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Unit information: Advanced Programming in 2020/21

Unit name Advanced Programming
Unit code BIOLM0035
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Matt Williams
Open unit status Not open

Scientific programming, Statistics and R



School/department School of Biological Sciences
Faculty Faculty of Life Sciences


This a new unit that will introduce students to the theoretical and practical aspects of programming applied to scientific data. These include reading in data, processing it to answer simple questions and advanced data analysis techniques.

The aim of this unit will be to:

  1. Provide students with a detailed understanding of the concepts behind designing and writing their own programming scripts.
  2. Provide students with an understanding of how information can be extracted from their data.

Intended learning outcomes

The Learning Outcomes (LOs) for this unit are:

A: Knowledge and Understanding:

  1. to understand how to gather and read in data into a computer program
  2. to develop knowledge on the theoretical aspects behind the design of a program algorithm
  3. to understand how to analyse data in a repeatable way.

B: Intellectual Skills/Attributes:

  1. to understand which data analysis techniques are appropriate where
  2. to design computer algorithms and critically assess their suitability in different scenarios
  3. to design software which is sustainable and sharable.

C: Other Skills /Attributes (Practical/Professional/Transferable):

  1. to acquire competency with the Python programming language
  2. to acquire competency with different data analysis packages and how that data can be visualised.

Teaching details

The unit will be delivered through a mixture of short lectures followed by individual exercises with computers. Blackboard will be used engage students with the unit content.

Assessment Details

A summative computer assessment will consist of a final computer task integrating all the learning objectives.

Reading and References

  1. Hands-on Machine Learning with 'Scikit'-Learn, 'Keras', and TensorFlow, 2nd Edition by Aurélien Géron
  1. Python for Data Analysis, 2e by Wes Mckinney