This document provides an overview of free resources for learning data science. It recommends the Data Science Handbook, DataCamp for learning coding skills, and An Introduction to Statistical Learning for algorithms and concepts. It also recommends Kaggle for data science projects and competitions and notes that it allows working on datasets in a virtual machine without installing software. The resources are aimed at beginners and provide reviews of books, websites, MOOCs and podcasts for learning data science.

1. Data Science Sydney - Careers
Ian Hansel - Director Verge Labs
Materials for Getting
Started with Data Science

2. There are many, many,
many resources for
learning Data Science. We
are going to cover five
resources that give a good
overview of the field and
are free

3. The Data Science Handbook
Interviews with leading Data Scientists such as DJ
Patil, Hilary Mason and Drew Conway
Covers what a Data Scientist actually does at work,
how teams operate, data-centric approaches to
problems and communication of results
“Expect that you’re going to have to talk to lots
and lots of people. It took me 6 months before
eBay took a chance on me.”
DJ PATIL
“

4. Datacamp.com
Online resource for learning how to code.
Covers skills like data manipulation, visualisation
and machine learning in both R and Python.
Does not require you to install software on your own
machine, all you need is a web browser. So you can
hit the ground running!

5. An Introduction to Statistical Learning
“... this book provides clear and intuitive guidance on how to
implement cutting edge statistical and machine learning methods.
ISL makes modern methods accessible to a wide audience without
requiring a background in Statistics or Computer Science”
Larry Wasserman
“
The simplest and most concise book that covers the
algorithms and concepts you will come across as a
data scientist.
Gives practical examples, code to try out and an
explanation of how the algorithms work.

6. Kaggle
More than just a competition site it is a community
of data scientists sharing ideas and approaches to
data science
A great resource for learning is Kaggle Kernels. This
allows you to do machine learning from a virtual
machine supplied by Kaggle. You can work on
existing datasets or upload and share your own
Does not require you to install software on your own
machine, all you need is a web browser. So you can
hit the ground running!

7. This is a collection of materials for learning data
science aimed at beginners. It provides reviews of
books, websites, podcasts, Massive Online Open
Courses (MOOCS - edX, coursera, etc) and what you
can expect to get out of the material.
Data Sci Guide
““What skills are the best add-ons to the skills you have
already developed? or skills for a certain industry or
subset of data science?”
Renee Teate

8. Resources
● The Data Science Handbook (book), http://www.thedatasciencehandbook.com/
● Data Camp, www.datacamp.com
● An Introduction to Statistical Learning (book), http://www-bcf.usc.edu/~gareth/ISL/
● Kaggle, https://www.kaggle.com
● Data Sci Guide, http://www.datasciguide.com/
● William S. Cleveland, An Action Plan for Expanding the Technical Areas of the Field of Statistics, https://utexas.instructure.com/files/35465950/download
● FiveThirtyEight, http://fivethirtyeight.com/
● KD Nuggets, http://kdnuggets.com/
● Coursera, https://www.coursera.org
● EdX, https://www.edx.org/
● Khan Academy, www.khanacademy.org
● Meta Academy, www.metacademy.org
● Data Science Masters, www.datasciencemasters.org
● Talking Machines Podcast, https://www.thetalkingmachines.com/
● Data Science Sydney Meetup, http://www.meetup.com/Data-Science-Sydney/
● R User group Meetup, http://www.meetup.com/R-Users-Sydney/
● Data Science Breakfast Meetup, http://www.meetup.com/The-Sydney-Data-Science-Breakfast-Meetup-Group/