KL7010 - Principles of Data Science

The module includes a combination of methods to support learning, including lectures and computer based seminars/workshops allowing you to put the theory from lectures into practice. Topics will normally be introduced in lectures and explored through real world examples and practical exercises (helping you develop the required knowledge and understanding) and guided learning activities. You will be encouraged to develop independent self-learning skills to explore further the subject area.

You will be given advice and feedback on your formative assessment (e.g., lab exercises) during the timetabled classes. In addition, the module’s site on the Blackboard Learning Portal will be used to provide extensive supporting material. You will be given detailed feedback on your summative assignment clearly identifying both the weaknesses and strong points of the work.

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

Knowledge & Understanding:
1. Demonstrate critical understanding of foundations and principles of data science
2. Demonstrate deep knowledge of fundamental statistical methods, techniques and applications in data science

Intellectual / Professional skills & abilities:
3. Critically assess, select, and apply data collection and cleaning, visualization, statistical inference, predictive modelling, and decision making for statistical analysis in the context of applied data analysis problems based a critical review of the relevant literature

4. Critically evaluate the choice of data science techniques and tools for particular scenarios as well as evaluate the environmental and societal impact of data science solutions and minimise their adverse impacts

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Build a critical awareness of professional, legal, cultural and ethical issues surrounding analysis, exploration, protection and dissemination of data in the context of your role as a data scientist.

Formative assessment: Exercises provided and carried out within practical classes and workshops will build up to form a basis of the summative assessment. Feedback will be given during these practical classes and workshops and/or through discussions on the Blackboard Learning Portal’s discussion board.

Summative assessments:
• A written assignment (3000 words) – to select, apply and evaluate a choice of data science methods, techniques, and tools on a sizeable dataset (75%) and will test MLOs 1, 2, 3, 4 and 5.
• A 10-minutes presentation (25%) – to focus on and highlight the approach taken and its justification, and key findings derived from the practical work undertaken in the first assessment and will test MLO 4 and 5.

Feedback: You will be given detailed feedback on the assignment clearly identifying both the weaknesses and strong points of the work.

An undergraduate degree with major in computing and information sciences or mathematical and statistical disciplines with applied computing components

None

The aim of this module is to provide you with the data science lifecycle and foundations and principles of data science, and fundamental statistical methods, techniques and applications in data science. It explores key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modelling, and decision-making. It will help you learn about fundamental aspects of probability and statistics to equip you to lead standard data analysis projects in industry and research. The module will teach you how to use probability to deal with uncertainty, ways of visualizing and preparing data for statistical analysis in the context of applied data analysis problems, and hypothesis testing, predictive analysis from the point of view of regression. The module will covers topics such as foundations of Data Science including visualisation, hypothesis testing, prediction and regression; principles and techniques of Data Science including the Data Science Lifecycle, Data Design, Exploratory Data Analysis, Data Cleaning, Probability and Generalization, Linear Regression, Classification and Statistical Inference.

This module includes a combination of methods to support learning, including lectures and lab based seminars/workshops allowing you to put the theory from lectures into practice. Topics will normally be introduced in lectures and explored further through real world examples and practical exercises (helping you develop the knowledge and understanding needed) and guided independent learning activities. You will be encouraged to develop independent learning skills to explore further in the field of data science. Moreover, you will be supported to develop awareness of any health & safety, diversity, inclusion, cultural, societal, environmental and commercial matters, codes of practice and industry standards related to particular data science problem scenarios.