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{
"name": "Data Science Part Time Course - SYD DAT 6",
"tagline": "Sydney Part Time Data Science with General Assembly 6",
"body": "# Course Page\r\n\r\n\r\n\r\n**Instructor:** Alasdair Douglas\r\n\r\n**Teaching Assistant:** Louis Tsang\r\n\r\n**Location:** Level M, 56-58 York St Sydney NSW 2000\r\n\r\n\r\n\r\n**Dates:** 10/10/2016 - 14/12/2016\r\n\r\n**Time:** 6:00 p.m. - 9:00 p.m., Monday and Wednesday evenings\r\n\r\n## Schedule\r\n\r\n|week| **Monday** | **Wednesday** |\r\n|---|----------|-------------|\r\n|1 | 10/10: [Introduction](#class-1-introduction) | 12/10: Basics of Data Science with Python and Git |\r\n|2 | 17/10: Data Visualisation | 19/10: Linear Regression |\r\n|3 | 24/10: Logistic Regression | 26/10: Model Evaluation |\r\n|4 | 31/10: Regularisation & Dimensionality Reduction | 02/11: Clustering |\r\n|5 | 07/11: Decision Trees | 09/11: Random Forest & Ensembling |\r\n|6 | 16/11: Recommendation Engines | 21/11: Cloud Computing, Big Data and Spark |\r\n|7 | 23/11: Natural Language Processing | 26/10: Graphs & Network Analysis |\r\n|8 | 28/11: Time Series | 30/11: Causality |\r\n|9 | 05/12: Communication | 07/12: Neural Networks & Deep Learning |\r\n|10 | 12/12: Course Review & Project Presentations | 14/12: Project Presentations |\r\n\r\n\r\n[test](#test)\r\n\r\n# test\r\n\r\n## Pre-Work\r\n\r\n### Installation and Setup\r\n\r\n- Install the [Anaconda](http://continuum.io/downloads) distribution of Python 2.7x.\r\n- Install [Git](http://git-scm.com/book/en/v2/Getting-Started-Installing-Git) and create a [GitHub](https://github.com/) account.\r\n- Once you receive an email invitation from [Slack](https://slack.com/), join our \"SYD_DAT_6 team\" and add your photo!\r\n\r\n## Resources\r\n\r\n[PEP 8 - Style Guide for Python](http://www.python.org/dev/peps/pep-0008)\r\n\r\n## Readings\r\n\r\n- Read the first two chapters of [The Data Science Handbook](http://www.thedatasciencehandbook.com/)\r\n- Read the first two chapters of an [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf)\r\n\r\n## Optional\r\nYou're also more than welcome to do the following if you're keen to get extra advanced for your first class:\r\n\r\n- [Python codecademy course](https://www.codecademy.com/learn/python)\r\n- Chapters 1, 2 and 5 of [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)\r\n- [Learn Python the Hard Way](http://ihansel.github.io/SYD_DAT_4/www.learnpythonthehardway.org)\r\n- [Command Line Crash Course](http://cli.learncodethehardway.org/book/)\r\n- [Khan Academy on Probability](https://www.khanacademy.org/math/probability)\r\n\r\n---\r\n\r\n## Class 1: Introduction\r\n\r\n- Slides\r\n- Lab\r\n- Introduction to General Assembly\r\n- Course overview: our philosophy and expectations\r\n- Tools: check for proper setup of Git, Anaconda, overview of Slack\r\n\r\n**Homework:**\r\n\r\n- Resolve any installation issues before next class.\r\n- Make sure you have a github profile and created a repo called \"SYD_DAT_4\"\r\n- Clone the class repo (this one!)\r\n\r\n**Optional:**\r\n\r\n- Read [Analyzing the Analyzers](http://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf) for a useful look at the different types of data scientists.\r\n- Read about [Markdown](http://daringfireball.net/projects/markdown/syntax) Techniques and refer to this [cheat sheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\r\n\r\n",
"note": "Don't delete this file! It's used internally to help with page regeneration."
}