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Python is one of the most popular programming languages for data science, both in academia and industry. It is also a good entry programming language for anyone trying their hand at coding for the first time. This gentle introduction to Python is aimed at all students or staff around campus with little to no prior experience with Python or programming wanting to take that first step.
The course will cover all the basics needed to get future coders started on their journey. By the end of the session, you will have written your first bits of code and be ready to explore what Python can do for your own data or personal projects.
The training is divided into two sessions:
Session 1, 15 October, 9:00 am – 11:00 am
Session 2, 17 October, 11:00 am – 1:00 pm
This will be an interactive session where participants will be expected to follow along with the demonstration, coding throughout the session.
Topics to be covered:
-Why use Python?
-Data types and data structures in Python.
-Conditional statements and loops.
-Writing flexible functions.
-Reading and writing to files.
-Putting it altogether in a simple bit of code.
Intended Audience:
Staff and students with little to no prior experience with Python or programming in general.
Objectives:
-Become more comfortable with Python and programming in general.
-Have a basic understanding of the main data types and structures in Python.
-Have a basic understanding of error messages and how to resolve them.
-Write some code to automate the resolution of a small problem.
-Be able to write a short bit of code to extract or write information from a file.
-Learn how to break a bigger problem into smaller problems through divide and conquer approach.
-Know where to find additional information on Python.
Requirements:
Participants are expected to bring their own laptops so they can follow along with the interactive session.
Pre-course work:
None. There will be a small challenge in between the two sessions for participants to attempt.
Software required:
Python with Python IDLE or similar
