View on GitHub

Data Science Part Time Course

General Assembly - Sydney

Download this project as a .zip file Download this project as a tar.gz file

General Assembly

Course Repository

Course materials for General Assembly's Data Science course

Instructor: Ian Hansel

Teaching Assistant: Matt Gibson

Location: Level M, 56-58 York St Sydney NSW 2000

Location

Dates: 21/03/2016 - 01/06/2016

Time: 6:00 p.m. - 9:00 p.m.

Schedule

Monday Wednesday
21/03: Introduction 23/03: Basics
28/03: Easter Break 30/03: Data Visualisation
04/04: Linear Regression 06/04: Logistic Regression
11/04: Model Evaluation 13/04: Regularisation
18/04: Clustering 20/04: Recommendation Engines
25/04: Anzac Day 27/04: Dimensionality Reduction
02/05: Decision Trees 04/05: Random Forests & Ensembling
09/05: Cloud Computing 11/05: Natural Language Processing
16/05: Time Series 18/05: Communication
23/05: Graphs & Network Analysis 25/05: Neural Networks & Deep Learning
30/05: Course Review & Project Presentations 01/06: Project Presentations

Pre-Work

Installation and Setup

Resources

Readings

Optional

You're also more than welcome to do the following if you're keen to get extra advanced for your first class:

Course Project Information

The final project should represent significant original work applying data science techniques to an interesting problem. Final projects are individual attainments, but you should be talking frequently with your instructors and classmates about them.

Address a data-related problem in your professional field or a field you're interested in. Pick a subject that you're passionate about; if you're strongly interested in the subject matter it'll be more fun for you and you'll produce a better project!

Look at past projects on github for some ideas.

Guest Presentations

Over the course of the class we will have guest presenters talking to us about how they run data science teams in the real world. I encourage you to read up on these companies prior to the presentations so you have some background knowledge on these companies and the types of work they do.

Class 1: Introduction

Homework:

Optional:

Class 2: Basics

Extra Reading:

Class 3: Data Visualisation

Extra Reading:

Geographic Visualisation:

Class 4: Linear Regression

Homework:

Class 5: Logistic Regression

Class 6: Model Evaluation

Extra Reading Some good resources on data pre-processing and feature transformation:

Class 7: Regularisation

Homework:

Class 8: Clustering

Pre-Reading:

Class 9: Recommendation Engines

Class 10: Dimensionality Reduction

Online Resources:

Class 11: Decision Trees

Homework:

Class 12: Random Forests and Ensembling

Class 13: Cloud Computing

Pre-Reading:

Class 14: Natural Language Processing

Pre-Class Setup:

Class 15: Time Series

Class 16: Communication

Extra Reading: Here's some of the topics that came up tonight:

Class 17: Graphs and Network Analysis

Extra Materials:

Class 18: Neural Network Analysis and Deep Learning

Extra Materials:

Class 19: Course Review

Where To Now?

Meetups: