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Data Science Part-Time

  • Short course or microcredential

Create robust predictive models with statistics and Python programming. Build confidence and credibility to tackle complex machine learning problems on the job.

Key details

Degree Type
Short course or microcredential
Duration
10 Week Part-Time
Study Mode
In person, Online
Intake Months
Feb, Mar, Apr, May, Jun

About this course

A part-time 10-week, in-person, or remote course for data professionals that teaches them to solve problems using computation that involve large data sets.

Study locations

Sydney

Melbourne

Online

What you will learn

Designed With — and for — Data Professionals

Concentrate on the most important tools for data scientists on the job. GA’s data science advisory board regularly curates the best practices and innovative teaching approaches of our entire expert network to emphasise real-world relevance and meet evolving employer demands. Its work ensures that students graduate ready to tackle the challenges they’ll face in the field.

Harness the Predictive Power of Data

Tailored for students with quantitative or programming backgrounds, this course dives into the essentials of data science: Python programming, exploratory data analysis, data modeling, and machine learning. Get the hands-on experience you need to synthesise extremely large data sets, build predictive models, and tell a compelling story to stakeholders.

Career pathways

In this course, students will learn how to create a basic website with HTML and CSS. Then, students progress to programming with Javascript and JQuery basics. Finally, students build an interactive website that showcases their learning and skill. The course covers such popular front-end development tools as Git, GitHub, text editors, HTML5, CSS3, Javascript and Responsive web design.

Course structure

Programming Basics

Practice the fundamentals of evidence science by practising basic functions in Python.

  • What is data science
  • Your development environment
  • Foundations of Python

Research Design and Exploratory Data Analysis

Practice exploratory data analysis for cleaning and aggregating data and understanding the basic statistical testing values of your data.

  • Exploratory data analysis in pandas
  • Experiments and hypothesis testing
  • Data visualization in python
  • Statistics in python

Create linear and logistic regression models, and branch from statistics into machine learning with kNN and classification. 

  • Linear regression
  • Train-test split
  • KNN and classification
  • Logistic regression

Learn and practice core machine learning models to evaluate complex problems

  • Decision trees and random forests
  • Working with API data
  • Natural language processing (NLP)
  • Time series