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Introduction
Python is ranked the top programming language both in the 2019 IEEE Spectrum annual ranking of the top programming languages, and in the PYPL (PopularitY of Programming Language) Index. Its popularity is driven partly by the vast number of specialised libraries available for it, and by its simple syntax resulting in high code readability.
This course aims to equip teachers, particularly mathematics and physics teachers, with Python programming skills. Participants will be taught how concepts related to computational thinking using Python, and how to incorporate them into the teaching of mathematics. Examples related to the simultaneous solution of equations, the Newton-Raphson Method, Cramer’s Rule, etc., will be illustrated using a combination of lectures and tutorials.
Course Availability
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Date(s): 17 Feb 2024 to 09 Mar 2024
Time: 17, 24 Feb, 2, 9 Mar 2024 (Every Saturdays : 9:00AM - 12:30PM)
Venue: Virtual (Online)
Registration Closing Date: 03 Feb 2024
At the end of the course, participants will be able to:
- write simple Python programs, and run them using (1) Jupyter Notebooks; Python’s Integrated Development and Learning Environment (IDLE); and (3) the command line
- understand how to import and use the Python standard and external libraries related to mathematics and statistics
- visualise 2D data using matplotlib
- relate computational thinking concepts using Python
Day 1
- Why I chose to teach Python instead of R
- Basic data types in Python: int, float, str and bool
- Python input and output: input(), print() and format()
- Working with numbers
- Using comments
- The Python Standard Library: using the import command
- Generating a random number: randint()
- Using specialised libraries: scipy.stats
- Python relational operators: >, <, ==, !=, >=, <=
- Python logical operators: and, or and not
- Conditional statements
- Iterations using for and while loops
- Working with strings
Day 2
- Iterables: lists, dictionaries, tuples and sets
- Python Membership operators: in, not in
- Iterating through iterables
- Using the Python zip() and enumerate() functions
- Reading and writing files
- Importing data from a CSV file
- Exception handing in Python
- Visualising Data Using Matplotlib
- Where to go from here …
- Getting help
- Python resources
Standard Course Fee: S$1,284.00
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SSG Funding Support |
Course fee |
Course fee payable after SSG funding, if eligible under various schemes |
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BEFORE funding & GST |
AFTER funding & 8% GST |
AFTER funding & 9% GST |
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Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding) |
S$1,200.00 |
S$388.80 |
S$392.40 |
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Enhanced Training Support for SMEs (ETSS) |
S$148.80 |
S$152.40 |
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SCs aged ≥ 40 years old |
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• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.
Note: Course fee payment made before 1 Jan 2024 will be subject to GST at 8%, and payment made on or after 1 Jan 2024 will be subject to GST at 9%.
