- Dr Hong Wei (Associate Professor - University of Reading)
- Dr John Hunt (Chief Operating Officer - Mallon Associates International)
|Delivery Type||Delivery length / details|
|Lecture||20 x 1 Hour Lectures|
|Practical||11 x 1 Hour Practicals|
|Practical||10 x 1 Hour Practicals|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||3 Hours Exam at end of Semester 2 Online coding and data analysis exam.||70%|
|Semester Assessment||2 x In-class test in Semester 1 (Proportion 2 x 15%) Online coding test, in class time.||30%|
|Supplementary Exam||3 Hours Exam at end of Semester 2 Online coding and data analysis exam.||100%|
On successful completion of this module students should be able to:
Plan and execute a computational scientific experiment.
Demonstrate an ability to write small programs in Python
Demonstrate an understanding of the potential biases and sources of error in science.
Analyse a data set (process data, apply appropriate tests, calculate summary statistics, plot results).
Week 1: Introduction, types, variables, if-statements, for- and while- loops. The interpreter, and the evaluation of simple expressions. Editing, saving and loading code.
Week 2: The data structures: Lists and dictionaries.
Week 3: numpy arrays.
Week 4: Functions. Function definition, function calling
Week 5: Organising Python code. Generating documentation (e.g. pydoc), catching and handling exceptions, organising code into modules, executing main (and __main__)
Week 6: Objects and classes. Defining classes, instantiating an object. Inheritance.
Week 7: Working with objects
Week 8: File handling. CSV format.
Week 9: Plotting. Manipulate data and plot results.
Week 10: Review and revision classes
Week 1: The Scientific Method. Structure of a scientific investigation. Hypotheses. Occam's razor. Controls. Correlation vs causation. Falsification. Examples such as intention to treat.
Week 2: Randomness. Random number generators. Random distributions. Latin squares. Controlled and double blind trials.
Week 3: Summary statistics: measures of central tendency and dispersion. Use of scipy.
Week 4: Hypothesis tests. t-tests. Confidence. p-values. Correction of multiple tests. Correlation.
Week 5: Application to real data.
Week 6: Sampling. Bootstrap. Monte Carlo. Biases.
Weeks 7-9: Hot topics in science.
Week 10: Review and Revision classes.
This module introduces students to the Python programming language and more broadly, the Scientific Python Stack. The module will then cover the basic structure of a scientific experiment, hypotheses and testing with illustrated examples of good and bad practice. We’ll discuss the difficulty in achieving randomness, sources of bias during sampling and introduce appropriate statistical testing processes for various types of study.
|Skills Type||Skills details|
|Application of Number||Inherent in subject.|
|Improving own Learning and Performance||From feedback (automatic feedback from computer and in-practical feedback from demonstrators).|
|Information Technology||Inherent in subject.|
|Personal Development and Career planning||No, though the skills in this module are highly in demand from employers.|
|Problem solving||Problems will need to be overcome in order to develop solutions that behave and appear as intended.|
|Research skills||Using a Computer. Searching the language and library documentation.|
|Subject Specific Skills||Programming skills, debugging skills, statistics skills, data analysis skills.|
This module is at CQFW Level 5