datasciencespecialization.github.io
Data Science SpecializationCommunity Site
http://datasciencespecialization.github.io/
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Data Science Specialization | datasciencespecialization.github.io Reviews
https://datasciencespecialization.github.io
Community Site
datasciencespecialization.github.io
Other Resources
http://datasciencespecialization.github.io/other
Configuring R and RStudio (Linux). Installing xlsx and XML packages on Debian Wheezy. Rscript to customize R environment. Installs packages used in the specialization. Installing Some Basic R Packages in Ubuntu; Ibrahim El Merehbi. Using Projects in RStudio. Using Version Control with RStudio. Using R behind HTTP/HTTPS Proxy. Ignoring R and RStudio files. Gitignore template for R. Github Help - Using Git / Ignoring files. Windows batch file to work around RStudio startup issues. Dockerize a Shiny App.
About
http://datasciencespecialization.github.io/about
The Data Science Specialization. Is a 9 courses series on Data Science. Every class in the series runs every month, and the course material is availible on GitHub. The JHU Data Science Lab:. Allan Inocêncio de Souza Costa.
The Data Scientist's Toolbox
http://datasciencespecialization.github.io/toolbox
The Data Scientist's Toolbox. Working with files in Bash. Git and GitHub Video Playlist. Also available for download. A Beginner’s Quick Reference Guide for Git Commands. Understanding the Relationship Between Git and GitHub. Simple Guide to GitHub Forks. Github Repo Tutorial How to fork a repo, download it to your local drive and commit changes. Configuring RStudio to work with Git / Github - Mac OSX. Configuring RStudio to work with Git / Github - Windows. Complete notes for The Data Scientist’s Toolbox.
Exploratory Data Analysis
http://datasciencespecialization.github.io/eda
Creating a Kite Graph. Analyzing Top/Green500 Supercomputer Technology Trends. Data Analysis using Twitter API and Python. Complete notes for Exploratory Data Analysis.
Statistical Inference
http://datasciencespecialization.github.io/statinf
Why degrees of freedom decrease for sample variance. Analysis of exponential distribution of births data set from the CDC. Exponential Distribution / Central Limit Theorem - Assignment Checklist. ToothGrowth Analysis - Assignment Checklist. Exploratory Data Analysis in ToothGrowth Assignment. Explaining the exploratory data analysis requirement for students who have not taken the. Course prior to taking. Using MathJax with Discussion Forums, R Markdown, and Github Pages. Kable Tables with Data Frames.
TOTAL PAGES IN THIS WEBSITE
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Technical References | Utsav Bali
http://utsavbali.com/scientist/technicalreferences
Ask an impertinent question and you're on your way to a pertinent answer. Isn't that the very essence of science? 8212; Fox Mulder, The X-Files. Skip to primary content. New Forest Pony Express 60 Miles. Mulhacen – Spain. Monte Rosa – Italy. Why martial arts…. My Amazon Wish List. My Academic Reviews (for personal reference only! Quick Reference for Vi/Emacs. Quick Reference for Basic Unix Commands. Quick Reference for Basic Perl Syntax. Quick Reference for R Syntax. Tech Notes for R. My Git Hub Repos.
carstenj
http://carstenj.io/2015/10/03/kom-i-gang-med-data-science.html
Software, matematik og data. Kom i gang med Data Science. Dette indlæg markerer starten på en ny vidensrejse og en serie af indlæg om min kommende erfaring med Data Science. Af Thomas C. Erren, Tracy E. Slanger, J. Valérie Groß, Philip E. Bourne og Paul Cullen. I denne forbindelse har jeg fokus på regel 4:. Focus on the Future but Don’t Ignore the Past. Jeg er helt sikker på at rapporten har ret. Data analyse vil blive en vigtig kompetence at besidde i den nærmeste fremtid. Hvis du har kigget bar...Jeg h...
Data Science by Robert Jonczy | ZEEF
https://data-science.zeef.com/robert.jonczy
Jenda R. Benetka. Data Science related links. Python, R, Spark. Passionate about #DataScience, #DataMining, #MachineLearning, #DeepLearning, #NLP, etc…. Published 1 year ago. Updated 3 months ago. Jenda R. Benetka. Data Scrapping / Crawlers. Journal of Machine Learning Research Homepage. XKCD plots in Matplotlib. Mining the Social Web. A gallery of interesting IPython Notebooks. 538 Election Forecasting Model. Six Degrees of Kevin Bacon. Data Science Python Notebooks. Reddit - Data is beautiful.
christian-foricher.blogspot.com
Christian Foricher: Data Science, Learning Machine
http://christian-foricher.blogspot.com/2014/04/learning-machine.html
Jeudi 24 avril 2014. Data Science, Learning Machine. Http:/ immersivemath.com/ila/index.html. Http:/ ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/. Https:/ share.coursera.org/wiki/index.php/DataSciSpec:Useful Resources. Http:/ www.cs.cmu.edu/ tom/10701 sp11/lectures.shtml. Https:/ mitpress.mit.edu/books/introduction-machine-learning. Http:/ www.cs.cmu.edu/ tom/10601 fall2012/lectures.shtml. Https:/ www.kaggle.com/wiki/Tutorials. Http:/ www.math.univ-toulouse.
Waldron Lab - Coursera
http://waldronlab.github.io/coursera
Waldron Lab Research Website. April 16th, 2016. Is an online platform for learning. The courses are free to sign up and should be accessed via direct link or from the search function on the website. Have to complete the course although it is a good idea. To have the certificate of completion. From Johns Hopkins University. Is a great set of courses that can be taken to enhance your skills working with and interpreting data. Particularly, the R Programming. Waldron Lab Research Website.
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DataScienceSource.com
AWS Instance Set Up. Python With Spyder Tutorial. Python With Spyder Tutorial. The videos below provide a tutorial on Python 2.7 using Anaconda Python and the Spyder IDE (which comes with Anaconda Python). Anaconda Python is available for free from Continuum Analytics. To go to the download page and look for the Download Anaconda button. You will need to provide your e-mail. Python with Spyder 1: First Steps (16 minutes). Python With Spyder 2: Basic Arithmetic and Variable Assignment (24 minutes). This s...
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datasciencespecialization.github.io
Data Science Specialization
This is site is meant to serve as a directory for the amazing content the community has created around the Data Science Specialization. If you are interested in contributing click here. The Data Scientist’s Toolbox. Getting and Cleaning Data.
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