There is many Data Science resources available online. So here is my Data Science Resources that I compiled, I think it should be useful on my path of learning Data Science.
- An Introduction to Statistical Learning with Applications in R: http://www-bcf.usc.edu/~gareth/ISL/
- A Programmer's Guide to Data Mining: http://guidetodatamining.com/
- Doing Bayesian Data Analysis: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/
- Think Stats: Probability and Statistics for Programmers: http://www.greenteapress.com/thinkstats/html/index.html
- Probabilistic Programming & Bayesian Methods for Hackers: https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
- Frequentism and Bayesianism: A Practical Introduction: http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/
- Online Stat Book: http://onlinestatbook.com/index.html
- Practical Data Science with R: http://www.manning.com/zumel/
- Better Explained: http://betterexplained.com/articles/a-brief-introduction-to-probability-statistics/
- 10 FREE Resources to Learn Statistics: http://www.marketingdistillery.com/2014/09/06/10-free-resources-to-learn-statistics/
- Philosophy of Statistics: http://plato.stanford.edu/entries/statistics/
- Probability Course: http://www.probabilitycourse.com/
- Teoria da Amostragem: http://alexandreprofessor.blogspot.pt/p/tipos-de-teorias.html
- Intervalos de confiança: http://www.pedro-magalhaes.org/intervalos-de-confianca-so-para-nerds/
- Pivot Tables in R with melt and cast: http://marcoghislanzoni.com/blog/2013/10/11/pivot-tables-in-r-with-melt-and-cast/
- Introduction to R from Tiny Data: http://ramnathv.github.io/pycon2014-r/explore/tidy.html
- UCLA R Resources: http://statistics.ats.ucla.edu/stat/r/
- R Language for Programmers: http://www.johndcook.com/blog/r_language_for_programmers/ Hands-On Data Science with R: http://onepager.togaware.com/
- Getting Started with R Markdown, knitr, and Rstudio: http://jeromyanglim.blogspot.pt/2012/05/getting-started-with-r-markdown-knitr.html
- R Programming: https://www.coursera.org/course/rprog
- Getting and Cleaning Data: https://www.coursera.org/course/getdata
- Introduction to Data Science: https://www.coursera.org/course/datasci
- Stanford Statistical Learning: http://online.stanford.edu/course/statistical-learning
- The Data Scientist's Toolbox: https://www.coursera.org/course/datascitoolbox
- Exploratory Data Analysis: https://www.coursera.org/course/exdata
- Pattern Discovery in Data Mining: https://www.coursera.org/course/patterndiscovery
- Reproducible Research: https://www.coursera.org/course/repdata
- Process Mining: Data science in Action: https://www.coursera.org/course/procmin
- Data Analysis and Statistical Inference: https://www.coursera.org/course/statistics
- PH525.1x: Statistics and R for the Life Sciences: https://www.edx.org/course/statistics-with-r-for-life-sciences-harvardx-ph525-1x
- PH525.2x: Introduction to Linear Models and Matrix Algebra: https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra-harvardx-ph525-2x
- PH525.3x: Advanced Statistics for the Life Sciences: https://www.edx.org/course/advanced-statistics-for-the-life-sciences-harvardx-ph525-3x
- PH525.4x: Introduction to Bioconductor: https://www.edx.org/course/introduction-to-bioconductor-harvardx-ph525-4x
- Introduction to Big Data with Apache Spark: https://www.edx.org/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x#.VPNHSOE2Wwk
- Introduction to Probability - The Science of Uncertainty: https://www.edx.org/course/introduction-probability-science-mitx-6-041x-0#.VPNHmOE2Wwk
- Stat2.1X Introduction to Statistics: Descriptive Statistics: https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x#.VPNICeE2Wwk
- Stat2.2x Introduction to Statistics: Probability: https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x#.VPNIFOE2Wwk
- Stat2.3x Introduction to Statistics: Inference: https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x#.VPNIJOE2Wwk
- Big Data and Social Physics: https://www.edx.org/course/big-data-social-physics-mitx-mas-s69x#.VPNIH-E2Wwk
- 15.071x The Analytics Edge: https://www.edx.org/course/analytics-edge-mitx-15-071x-0#.VPNGyeE2Wwk
- Zen of Python Examples: http://stackoverflow.com/questions/228181/the-zen-of-python
- Problem Solving with Algorithms and Data Structures: http://interactivepython.org/courselib/static/pythonds/index.html
- Pandas Basics: http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb
- Useful Pandas Snippets: http://www.swegler.com/becky/blog/2014/08/06/useful-pandas-snippets/
- Sentiment Analysis: http://blog.kimonolabs.com/2014/12/17/guest-blog-sentiment-analysis-on-web-scraped-data-with-kimono-and-monkeylearn/
- Remote Data Access - pandas: http://pandas.pydata.org/pandas-docs/stable/remote_data.html
- Create HTML or PDF Files with R Knitr MikTex Pandoc: http://rprogramming.net/create-html-or-pdf-files-with-r-knitr-miktex-and-pandoc/
- How to Easily Add PHP Codes in Blogger: http://www.mybloggerlab.com/2013/10/how-to-easily-add-php-codes-in-blogger.html
- QuantMod Basics - Stock Data Download and Manipulation: http://gekkoquant.com/2012/05/13/quantmod-basics-stock-data-download-and-manipulation/
- Web-Scraping: the Basics: http://quantifyingmemory.blogspot.co.uk/2014/02/web-scraping-basics.html
- Linear Algebra Resources: http://www.itshared.org/2015/02/best-time-to-learn-linear-algebra-is-now.html
- Stanford Datasets: http://snap.stanford.edu/data/index.html
- Chicago Crime (2001 to Present): https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
- Matplotlib: http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb
- Intro to D3.js http://square.github.io/intro-to-d3/web-standards/
- verkter/My-Data-Science-Resources: https://github.com/verkter/My-Data-Science-Resources
- Collection of Data Science Resources: https://github.com/jonathan-bower/DataScienceResources
- The Open Source Data Science Masters: http://datasciencemasters.org/
- A Practical Intro to Data Science: http://diggdata.in/post/50410269207/a-practical-intro-to-data-science
- Data Science Ontology: http://www.datascienceontology.com/
- How to Become a Data Scientist Inforgraphic: http://blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg