What you will study We’ve taken some insights from our industry partners and have focused on delivering some of the modules that provide the specific knowledge and skills valuable for today’s digital industry.
What you will study
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Software Development for Data Science – 20 Credits This module will focus on the development of software programming skills used for data science using an appropriate programming language such as Python or R. Implementation methods for the foundation topics in data science such as data capture, wrangling, analysis, processing, visualisation and reproducible reports will be covered. You will gain an understanding of the various data science software ecosystems in order to apply statistical data analysis techniques (descriptive and inferential), machine learning and information visualisation techniques. This will be introduced via practical examples using both data simulation and real-world datasets to allow you to make decisions that are supported by data.
Syllabus Data Science Ecosystem: introduction to the software development libraries available for data science vectors, matrices and data representations. Fundamentals of Data Science: software development sequence, selection and iteration in a suitable programming language for data science. Data Preparation: data collection, pre-processing, cleaning, wrangling, transformation and integration. Exploratory Data: analysis basic statistics: population versus sample, mean, median, mode, standard deviation, skewness, variance, correlation, covariance. Hypothesis testing: statistical distributions, standard error and confidence interval, type 1 and 2 errors, p-value, Bayes factor, test for the mean, comparing two means (dependent and independent samples). Linear Regression: simple linear regression, multiple regression, forecasting, classification and clustering. Data Visualisation: visualisation of large and small data sets using open-source software libraries. Learning outcomes On successful completion of this module you should be able to:
Demonstrate a detailed understanding of the available tools within the data science software ecosystem Demonstrate a detailed understanding of software development processes for data science Analyse and evaluate data analysis modelling methods Develop software applications to conduct data analysis and visualisation Critically interpret and evaluate the outputs generated by analysis techniques The module and assessment information described here has been approved in line with the University’s Quality Assurance and Enhancement Process. This allows us to ensure that the courses we offer are high quality and relevant for today’s students. Occasionally we have to make changes to course information after it has been published. For full details of potential changes to courses and how they can affect your application please read the information for students relating to GCU Statement on Changes to Programmes
Course timetable
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Modules will run according to the timetable below:
12 weeks: Software Development for Data Science, 20 creditsTypical module study duration is 12 weeks, final assessment occurs in 13-15 weeks. You will attend an initial comprehensive induction, followed by synchronous and asynchronous teaching one day per week thereafter. Additionally, you should plan self-directed study time to complete module coursework and assessments.
Each 20 credit module represents approximately 200 hours of study.
This will generally constitute, per week for 12 weeks:
Two hours of lectures One hour tutorial Two hours laboratory time You should allow approximately 10 hours per week of self-directed study. However, some modules may require more laboratory time as part of your self-directed study.
Assessment and teaching methods
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These unique short courses are designed to be flexible in a challenging economic environment. Recognising that not all candidates will be in a software role, learning and assessment will combine simulated and real-work challenges. Learning and assessment is your responsibility.
All reading material, tutor discussions, assignment setting, marking and feedback, timelines and key dates are located online in the University’s Virtual Learning Environment (VLE), GCU Learn. You will also have online access to the University’s award-winning library.
Entry requirements
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UK honours degree 2:2 (or equivalent)
We also welcome applicants with relevant industry qualifications/experience within the GCU Recognition of Prior Learning (RPL) Policy.
