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COMP8042 - Analytical and Scientific Prog

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Title:Analytical and Scientific Prog
Long Title:Analytical and Scientific Programming
Module Code:COMP8042
 
Credits: 5
NFQ Level:Advanced
Field of Study: Computer Science
Valid From: Semester 2 - 2012/13 ( February 2013 )
Module Delivered in 1 programme(s)
Module Coordinator: TIM HORGAN
Module Author: AISLING O DRISCOLL
Module Description: In this module the learner will use a programming language to manipulate, manage and process data using next generation technologies. More specifically, statistical and scientific libraries will be applied to analyse, mine and visualise complex data sets.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Appraise the role and applicability of programming languages within a data analysis environment.
LO2 Apply programming principles to develop and implement a program design from a specification to solve data driven problems.
LO3 Apply programming techniques to transform, manage, mine and visualise data sets.
LO4 Develop and document a program that uses open source scientific and statistical analysis libraries on a chosen data set.
Pre-requisite learning
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 CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
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
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
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).
Data Manipulation and Visualisation
Overview of the standard programming library, basic mathematical libraries, Visualisation of Data (object oriented plotting in 2D and 3D, annotation, styles, legend, subplots), use of enhanced interactive shells for improved debugging, profiling code and interactive plotting.
Numerical Analysis and Scientific Computing:
Use of open source numerical and scientific libraries for: Manipulation of arrays and matrix structures, indexing, slicing, broadcasting, sorting, searching and counting, basic linear algebra, basic Fourier transformation and random number generation routines. Statistical functions, integration, numerical optimization and interpolation tools, spatial computation, advanced mathematical routines.
Advanced Data Analysis
Data gathering, analysis, processing, mining, plotting and storage using open source libraries, Efficient handling of large datasets.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation Laboratory practical/skills evaluations 1,2,3 50.0 Every Second Week
Project Design, build, document, test and deploy a programme to analyse a given data set 2,3,4 50.0 Sem End
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 institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 1.0 Every Week 1.00
Lab Laboratory Practical 3.0 Every Week 3.00
Independent Learning Independent Student Learning 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 1.0 Every Week 1.00
Lab Laboratory practical 3.0 Every Week 3.00
Independent Learning Independent Student Learning 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Eli Bressert, SciPy and NumPy: An Overview for Developers, O' Reilly Media [ISBN: 978-1449305468]
  • Wes McKinney, Python for Data Analysis, O' Reilly Media [ISBN: 978-1449319793]
  • Hans Petter Langtangen,, A Primer on Scientific Programming with Python [ISBN: 9783642183652]
Supplementary Book Resources
  • Philipp K. Janert, A hands-on guide for programmers and data scientists Larger Cover Data Analysis with Open Source Tools, O' Reilly [ISBN: ISBN 10: 0-596-80235-8]
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

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie