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
Andrew Taylor
Andrew has delivered workshops on data strategy, program evaluation and AI applications in social science. His expertise includes machine learning, multimodal AI pipelines and advanced statistical analysis. He has previously taught on topics including restorative justice program evaluation and data-driven decision making.
Andrew worked as a Senior Research Associate at the Vera Institute of Justice, where he led AI-driven data solutions and ETL pipeline development across cloud platforms. He has managed research teams and analytics projects for John Jay College of Criminal Justice and government agencies, with a focus on equity, diversion programs and public health.
Andrew holds a Master of Public Administration from the University of Washington and is a Pi Alpha Alpha Honours inductee. He has completed advanced training in AI and machine learning, including LangChain systems, LLMOps, and prompt engineering, alongside certifications in Principles of Machine Learning and AI strategies from UC Berkeley and Deeplearning.ai.
