Module Details

Module Code: COMP8060
Title: Scientific Prog in Python
Long Title: Scientific Prog in Python
NFQ Level: Advanced
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 2 programme(s)
Module Description: In this module, the learner will use the Python programming language to manipulate, manage and process data. More specifically, statistical and numerical libraries will be applied to analyse and manipulate complex data sets. The learner will also use Linux commands to perform basic system and file operations.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply Linux commands to perform basic system and file operations.
LO2 Implement a program to solve data-driven problems by applying standard programming concepts.
LO3 Utilise standard programming libraries and their associated functionality to perform analysis of datasets.
LO4 Apply programming techniques to clean, transform and query data.
LO5 Illustrate data sets using appropriate visualisation techniques.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Linux
Introduction to Linux file system and commands. Command line overview. Directory navigation, creation of directories and files, manipulating files, permissions, regular expression, process management, editors.
Programming Concepts:
Categories of programming language, their typical application, programming in an analytical and scientific context, Language, Syntax, error checking and debugging, variables and basic data types, lists, dictionaries and sets. Processing data structures: Conditionals and Loops. Efficient code structure: functions, modules, packages and files. Engineering code: objects, classes, and Object Oriented Programming (OOP). Introduction to version control, repositories, branching, commits and merging.
Data Array Manipulation
Overview of standard programming libraries for numerical computation. Creating multi-dimensional arrays. Performing operations such as indexing, slicing, boolean indexing, fancy indexing, building queries, transposing and applying conditional logic to arrays.
Numerical and Scientific Computing:
Use of open source numerical and scientific libraries for: performing mathematical and statistical routines, filtering, sorting, ranking, function application and mapping. Data loading, storage and different file formats. Data wrangling in the form of cleaning, transforming, merging and reshaping data. Data aggregation and grouping operations.
Visualisation
Visualisation and interpretation of data using 2D and 3D plots such as libraries such as Matlplotlib and Seaborn. Line graphs, bar plots, violin plots, KDE plots, heatmaps, etc.
Python Packages
NumPy, SciPy, Matplotlib, Seaborn, Pandas
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Timing Week 3 Learning Outcomes 1
Assessment Description
Laboratory exam that assesses proficiency in applying Linux commands.
Assessment Type Practical/Skills Evaluation % of Total Mark 40
Timing Week 11 Learning Outcomes 2,3,4
Assessment Description
Practical lab assessment focused on building a program that applies a range of programming concepts and libraries to solve data driven problems.
Assessment Type Project % of Total Mark 40
Timing Sem End Learning Outcomes 3,4,5
Assessment Description
Develop a program to solve specific data-driven problems by using numerical libraries and techniques and applying appropriate visualisations.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 2.00 2
Lab Contact Laboratory Practical Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study and develops programming skills Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 2.00 2
Lab Contact Laboratory practical Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study and develops programming skills Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Wes McKinney. (2017), Python for Data Analysis, 2nd. O' Reilly Media, [ISBN: 9781491957660].
  • Joel Grus. (2015), Data Science from Scratch: First Principles with Python, 1st. [ISBN: 9781491901427].
Supplementary Book Resources
  • Philipp K. Janert. A hands-on guide for programmers and data scientists: Data Analysis with Open Source Tools, O' Reilly, [ISBN: 9780596802356].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science & Analytics 1 Mandatory