Instructor: Alasdair Douglas
Teaching Assistant: Louis Tsang
Location: Level M, 56-58 York St Sydney NSW 2000
Dates: 10/10/2016 - 14/12/2016
Time: 6:00 p.m. - 9:00 p.m., Monday and Wednesday evenings
| week | Monday | Wednesday |
|---|---|---|
| 1 | 10/10: Introduction | 12/10: Basics of Data Science with Python and Git |
| 2 | 17/10: Data Visualisation | 19/10: Linear Regression |
| 3 | 24/10: Logistic Regression | 26/10: Model Evaluation |
| 4 | 31/10: Regularisation & Dimensionality Reduction | 02/11: Clustering |
| 5 | 07/11: Decision Trees | 09/11: Random Forest & Ensembling |
| 6 | 16/11: Recommendation Engines | 21/11: Cloud Computing, Big Data and Spark |
| 7 | 23/11: Natural Language Processing | 26/10: Graphs & Network Analysis |
| 8 | 28/11: Time Series | 30/11: Causality |
| 9 | 05/12: Communication | 07/12: Neural Networks & Deep Learning |
| 10 | 12/12: Course Review & Project Presentations | 14/12: Project Presentations |
- Install the Anaconda distribution of Python 2.7x.
- Install Git and create a GitHub account.
- Once you receive an email invitation from Slack, join our "SYD_DAT_6 team" and add your photo!
- Read the first two chapters of The Data Science Handbook
- Read the first two chapters of an Introduction to Statistical Learning
You're also more than welcome to do the following if you're keen to get extra advanced for your first class:
- Python codecademy course
- Chapters 1, 2 and 5 of Python for Data Analysis
- Learn Python the Hard Way
- Command Line Crash Course
- Khan Academy on Probability
- [Slides](/slides/Data Science Week 1 - Monday 10 October - Intro.pdf)
- Lab
- Introduction to General Assembly and the Data Science Part Time course
- Course overview: our philosophy and expectations
- Agree on a way of working
- Tools: check for proper setup of Git, Anaconda, overview of Slack
Homework:
- Resolve any installation issues before next class.
- Make sure you have a github profile and have forked this repo "SYD_DAT_6"
- Clone the fork you created on your github profile to your computer
- Complete the brief skill survey after Lesson 1 https://goo.gl/forms/sCot5Y4CfuO1Oacg2
Optional:
- Read Analyzing the Analyzers for a useful look at the different types of data scientists.
- Read about Markdown Techniques and refer to this cheat sheet
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- [Slides](/slides/Data Science Week 1 - Wednesday 12 October.pdf)
- Lab
- What is data science
- What does a Data Scientist need to succeed
- How does a data science project flow
- What is Git
- Using Git bash
- Using Git for version control and collaboration
- Using the Pandas package for data manipulation in Python
Homework:
- Homework1.ipynb which is located in the homework folder. Due Friday the 21st of October
- Finish reading the first two chapters of an Introduction to Statistical Learning
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- [Slides](/slides/Data Science Week 2 - Monday 17 October - Data Visualisation.pdf)
- Lab
- What is data visualisation
- Why we visualise data
- How do we visualise data
- Philosophies on visualising data
- Git sync with upstream, make changes, push and make pull request
- Git lab
- Visualisation Lab
Homework:
- Homework1.ipynb which is located in the homework folder. Due Friday the 21st of October
- Reading chapter 3 of an Introduction to Statistical Learning on Linear Regression <<<<<<< HEAD =======
- Slides
- Lab
- Understand the differences between supervised and unsupervised learning
- Describe the process of building a linear regression model
- Build a linear regression model and interpret the output
Homework:
- Homework1.ipynb which is located in the homework folder. Due Friday the 21st of October
- Reading chapter 4 of an Introduction to Statistical Learning on Classification
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