Module Details
| Module Code: |
COMP9073 |
| Title: |
Automation with Python
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Long Title:
|
Automation with Python
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| NFQ Level: |
Expert |
| Valid From: |
Semester 1 - 2019/20 ( September 2019 ) |
| Field of Study: |
4811 - Computer Science
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| Module Description: |
In this module, the learner will be provided with the skills to write Python programs that are able to accomplish real and practical automation and robotics tasks. The learner will also be equipped with the skills to effectively use specific Python libraries such as OpenCV, RoboDK and Robolink.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Apply Python syntax to write scripts to perform specific tasks. |
| LO2 |
Simulate an automation task with a Python program. |
| LO3 |
Apply Python programs to perform a real world automation task or problem. |
| LO4 |
Employ appropriate visualization techniques for depicting results. |
| LO5 |
Critically evaluate results of the automation and robotics tasks. |
| 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).
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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.
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| 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 |
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Introduction
Overview of the terminology and applications of python programming in the area of automation and Data analysis. Importance of data analytics in industry.
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Python Syntax
Learn how to use Python both interactively and through a script. Create your first programs and acquaint yourself with Python's basic data types. Learn how to use python variables, comments, functions, modules and libraries.
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Data manipulation with Python
Importing data from different sources in different formats. Applying data manipulation techniques such as reshaping, pivoting, array-based indexing, joining, cleaning and munging, grouping, aggregation.
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Visualisation
Overview of a range of visualization techniques such as histograms, scatter plots, heatmaps, clustered matrices, boxplots, regression plots.
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Automation task Simulation
Create simulations with robots and mechanisms as well as generating brand-specific programs for robots using automation libraries such as RoboDK or Dronekit.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| 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 |
Lecture delivering theory underpinning learning outcomes. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Practical computer-based lab supporting learning outcomes. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Independent Student Learning. |
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 |
Lecture delivering theory underpinning learning outcomes. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Practical computer-based lab supporting learning outcomes. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Independent Student Learning. |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Lentin Joseph. (2018), Learning Robotics Using Python, 2nd Ed. Packt Publishing, p.280, [ISBN: 9781788623315].
| | Supplementary Book Resources |
|---|
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John V. Guttag. (2016), Introduction to Computation and Programming Using Python: With Application to Understanding Data, 2nd Ed. MIT Press, [ISBN: 9780262529624].
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Eric Matthes. (2015), Python Crash Course: A Hands-On, Project-Based Introduction to Programming, No Starch Press, [ISBN: 1593276036].
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David Beazley and Brian K. Jones. (2013), Python Cookbook: Recipes for Mastering Python 3, O'Reilly, [ISBN: 9781449340377].
| | Recommended Article/Paper Resources |
|---|
-
Atsushi Sakai, Daniel Ingram, Joseph
Dinius, Karan Chawla, Antonin Raffin and
Alexis Paques. (2018), PythonRobotics: a Python code collection
of robotics algorithms, ArXiv 2018, 31 AUG 2018.
| | Other Resources |
|---|
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Website, RoboDK,
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Website, Dronekit,
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