This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Postgraduate Handbook 2022-2023 (archived)

Module MATH42715: Introduction to Statistics for Data Science

Department: Mathematical Sciences

MATH42715: Introduction to Statistics for Data Science

Type	Level	Credits	Availability	Module Cap	Location
Tied	4	15	Available in 2022/2023	None.	Durham

Tied To

Pre-requisites

None

Corequisites

None

Excluded Combinations

None

Aims

To introduce the fundamentals of statistics needed for data science.

Content

Exploratory statistics: descriptive statistics, data types and data collection.
Statistical inference: sampling distributions, estimation, and hypothesis testing.
Linear models: Assumptions, estimation, inference, prediction.
Classification and clustering methods.
Resampling and validation.

Learning Outcomes

Subject-specific Knowledge

By the end of the module students will have a knowledge and understanding of fundamental statistics concepts in the following areas:
Descriptive and Inferential Statistics.
Linear models and correlation.
Classification and clustering methods.
Resampling and validation.
Statistical computing with R.

Subject-specific Skills

Students will have basic statistical skills in the following areas: modelling, computation.

Key Skills

Students will have skills in the following areas: synthesis of data and data analysis, critical and analytical thinking, statistical modelling, computer skills.

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

This module will be delivered by the Department of Mathematical Sciences.
Workshops describe theory and its application to concrete examples, concretize understanding via the application of calculational and computational methods to more complex problems, as well as providing feedback and encouraging active engagement via discussion and groupwork.
All lectures will be recorded in line with the University's Lecture Capturre Policy and will be available for the duration of the programme.
Online resources support learning and normally include: video content, directed reading, reflection through activities, opportunities for self-assessment, and peer-to-peer learning within a tutor-facilitated discussion board.
Surgeries give students the chance to pose personalized questions on both theory and practice.
Summative assignments are designed to test the acquisition and articulation of knowledge and critical understanding, and skills of implementation and interpretation of calculational and computational methods as applied to both synthetic and real problems.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total Hours
Workshops in hybrid mode (a combination of live lectures, computer practicals, problem classes, and tutorials)	12	3 times per week (Term 1, weeks 6-9)	2 hours	24
Lectures	8	2 times per week (Term 1, weeks 6-9)	1 hour	8
Surgeries	12	3 times per week (Term 1, weeks 6-9)	1 hour	23
Preparation, exercises, and reading				106
tOTAL				150

Summative Assessment

Component	Component Weighting	Element	Element Weighting
Coursework	10%	Quizzes (e-assessments)	100%
Assignment 1	30%	Assignment	100%
Assignment 2	60%	Assignment	100%

Formative Assessment

None

Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.