Intro to Python for Chemical and Pharmaceutical Sciences
| Code | School | Level | Credits | Semesters |
| PHAR2062 | Pharmacy | 2 | 10 | Spring UK |
- Code
- PHAR2062
- School
- Pharmacy
- Level
- 2
- Credits
- 10
- Semesters
- Spring UK
Summary
The module provides an introduction to the Python programming language, with an emphasis on applications relevant to students studying in areas related to chemical sciences (e.g. Chemistry, Pharmaceutical Sciences). Content includes:
• Basic syntax and data types in Python: variables, values, datatypes, simple mathematical operations, comparisons and logical operators;
• data structures: lists and tuples, dictionaries and sets;
• flow control: loops, conditional statements, errors and exceptions;
• program organisation: packages, libraries and modules;
• data manipulation with Numpy;
• graph drawing with Matplotlib;
• chemical data manipulation with RDKit.
Target Students
3rd year BSc (Pharmaceutical Sciences) and MSci (Pharmaceutical Sciences) students from the School of Pharmacy2nd year BSc and MSci students from Chemistry2nd or 3rd year BSc Pharmacology
Classes
- One workshop each week for 11 weeks
Assessment
- 100% Coursework 1: Python coding coursework
Assessed by end of spring semester
Educational Aims
To equip students with a basic competency in Python and ability to write code to solve problems relevant to studies in the chemical and pharmaceutical sciences, particularly related to data manipulation and visualisation.Learning Outcomes
Understand principles of coding using Python, including the ability to perform simple mathematical operations on numerical data and simple analyses and manipulations of textual data.
Be able to choose, construct and manipulate standard Python data structures (such as lists and dictionaries) and standard flow control structures (such as loops and conditional statements) to facilitate the coding of analysis tasks.
Be able to write Python code to read in data from external files, and write analysis results to new files.
Be able to use libraries such as Numpy and Matplotlib to extend the functionality of Python.
Be able to solve a problem related to chemical or pharmaceutical science using a Python-based computational method.
Be able to document code and describe the design and implementation process of an algorithm.