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Archive Module Description

Department: Mathematical Sciences

MATH42715: Introduction to Statistics for Data Science

Type Level Credits Availability Tied 4 15 Available in 2021/22
Tied to G5K823 Data Science G5K923 Data Science (Digital Humanities)

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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.
• 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 (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: Coursework Component Weighting: 25%
Element Length / duration Element Weighting Resit Opportunity
Quizzes (e-assessments) 100%
Component: Assignment 1 Component Weighting: 25%
Element Length / duration Element Weighting Resit Opportunity
Assignment 100%
Component: Assignment 2 Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Assignment 100%

Formative Assessment:

None

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