Computational Data Analysis - 2025 entry

A knowledge of a computing language and how to write programs to solve physics related problems is a valuable transferable skill.  This module teaches the Python programming language, but the principles involved are applicable to almost every procedural programming language.  Python is an interpreted, high-level, general-purpose programming language that is widely used in commercial and academic environments and for scientific research including high level data analysis work.

 

The module is taught through a series of lectures and practical sessions based on Jupyter notebooks.  You will learn the building blocks of the language, and a logical approach to coding, and use these to create your own programs with Physics applications.

Module Specific Skills and Knowledge:

1 Explain and use standard features of the Python programming language including statements, assignments, objects, loops, conditionals and functions

2 Write and modify simple programs in Python

3 Find errors and debug code

4 Write structured code based on short routines with a clear purpose and interfaces that are simple and unambiguous

5 Self-explanatory, self-documenting code using markdown, docstrings and comments

6 select and apply existing tools for scientific programming from modules including Numpy, Scipy, Matplotlib and Astropy, based on the documentation

 

Discipline Specific Skills and Knowledge:

7 Apply logic to the solution of problems

8 Keep proper records of work

9 Apply the Python programming language to simple physical problems including calculations, modelling and data analysis

10 Produce publication-quality plots

11 Present a portfolio of work

 

Personal and Key Transferable/ Employment Skills and Knowledge:

12 Deal with the practicalities of writing a computer program

13 Think and plan in a logical manner

14 Apply a structured approach to problem solving

 

I Introduction to Python: 

 

1. Running interactive Python; loading modules and packages; using Python as a graphical calculator; simple calculations, maths, simple functions and plotting

2. Using Jupyter notebooks with Numpy and Matplotlib

 

II Core Python programming:

 

1. Objects, variables and assignments.    Dynamic 'Duck' typing.   Numerical

datatypes

2. More datatypes: strings, lists, tuples, and dictionaries

3. Control flow I: Conditionals, comparisons and Boolean logic 

4. Control flow II: Loops

5. Functions: keyword and positional arguments, default arguments, *args and **kwargs, docstrings, variable scope

6. Program structure and documentation, error handling, testing and debugging

 

III  Python for labs:

 

1. Numpy arrays and datatypes

2. Using Numpy for reading and writing data; simple statistics; plotting data with errorbars

3. Fitting a straight line with a least-squares fit 

4. Nonlinear least-squares fitting with Scipy

5. Publication-quality plots with Matplotlib:  multiple axes, control of plot elements

 

IV  Python packages and modules:

 

1. How to find out what's available and use the documentation

2. Further examples from Matplotlib e.g. histograms, 2D plots

3. Further examples from Numpy e.g. random numbers, matrices

4. Introduction and examples from Scipy e.g. root finding and numerical integration

5. Introduction and examples from Astropy e.g. reading and displaying FITS images

6. Introduction and examples from pandas, e.g. reading and manipulating tabular data

 

V Advanced Python:

 

1. Handling files and filenames with contexts and ‘os’

2. Classes and objects 

3. Creating a Python program and /or module in an IDE.   if __name_ == "__main__" and command-line arguments

 

VI Projects:

 

1. Programming project based on the Stage 1 Physics course content

 

Re-assessment is not available except when required by referral or deferral.

 

Web-based and electronic resources: 

 

ELE – https://vle.exeter.ac.uk/course/view.php?id=14084

 

Other resources: None