Here is the latest bag of tweets^*, which covers November 2012.

(*) These are interesting news that I found on Twitter and that I archive periodically.

• stefanjudis: For git power users!!! A lot of advanced git stuff for terminal usage. #git http://t.co/qbJhG2FT (25 Nov)
• DataJunkie: Good Python implementation of Damerau-Levenshtein distance http://t.co/TiApm2Ba (24 Nov)
• siah: Introduction to Machine Learning: Logistic Regression for Predicting Bad Trips http://t.co/4LbENlJx (24 Nov)
• siah: Programmer Competency Matrix http://t.co/07yXE7BI (24 Nov)
• siah: Deep Learning Tutorials http://t.co/Fn9kFwuu (24 Nov)
• vsbuffalo: Front of NYTimes: Scientists See Promise in Deep-Learning Programs #machinelearning http://t.co/4iw7TnwK (24 Nov)
• Piboonrungroj: A list of more than 90 FREE R tutorials by different universities #rstats http://t.co/8pdLfgQ1 (24 Nov)
• druvus: Bioclipse-R: Integrating management and visualization of life science data with statistical analysis http://t.co/uMRxKZlf #bioinformatics #R (24 Nov)
• johannux: Object oriented programming in R, covering S4 style methods and classes - a brief overview | http://t.co/CpZm2I3W #Rstats (24 Nov)
• statschat: Why real data is important in teaching http://t.co/x7bsvCU0 (24 Nov)
• fonnesbeck: %load_ext d3graph Very exciting! (24 Nov)
• planetclojure: Nice looking JQuery with Clojuresript http://t.co/ETq1NbfV (23 Nov)
• leonpalafox: Time to read NIPS 2012 papers http://t.co/eI8Bmxss (23 Nov)
• CoolSWEng: Course notes on Mathematical Writing by Don Knuth and other illustrious colleagues. http://t.co/vk2vhKTO A true gem! (23 Nov)
• japerk: NLTK + sklearn for text classification: http://t.co/gQhokSxQ (22 Nov)
• TWiecki: Blog post on PyConCa and some recent #IPython advances by @fperez_org http://t.co/Kd8Ikopo (21 Nov)
• cemerick: Significant refresh of nREPL documentation: https://t.co/FFWmhaY1 #clojure (21 Nov)
• arnicas: Hive Plots in R - this document is very nice. (pdf) http://t.co/pjJO4AUA (21 Nov)
• Petzoldt: Checking out “Mining Text Data”. Good idea: Using transfer mining to create training corpus for supervised learning. http://t.co/1Fd4cq9z (21 Nov)
• abhi9u: renjin - JVM-based Interpreter for the R Language for Statistical Computing http://t.co/JZTz0Iif via @prismatic (21 Nov)
• arnicas: A fascinating 4-axes square line graph here by accurat: http://t.co/xG2acuWj plus other amazing graphics (http://t.co/atvuo45s>) (21 Nov)
• sharon000: Create simple, free charts with @Datawrapper http://t.co/9CLCJjKB #ddj #dataviz #datavis #datajournalism (21 Nov)
• Chris_Evelo: Our new PLoS1 paper: “Molecular Pathways Involved in Prostate Carcinogenesis: Insights from Public Microarray Datasets” http://t.co/ETUtstOd (21 Nov) rickasaurus: Best Practices for Scientific Computing http://t.co/KapmEwSD (21 Nov)
• genetics_blog: The nature of confounding in GWAS http://t.co/4NdQRXV4 (20 Nov)
• FGRibreau: Node-webkit - an app runtime based on Chromium and node.js // Feels like adobe air… but open. http://t.co/y2w07Fh5 (20 Nov)
• yannabraham: A tutorial in displaying mass spectrometry-based proteomic data using heat maps http://t.co/U6JHW3a5 (20 Nov)
• MEDevEcon: Maurizio Pisati on #spatial #data analysis in @Stata http://t.co/ffCxuSjM (20 Nov)
• triadsou: [Diagnostic] / “FDA Course, The Statistical Evaluation of Medical Tests for Classification and Prediction, Margaret S…” http://t.co/XM5uN4mM (20 Nov)
• arnicas: Nodebox has a new (maybe?) tutorial on doing data visualizations! http://t.co/PUm5QzyF #python (19 Nov)
• ProfAndyField: Handout updates continue with repeated measures ANOVA http://t.co/xLAiTaX9 (19 Nov)
• JMP_software: How to root out fraudulent & otherwise unusual data from clinical trials – the 1st part in a series by Richard Zink: http://t.co/K9rQWQf7 (19 Nov)
• joscani: Six sigma with R . Libro de R sobre control de calidad. http://t.co/HYwpyDXr (19 Nov)
• siah: Probabilistic Data Structures for Web Analytics and Data Mining http://t.co/xN481JT0 (19 Nov)
• yannabraham: Combining imaging and pathway profiling: an alternative approach to cancer drug discovery http://t.co/7HSf1K5i (19 Nov)
• Atabey_Kaygun: A brief guide to CLOS http://t.co/XCY8p2zM (19 Nov)
• DiffusePrioR: New blog post: The Heteroskedastic Probit Model http://t.co/LnfAuLN8 (19 Nov)
• cyrillerossant: High-performance visualization in the #IPython Notebook with #Galry http://t.co/bP3UDJyv #dataviz #opengl #gpu #hpc #python (18 Nov)
• rOpenSci: pander: An #rstats Pandoc writer http://t.co/ZZgmR08J (18 Nov)
• vsbuffalo: Hacker Monthly is a great, great magazine but it needs less business, more coding. http://t.co/Hh1Knu0Y (18 Nov)
• ctford: My Functional Composition talk from #clojure_conj is on Github - https://t.co/DSXuojiN #Overtone (18 Nov)
• albertocairo: Working hard! RT @saakshita Homework for week 3 at #MOOC with Alberto Cairo blogged http://t.co/Hh9Ctc91 (18 Nov)
• jebyrnes: Like #rstats and @EOL? Check out the Reol package http://t.co/Qe1g4gUQ (16 Nov)
• jcukier: after the simple models the other day http://t.co/9eXCXZ7M here’s a more complex one http://t.co/9gx25lq3 I had been working on for a while (16 Nov)
• jseabold: You can now load any R dataset into a DataFrame using statsmodels. duncan = sm.datasets.get_rdataset("Duncan", "car") http://t.co/Fl50QLmH (16 Nov)
• jandot: Nice #d3 tutorial by @vlandham http://t.co/dgggipk5 (16 Nov)
• jseabold: @fonnesbeck This one? http://t.co/jTRUol4D (16 Nov)
• SciPyTip: Seven Python libraries you should know about http://t.co/z4JZnJe3 (12 Nov)
• lynaghk: My talk on the grammar of graphics + #clojure is online already! http://t.co/edNrj9vY Damn Øredev, you guys are fast! #rstats #datavis (11 Nov)
• FGRibreau: Sisyphus.js - Gmail-like client-side drafts and bit more // Garlic.js w/ more options http://t.co/vW4bVzwy (11 Nov)
• TWiecki: CythonGSL 0.2.1 released. Cython interface for the GNU Scientific Library (GSL). Changes: https://t.co/f4WLuwpG (11 Nov)
• neilfws: Bookmarked: The Official For Dummies Cover Generator http://t.co/0d6Q0axE (11 Nov)
• blattnerma: Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems #recsys http://t.co/ABalf22y (11 Nov)
• patrickDurusau: Introducing Wakari #topicmaps #python #data #dataanalysis - http://t.co/2Z1LvMPZ (11 Nov)
• jonathanstray: Anybody who does topic-based text analysis work (e.g. LDA, LSI) should see this post on visualizing topic models http://t.co/70GKHwXk (11 Nov)
• jergason: I blogged a blog about categorical distributions in JavaScript! A gentle introduction for beginners. http://t.co/b84Wc1xe (9 Nov)
• sgsfak: @siah My comment at http://t.co/SEgTwZqS links to SICP where I first saw this Sieve example: http://t.co/uC2RrDRW (8 Nov)
• siah: Being a polyglot programmer http://t.co/VIattgnD (8 Nov)
• freakonometrics: for those who still want to understand how to play with data http://t.co/yrdhcSCW for the description of Nate Silver’s methodology (7 Nov)
• lizardbill: Interesting data visualization of last night’s election results. http://t.co/TANY8LKR (7 Nov)
• alignedleft: New list for posting (and getting) datavis gigs, moderated by @arnicas: https://t.co/ssI3qr6c (5 Nov)
• fonnesbeck: RT @arqbackup: Arq 2.9.2 is out! Pick “Check for Updates” from the Arq menu to get it. Several issues fixed: http://t.co/G1iz67K2 (5 Nov)
• alignedleft: Great process notes on the “512 Paths to the White House” graphic: http://t.co/UpR4Iltw by @mbostock (thanks to @kissane) (5 Nov)
• abmathewks: Let’s try this: Instead of saying, “The probability is 75%,” say “There’s a 25% chance I’m wrong” http://t.co/aEOWJdfv (5 Nov)
• REAS: Dan @Shiffman reinvents publishing with his excellent Nature of Code and Magic Book projects. http://t.co/Q9auDDK2 https://t.co/jsqKMtc9 (5 Nov)
• kjhealy: @drewconway @johnmyleswhite See e.g. https://t.co/u1wFVR2N and the link to the blogpost therein. (5 Nov)
• yannabraham: “If you’ve never used GPG to encrypt a file, now is the time to learn; keep your SNP data encrypted” http://t.co/kC8QE7XU (5 Nov)
• denisparra: wanna use d3.js for reusable charts without spending too much time learning JS ? check http://t.co/J3wh7qLc #visualization #js #d3.js (4 Nov)
• arnicas: My blog post with a (very) few comments about Strata and PyData: http://t.co/9Q5XnKtP (4 Nov)
• arnicas: Linear/matrixy large network display with BioFabric: http://t.co/4i8ZIrDA (software avail). Via @wjrl59 (3 Nov)
• JohnDCook: “When you are young you are afraid people will steal your ideas; when you are old you are afraid they won’t.” — David D. Friedman (3 Nov)
• albertocairo: Good post RT @bryanchristie On ink drawings and information graphics: http://t.co/IcNuwzMI … (3 Nov)
• gappy3000: “Iterative Reweighted Algorithms for Matrix Rank Minimization” http://t.co/GkVuw3Uw (3 Nov)
• zentree: “Getting Started with #Processing and Data Visualization”. http://t.co/XHkLAcKe (3 Nov)
• stefanjudis: Fantastic talk about #Git and #GitHub Secrets by @holman. More information, tips and tricks is nearly not possible. http://t.co/uh6QD8zs (3 Nov)
• Symcat: Good read. RT @Medgadget: Computational medicine enhances way doctors detect, treat disease http://t.co/HVOmYOKU (2 Nov)
• DataJunkie: GIS with Python, Shapely and Fiona (very cool!) http://t.co/wiU8lVN2 (2 Nov)
• stuartsierra: This is big RT @datomic_team: run #datomic on #riak http://t.co/prX2nvJZ (2 Nov)
• DianeMcKenna: Genomes project publishes inventory of human genetic variation #genomics #genetics #DNA http://t.co/pHCWlDTj (2 Nov)
• kaggle: Intro to t-SNE ( Merck Challenge Viz winner algo ) Implementations for #matlab #python #R #C http://t.co/cYeRrSmB (2 Nov)
• moorejh: Another example of authors not knowing the literature: Scan Statistics in Human Gene Mapping http://t.co/xxNK5Qy4 #genetics #genomics (1 Nov)
• pierreroudier: #knitr + @rstudioapp is ridiculously cool http://t.co/dE7GCfa3 Even works with Python, Bash, etc http://t.co/49lQILpO #rstats (1 Nov)
• drewconway: Great looking class from @josh_wills for learning a sophisticated analytics tool chain http://t.co/k7VKpzqw (1 Nov)
• jedisct1: RT @igrigorik: an interesting analysis of various caching strategies for your dropbox data: http://t.co/V9J29vvF - tl;dr, LRU. (1 Nov)
• JanWillemTulp: 100k tweets visualized with GraphInsight. And the best is: you can download the data and try it yourself: http://t.co/lXpXMrsj #dataviz (1 Nov)