Path to : How do I become a data scientist?

Source taken from : http://qph.is.quoracdn.net/main-qimg-7caa00a2ec674c434900d97d584a8dc4?convert_to_webp=true

Step by step way to approach to become a data Scientist:
http://www.quora.com/Data-Science/How-do-I-become-a-data-scientist

http://hail-data.quora.com/How-to-acquire-the-Essential-Skill-Set-the-Self-Starter-way

Quote

On average people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves. - Nate Silver

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DATA SCIENCE RESOURCES : Data Science

Resources : Data Science

Data Science is an inherently multidisciplinary field that requires a myriadof skills to be a proficient practitioner. The necessary curriculum has not fit into traditional course offerings, but asawareness  of theneed  for individuals who have such abilities is growing, we are seeing universities and private companies creating custom classes.

• Books
• An Introduction to Data Science: The companion textbook to Syracuse University’s flagship course for their new Data Science program.

• Courses
• UC Berkeley: Introduction to Data Science: A course taught by Jeff Hammerbacher and Mike Franklin that highlights each of the varied skills that a Data Scientist must be proficient with.
• CouHow to Process, Analyze and Visualize Data: A lab oriented course that teaches you the entire pipeline of data science; from acquiring datasets and analyzing them at scale to effectively visualizing the results.
• CMCoursera: Introduction to Data Science: A tour of the basic techniques for Data Science including SQL and NoSQL databases, MapReduce on Hadoop, ML algorithms, and data visualization.
• Columbia: Introduction to Data Science: A very comprehensive course that covers all aspects of data science, with an humanistic treatment of the field.
• Columbia: Applied Data Science (with book): Another Columbia course — teaches applied software development fundamentals using real data, targeted towards people with mathematical backgrounds.
• Coursera: Data Analysis (with notes and lectures): An applied statistics course that covers algorithms and techniques for analyzing data and interpreting the results to communicate your findings.
• Kaggle: Getting Started with Python for Data Science: A guided tour of setting up a development environment, an introduction to making your first competition submission, and validating your results.
• http://ischool.syr.edu/future/cas/applieddatasciencemooc.aspx

Resources : Others

• Data Beta: Professor Joe Hellerstein’s blog about education, computing, and data.
• Dataists: Hilary Mason and Vince Buffalo’s old blog that has a wealth of information and resources about the field and practice of data science
• Five Thirty Eight: Nate Silver’s famous NYT blog where he discusses predictive modeling and political forecasts.
• grep alex: Alex Holmes’s blog about distributed computing and the intricacies of Hadoop.
• Data Science 101: One man’s personal journey to becoming a data scientist (with plenty of resources)
• no free hunch: Kaggle’s blog about the practice of data science and its competition highlights.

• Berkeley: Introduction to Data Science: One of the most comprehensive lists of resources about all things data science.
• Cloudera: New to Data Science: Resources about data science from Cloudera’s introduction to data science course/certification.
• Kaggle: Tutorials: A set of tutorials, books, courses, and competitions for statistics, data analysis, and machine learning.
• http://dataiap.github.io/dataiap
• http://cs229.stanford.edu/materials.html
• http://www-stat.stanford.edu/~naras/stat290/Stat290_Website/Stat_290.html
• http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1
• http://www.ischool.berkeley.edu/courses/i290-abdt
• http://hackershelf.com/topic/machine-learning/
• http://www.e-booksdirectory.com/listing.php?category=284
• http://www.intechopen.com/books/machine-learning
• http://pages.cs.wisc.edu/~shavlik/cs760.html
• http://www.realtechsupport.org/UB/MRIII/papers/MachineLearning/Alppaydin_MachineLearning_2010.pdf
• http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/index.html
• http://www3.nd.edu/~steve/Rcourse/Rnotes.html
• http://alex.smola.org/teaching/cmu2013-10-701/
• http://www.cmpe.boun.edu.tr/~ethem/i2ml2e/
• http://courses.ischool.berkeley.edu/i296a-dsa/s12/
• http://datascienc.es/spring-2011-course/