Scientific Python

Wedi cwblhau'r modiwl dylai'r myfyrwyr fedru:

1. Analyse a computational scientific experiment.
2. Demonstrate an ability to write small programs in Python
3. Demonstrate an understanding of the potential biases and sources of error in science.
4. Analyse a data set (process data, apply appropriate tests, calculate summary statistics, plot results).

This module introduces students to the Python programming language and to the use of Python and its library modules for data science. The module also covers the principles of the Scientific Method: the basic structure of a scientific experiment, making and testing hypotheses, and issues such as achieving randomness and sources of sample bias. This in turn leads to using Python's advanced library modules to apply statistics and hypothesis testing to analyse and summarise datasets.

The module introduces the student to Python and uses Python as the programming language to solve various data analysis-related tasks. This leads to the study of more advanced scientific data analysis principles and corresponding programming techniques.

Introduction to the Python Language: types, variables, flow-control statements, loops. The interactive interpreter and evaluation of simple expressions.

Python's basic data structures: Lists, tuples, dictionaries and sets.

Functions: Function definition, calling, parameter passing and value return.

The NumPy module: Data arrays and vectorised operations

Organising code: Creating and using modules. Generating documentation. Handling exceptions.

File handling: Reading and writing text and CSV data files.

Plotting: Manipulating data and plotting results.

The Scientific Method: Structure of a scientific investigation. Hypotheses.

Introduction to modules for scientific data processing.

Randomness: Sources of randomness and random number generators. Random distributions. Random sampling.

Descriptive statistics: how do we summarise data?

Hypothesis testing: determining whether there is enough evidence to draw a conclusion.

Correlation (are there relationships between your data) and Regression (what are those relationships? and can we make predictions?).

Sampling from data.

Application to real data.

Review and revision classes.

Mae'r modiwl hwn yn cydymffurfio a FfCChC Lefel 5