Class Details
Python Programming Course: Fundamentals
Course summary
Python is one of the most in-demand technical skills and one of the easiest programming languages to learn. With a syntax similar to the English language, Python is an ideal choice for beginners. It has a wide range of applications, from web development to data analytics and visualisation.
This course is the first of a two-part Introduction to Python series. We recommend enrolling in both courses to gain entry level skills, however enrolment in both courses is not compulsory.
This course covers the fundamentals of Python syntax and general computer programming. It does not cover data analytics and visualisation with Python - this is covered in Python Programming Course: Data Analytics.
If you are unsure which course level is right for you, please use the CCE Python self-assessment tool.
Aims
The aim of this course is to teach basic programming skills using Python, including variable types, operations, user input/output, logic, loops and functions.
Learning outcomes
By the end of this course, you should be able to:
- install Python and additional packages (via Anaconda)
- understand the difference between Python and Anaconda
- find and read documentation for Python libraries and functions
- use Jupyter Notebook to write and run Python code
- work with basic Python data types (string, float, integer, etc)
- work with basic Python collections (list, dictionary, tuple, etc)
- write Python expressions that involve variables, variable assignment, operators, functions
- understand the basics of object oriented programming
- use conditionals and loops
- fix coding errors
- read csv data files into Python using the Pandas package.
Content
- Introduction to programming
- Introduction to Python, Anaconda and Jupyter Notebook
- Finding, installing and loading Python libraries/packages
- Variables and data types
- Writing expressions
- Collections (lists, tuples, and dictionaries)
- Debugging
- Using logic, conditionals and loops
- Creating functions
- Finding and reading documentation
- Basic exposure to object-oriented programming
- Reading csv files using pandas package
Prerequisites
It is assumed you have computer and data literacy knowledge to the level of performing basic data analysis tasks in Excel, i.e. basic (high school) algebra, percentages, probability, averages. No prior programming experience is required.
Who this course is for
Suitable for professionals, students, academics and members of the public who want to learn programming foundations.
Delivery modes
- Face-to-face, presenter-taught training using your own device
- Online training via the platform Zoom
Materials
Training materials, including a mixture of step-by-step instructions, examples and exercises are provided electronically using Dropbox. These materials will serve as a useful reference when working with Python in future.
What you need to do before the course
To participate in this course, you will need Anaconda Python Version 3.x installed on your device. It can be downloaded free of charge from Anaconda.
Face-to-face classes
Please bring your own laptop with all required software pre-installed.
Online classes
For the best experience, we recommend using a computer with a large monitor or dual screens/devices. Small laptop screens can make it difficult to follow the facilitator’s display while working in the software.
Sessions
Location
Online via Zoom
Other information
Our online via Zoom courses are interactive and delivered in real-time. You will have the chance to ask questions and join in activities, just as you would in any in-person class.
Our facilitators are well versed in Zoom delivery and will skillfully guide you in your online learning journey.
Instructions regarding how to connect to online classes will be sent to participants about 4 days before the class commences.
Meet the facilitators
Tony Arnold
Tony spent the first 12 years of his professional career developing software algorithms for companies including Canon, the CSIRO and Fairlight before transitioning into the transport sector where he has worked on a variety of issues such as freight optimisation and network design. Tony's key areas of expertise are geospatial analysis, software development (C++, Python, Django, R, Javascript), discrete choice modelling and mixed-methods research. He teaches several subjects at the University of Sydney and the Centre for Continuing Education including Python, R and GIS analysis.
Tony leads the Data & Analytics work on walking and cycling at Transport for NSW, and completed a PhD exploring the impacts of automated vehicles on walking and cycling in contrasting environmental contexts. He uses a diverse set of technical skills to tackle challenging problems, and is passionate about encouraging walking and cycling as a means to improve the life of people and the cities they inhabit.
