Module FOUD0049: Concepts, Methods and Theories in Data Science

Department: Foundation Year (Durham)

FOUD0049: Concepts, Methods and Theories in Data Science

Prerequisites

• None

Corequisites

• Mathematics 2 or Mathematics 3

Excluded Combination of Modules

• Mathematics 1

Aims

• Programme Aims: 
• Foundation students have 3 or 4 core components to their programme, depending on route. The CMT modules are designed to introduce students to concepts, methods and theories within the student’s chosen discipline, and provide a lens through which students engage with knowledge and knowledge creation in their chosen discipline. Meanwhile the Scholarship in Higher Education (SHE) module provides the tool-kit for their engagement and communication of knowledge; whereas the Advanced Scholarship in Higher Education module provides an iterative experience of bringing toolkit and lens together to provide students with the opportunity to actively engage in the process of knowledge generation and communication by completing a research project within the student’s chosen discipline. All students apart from Arts & Humanities also have a maths component. 
• This module contributes to the overall aims of the Foundation Programme, which are aligned to FHEQ level four descriptors. By the end of the programme, students will have demonstrated 
• knowledge of the underlying concepts and principles associated with their area(s) of study, and an ability to evaluate and interpret these within the context of that area of study 
• an ability to present, evaluate and interpret qualitative and quantitative data, in order to develop lines of argument and make sound judgements in accordance with basic theories and concepts of their subject(s) of study. 
• evaluate the appropriateness of different approaches to solving problems related to their area(s) of study and/or work  
• communicate the results of their study/work accurately and reliably, and with structured and coherent arguments  
• undertake further training and develop new skills within a structured and managed environment.  
• the qualities and transferable skills necessary for employment requiring the exercise of some personal responsibility. 
• Module Aims:
• To introduce a range of foundation mathematics skills in operating with datasets applied in a range of degree progression routes.
• To introduce statistical methods to represent, analyse, and interpret statistical data.
• To introduce logical thinking by description, analysis, deduction, and evaluation of real-life data.
• To introduce the ability to communicate work successfully.

Content

• Descriptive statistical analyses.
• Probability
• Inferential statistics (including confidence intervals and hypothesis tests)
• Large data analysis using appropriate software

Learning Outcomes

• By the end of the module students will have demonstrated the knowledge of:
• 1. relevant subject concepts and notations;
• 2. relevant statistical methods for problem solving equations;
• 3. relevant statistical vocabulary.

• By the end of the module students will have demonstrated that they can:
• 1. use a range of relevant statistics concepts in response to specific assessment tasks and maths problems;
• 2. use relevant statistics methods in response to specific assessment tasks and maths problems;
• 3. use a range of relevant statistical vocabulary in response to specific assessment tasks.

• By the end of the module students will have demonstrated that they can:
• 1. demonstrate critical thinking;
• 2. demonstrate logical reasoning through clear presentation of work;
• 3. demonstrate effective academic communication using appropriate subject specific vocabulary.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• This module will be delivered using seminars on a weekly basis. Students will be taught statistical concepts and skills, and then challenged to apply them in a variety of contextual tasks that are designed to lead to achieving the module outcomes. The teaching allows for an interactive teaching/learning style which will encompass some lecture-style presentations by the teacher. These are supplemented by self-access materials, such as introductory videos or readings, alongside weekly tasks to support tutorials which are posted on the VLE.
• Summative Assessment: Assessments within this module are designed to provide opportunities to engage in an iterative process to develop students’ epistemological maturity, self-regulation, and academic report writing and academic communication skills.
• The Statistics report allows students to demonstrate effective academic communication and logical reasoning through clear presentation of work. The Test primary function is to allow students to demonstrate mastering the skills of selecting and applying appropriate statistics knowledge and techniques in solving statistical problems. The secondary focus is on the key skills of academic communication under timed conditions (as they are likely to experience in their subsequent years of study). It is a three-hour test, however, the number of questions is written to allow students to complete them within two hours. This lessens the time constraint, hence reduces maths and test anxiety.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

A range of formative tasks including class practice and regular check point are used weekly to help students work towards module outcomes and to iteratively build competency towards each respective summative assessment.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University