Apps for Work and Life
  3. estimating a constant (not really)
  4. Reinforcement Learning in R: An Introduction to Dynamic Programming
  5. The Future of Machine Learning (and the End of the World?)
  6. 10 Papers Every Programmer Should Read (At Least Twice)
  7. R in the Press
  8. On Chomsky and the Two Cultures of Statistical Learning
  9. Speech Recognition Breakthrough for the Spoken, Translated Word
  10. Frequentist vs Bayesian
  11. w4s – the awesomeness we’re experiencing
  12. Why is the Gaussian so pervasive in mathematics?
  13. C++ Blogs that you Regularly Follow
  14. An interview with Brad Efron about scientific writing. I haven’t watched the whole interview, but I do know that Efron is one of my favorite writers among statisticians.
  15. Slidify, another approach for making HTML5 slides directly from R.  (1) It is still just a little too hard to change the theme/feel of the slides (2) The placement/insertion of images is still a little clunky, Google Docs has figured this out, if they integrated the best features of Slidify, Latex, etc. into that system, it will be great.
  16. Statistics is still the new hotness. Here is a Business Insider list about 5 statistics problems that will“change the way you think about the world”.
  17. New Yorker, especially the line,”statisticians are the new sexy vampires, only even more pasty” (via Brooke A.)
  18. The closed graph theorem in various categories
  19. Got spare time? Watch some videos about statistics
  20. About the first Borel-Cantelli lemma
  21. Yihui Xie—-The Setup
  22. Best Practices for Scientific Computing
  1. Towards Better PDF Management with the Filesystem
  2. What is life like for PhDs in computer science who go into industry?
  3. Online REPL for 17 programming languages
  4. Logistic regression vs. multiple regression—–Many statisticians seem to advise the use of logistic regression over multiple regression by invoking this logic: “A probability value can’t exceed 1 nor can it be less than 0. Since multiple regression often yields values less than 0 and greater than 1, use logistic regression.” While we can understand this argument, our feeling is that, in the applied fields we toil in, that argument is not a very practical one. In fact a seasoned statistics professor we know says (in effect): “What’s the big deal? If multiple regression yields any predicted values less than 0, consider them 0. If multiple regression yields any values greater than 1, consider them 1. End of story.” We agree.
  5. Scientific Python
  6. An everyday essential: the timer+My personal productivity rules
  7. Bill Thurston—by Terrace Tao; Bill Thurston, 1946-2012—by Peter Woit; Bill Thurston 1946-2012—by David Speyer.
  8.  Surviving a PhD: 10 top tips that shows how to survive your PhD
  9. How different PhD’s work:Differences and similarities between departments about PhD process
  10. Countdown Begins: Countdown starts for submission of the thesis
  11. PhD Life is Wonderful:Doing PhD at Warwick University is a wonderful experience
  12. Too Many Emails In Your Inbox: Use Outlook folders to manage your emails
  13. Introduction to REX Facility: Videos for introducing Wolfson Research Exchange and its facilities
  14. Power of Supervisors: Control,inner happiness and optimisim
  15.  Unorthodox Tools of a Researcher: Reflection and examples of unorthodox tools that helps you PhD period
  16. Homesickness and Culture Clashes: Homesickness of international students and cultural differences
  17. Choosing Your PhD Examiners: Tips for choosing the relevant examiners for PhD Viva
  18. Effective Research Tools: Examples of useful research tools
  19. PhD,Risks and Murphy’s Law: “Anything that can go wrong will go wrong” according to Murphy’s Law
  20. Will Data Scientists Be Replaced by Tools?
  21. Update: TeX Writer for iPad (+ LaTeX + AMS)
  22. Why physicists like models, and why biologists should
  23. The ENCODE project: lessons for scientific publication
  24. Perspectives From A Postdoc: What is a Postdoc?
  25. Chris Blattman gives advice on PhD students’ NSF applications
  26. ENCODE floods the news networks…
  27. Maybe mostly useful for me, but for other people with Tumblr blogs, here is a way to insert Latex.—From Simply Statistics
  28. Harvard Business school is getting in on the fun, calling the data scientist the sexy profession for the 21st century. Although I am a little worried that by the time it gets into a Harvard Business document, the hype may be outstripping the real promise of the discipline. Still, good news for statisticians! (via Rafa via Francesca D.’s Facebook feed).—From Simply Statistics
  29. The counterpoint is this article which suggests that data scientists might be able to be replaced by tools/software. I think this is also a bit too much hype for my tastes. Certain things will definitely be automated and we may even end up with a deterministic statistical machine or two. But there will continually be new problems to solve which require the expertise of people with data analysis skills and good intuition (link via Samara K.)—From Simply Statistics
  1. Getting Started with the WordPress Competition
  2. Simple Made Easy
  3. An Education TsunamiWill on-line courses destroy universities?
  4. Universities Reshaping Education on the Web
  5. Explanation or Prediction? An Amazing Quote from Phil Schrodt
  6. Should you apply PCA to your data?
  7. Which classifiers are fast enough for exploring medium-sized data?
  8. Quick classifiers for exploring medium-sized data (redux)
  9. Is C++ worth it?
  10. Unbiased estimators can be terrible
  11. Things You Should Never Do, Part I
  12. The Joel Test: 12 Steps to Better Code
  13. Methodologists’ Audience
  14. Bayesian Methodology in the Genetic Age
  15. Interview with Michael Hammel, author of The Artist’s Guide to GIMP
  16. Being Happy in Grad School
  17. 10 Fresh Tips for Finding Time to Blog
  18. A Quick Guide to Using Tumblr for Business
  19. Statistics Done Wrong
  20. Top N Reasons To Do A Ph.D. or Post-Doc in Bioinformatics/Computational Biology
  21. Interview(s) with Vladimir Voevodsky with an introduction on motivic homotopy along with the video and transcript.
  22. Are there examples of non-orientable manifolds in nature?
  23. Kolmogorov Complexity – A Primer
  24. Adventures at My First JSM (Joint Statistical Meetings) #JSM2012
  25. Yes, I was hacked. Hard.
  26. Does Julia have any hope of sticking in the statistical community?
  27. How Genome Sequencing is Revolutionizing Clinical Diagnostics, from the ISMB Conference
  28. Advice for an Undergraduate
  29. 4 things you should know about choosing examiners for your thesis
  30. The long tail of free online education : The author also plans to teach a class on graph partitioning, expander graphs, and random walks online in Winter 2013.
  31. Teaching the World to Search
  32. Beyond Pinterest and Instagram – ten visual social networks that should be on your radar
  33. Making Ubuntu 12.04 useable
  34. Basic Understanding of Compressed Sensing
  1. Simplicity is hard to sell
  2. Self-Repairing Bayesian Inference
  3. Praxis and Ideology in Bayesian Data Analysis
  4. In-consistent Bayesian inference
  5. Big Data Generalized Linear Models with Revolution R Enterprise
  6. Quants, Models, and the Blame Game
  7. Fun with the googleVis Package for R
  8. Topological Data Analysis
  9. The Winners of the LaTeX and Graphics Contest 
  10. Is Machine Learning Losing Impact?
  11. Machine Learning Doesn’t Matter?
  12. Components of Statistical Thinking and Implications for Instruction and Assessment
  13. Xiao-Li Meng and Xianchao Xie rethink asymptotics
  14. Higgs boson and five sigma
  15. What is the Statistics Department 25 Years From Now?
  16. Statistics: Your chance for happiness (or misery)
  17. Manifolds: motivation and definition
  18. Why Emacs is important to me? : ESS and org-mode
  19. Interesting Emacs linkfest
  20. Devs Love Bacon: Everything you need to know about Machine Learning in 30 minutes or less
  21. Visualizing Galois Fields
  22. Visualizing Galois Fields (Follow-up)
  23. Statistical Reasoning on iTunes U
  24. Computing log gamma differences
  25. Where to start if you’re going to revise statistics
  26. Power laws and the generalized CLT
  27. Open problems in next-gen sequence analysis
  28. More equations, less citations?
  29. Talk: Some Introductory Remarks on Bayesian Inference
  1. An easy way to think about priors on linear regression
  2. Combining priors and downweighting in linear regression
  3. Metropolis Hastings MCMC when the proposal and target have differing support
  4. Slidify: Things are coming together fast
  5. How to Convert Sweave LaTeX to knitr R Markdown: Winter Olympic Medals Example
  6. Testing R Markdown with R Studio and posting it on RPubs.com
  7. Announcing The R markdown Package
  8. Announcing RPubs: A New Web Publishing Service for R
  9. Approximate Bayesian computation
  10. Load Packages Automatically in RStudio
  11. Practical advice for machine learning: bias, variance and what to do next
  12. The overview article on “Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis” associated with the invited talk at the upcoming PODS 2012 meeting is on the arXiv here.
  13. The monograph on “Randomized Algorithms for Matrices and Data” is available in NOW’s “Foundations and Trends in Machine Learning” series here, and it is also available on the arXiv here.
  14. Click here for information (including the slides and video!) on the Tutorial on “Geometric Tools for Identifying Structure in Large Social and Information Networks,” given originally at ICML10 and KDD10 and subsequently at many other places. (The slides are also linked to below.)
  15. The overview chapter on “Algorithmic and Statistical Perspectives on Large-Scale Data Analysis” is finally on the arXiv here; the book in which it will appear is in press; and a video of the associated talk is here.
  16. Recent teaching: Fall 2009: CS369M: Algorithms for Massive Data Set Analysis
  17. Confidence distributions
  18. Making a singular matrix non-singular
  19. Statistics Versus Machine Learning
  20. How to post R code on WordPress blogs
  21. Pro Tips for Grad Students in Statistics/Biostatistics (Part 1)
  22. Pro Tips for Grad Students in Statistics/Biostatistics (Part 2)
  23. Why You Shouldn’t Conclude “No Effect” from Statistically Insignificant Slopes
  24. For those interested in knitr with Rmarkdown to beamer slides
  25. Notes from A Recent Spatial R Class I Gave
  26. Sparse Bayesian Methods for Low-Rank Matrix Estimation and Bayesian Group-Sparse Modeling and Variational Inference – implementation
  27. The Battle of the Bayes
  28. Ockham Workshop, Day 1
  29. Ockham Workshop, Day 2
  30. Ockham Workshop, Day 3
  31. Ockham’s Razor
  32. Occam

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