|Delivery Type||Delivery length / details|
|Lecture||40 x 1 Hour Lectures|
|Practical||5 x 2 Hour Practicals|
|Practical||10 x 1 Hour Practicals|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||3 Hours Exam at the end of Sem 2 Online coding and data analysis exam.||70%|
|Semester Assessment||Worksheets for practicals Assessed worksheets||30%|
|Supplementary Exam||3 Hours Supplementary exam 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).
The module introduces the student to Python and uses Python as programming language to solve various data analysis-related tasks. This leads into studying data analysis as the main topic in the second semester.
This module first introduces students to the Python programming language and to the use of Python and its library modules for processing scientific data. The module then goes on to cover the Scientific Method, the basic structure of a scientific experiment and making and testing hypotheses, with illustrated examples of good and bad practice. Also discussed are difficulties in achieving randomness, sources of bias during sampling and choosing appropriate statistical testing methods for various types of study.
• Introduction to the Python language: types, variables, flow control statements, loops. The interactive interpreter and evaluation of simple expressions.
• Python data structures: Lists, tuples and dictionaries.
• The NumPy module: Data arrays and vectorised operations.
• Functions: Function definition, calling, parameter passing and value return.
• Organising code: Generating documentation. Handling exceptions. Creating and using modules.
• Python objects and classes: Defining and using classes. Working with objects.
• File handling: Reading and writing text and csv data files. Extracting and processing data from web pages.
• Plotting: Manipulating data and plotting results. Use of the matplotlib module.
• Review and revision classes.
• The Scientific Method: Structure of a scientific investigation. Hypotheses. Occam's razor. Controls. Correlation vs. causation. Falsification. Controlled and double blind trials.
• Introduction to the scipy and pandas modules: Working with DataFrames and basic statistics.
• Randomness: Sources of randomness and random number generators. Random distributions. Random sampling.
• Descriptive statistics: Central tendency and spread. Discrete and continuous measurements.
• Hypothesis testing: T-test. Confidence interval. p-value. Chi-square test.
• Sampling: Biases. Bootstrap. Monte Carlo methods.
• Application to real data and hot topics in science.
• Review and revision classes.
|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