Module Details
Module Code: |
PROG C9001 |
Full Title:
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Programming for Data Analytics
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Valid From:: |
Semester 1 - 2019/20 ( June 2019 ) |
Language of Instruction: | English |
Module Owner:: |
John Loane
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Module Description: |
This module will teach students about data structures and programming techniques which will allow them to gather, manipulate, store and graph data sets.
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Module Learning Outcome |
On successful completion of this module the learner will be able to: |
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Module Learning Outcome Description |
MLO1 |
Analyse and evaluate the effectiveness of programming technologies for data analysis. |
MLO2 |
Assess the most appropriate data structure to store data sets. |
MLO3 |
Review and select libraries based on the processing of datasets. |
MLO4 |
Design and develop programs to scrap data from the web. |
MLO5 |
Design and prepare datasets for consumption over computer networks. |
MLO6 |
Design and develop RESTful APIs. |
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 DkIT 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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No recommendations listed |
Module Indicative Content |
Learning Python
Installing, Whitespace, Basic constructs, Functions, Modules, Packages, Third-party libraries.
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Working with in-memory data
Ordered/unordered data, lists, tuples, dictionaries, sets.
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Working with persistent data
TXT, CSV, Pickles, Binaries, JSON, XLSX, Local Databases.
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Manipulating data
Curation, Sorting, Searching, Transforming, Mapping, Filtering, Comprehensions.
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Working with web data
Scraping, HTML, XML, NLTK.
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Working with large numerical datasets
Numpy and Scipy.
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Working with data frames, time series, financial and economic data
Pandas.
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Producing graphs and plots from your data
Matplotlib, Jupyter notebooks, Bokeh.
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Working in the cloud
Accessing datasets via a REST based API and publishing data programmatically on the web.
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Other programming technologies
R
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Module Assessment
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Assessment Breakdown | % |
Course Work | 100.00% |
Module Special Regulation |
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AssessmentsFull Time On Campus
Part Time On Campus
Reassessment Requirement |
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.
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DKIT reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time On Campus |
Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Practical |
Contact |
Practical lab session |
Every Week |
5.00 |
5 |
Directed Reading |
Non Contact |
Reading lecturer recommended texts |
Every Week |
3.00 |
3 |
Independent Study |
Non Contact |
Trying practical tasks |
Every Week |
8.00 |
8 |
Total Weekly Learner Workload |
16.00 |
Total Weekly Contact Hours |
5.00 |
Workload: Part Time On Campus |
Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Practical |
Contact |
Practical lab session |
Every Week |
5.00 |
5 |
Directed Reading |
Non Contact |
Reading lecturer recommended texts |
Every Week |
3.00 |
3 |
Independent Study |
Non Contact |
Trying practical tasks |
Every Week |
8.00 |
8 |
Total Weekly Learner Workload |
16.00 |
Total Weekly Contact Hours |
5.00 |
Module Resources
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Recommended Book Resources |
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Grus, J.. (2015), Data Science From Scratch, 1. O'Reilly Media.
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Dorian Pyle. (1999), Data Preparation for Data Mining, Morgan Kaufman.
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McKinney W.. (2013), Python for Data Analysis, 1. O'Reilly Media.
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Lawson R.. (2015), Web scraping with Python, Packt.
| Recommended Article/Paper Resources |
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CODATA Data Science Journal,
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JDS Journal of Data Science,
| Other Resources |
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Website:, PyData,
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Website:, The R Project for Statistical Computing,
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Website:, Data Show Podcast,
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Website:, Python Data Analysis Library,
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Website:, Matplotlib Visualization,
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Website:, Data Carpentry,
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