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DRPS : Course Catalogue : Centre for Open Learning : Science

Undergraduate Course: Introduction to Quantitative Data Analysis (LLLL07001)

Course Outline
SchoolCentre for Open Learning CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 7 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryWe all use data to make decisions; making reliable decisions is a skill which is set to become increasingly important in 'the age of big data'. This course looks at methods for summarising and understanding large data sets and for drawing conclusions from uncertain and incomplete data. It will equip students with the mathematical and software skills required to confidently embark on further undergraduate study.

This course is suitable for International Foundation Programme Students progressing to undergraduate degrees requiring an understanding of probability and statistics.
Course description 1) Academic Description

This course will demonstrate how mathematics can be applied in real-world situations. In particular, it aims to show students how basic probability and statistics can be used to reliably report and interpret statistical information.

During this course, students will be introduced and supported to learn topics covered including: data collection, summary statistics, probability and probability distributions, hypothesis testing, bivariate data, parameter estimation and goodness of fit. The treatment is mathematically correct but with ample discussion so that understanding is not sacrificed for mathematical rigour or generality. A wide variety of examples, taken from subjects taught in CAHSS, are used to illustrate the various concepts and techniques.

Teaching uses a mixture of group teaching, practical, hands-on activities using software and workshops to consolidate learning. Assessment is based on coursework and a final exam.

On completion of the course students will be equipped with the mathematical and software skills necessary to be able to confidently continue their studies in the various CAHSS progression schools.

2) Outline of Content

The course covers:

the use of data sampling techniques and questionnaires to gather data

the graphical display of univariate and bivariate data

the use of summary statistics to characterise distributions and correlations

elements of basic probability theory

probability distributions including the binomial and normal

hypothesis testing

finding lines of best fit using least squares

estimating population parameters from samples and confidence intervals

the chi-squared distribution and testing goodness of fit, including for contingency tables

student's t-distribution and its application to various tests involving means

the use of software to carry out statistical analyses and create simple Monte Carlo models

3) Student Learning Experience

The course is taught as a series of small group classes. Sessions will be supplemented with further notes and problem sheets used to consolidate learning. Practical data analysis sessions will use Microsoft Excel spreadsheets.

The assessment of learning outcomes will be based on the marks from the problem sheets and a final assessment.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  33
Course Start Lifelong Learning - Session 3
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 24, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 74 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 40% coursework, 4 components worth 10% each

60% final time-limited assessment
Feedback Written feedback on will be provided on coursework.

Verbal feedback will be given in class.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. collect, represent and interpret data using summary statistics
  2. use probability theory to calculate the likelihood of events
  3. use inferential statistics to estimate model parameters and test hypotheses
  4. use software to analyse data and create simple Monte Carlo models
Reading List
There is no core textbook; course materials will be provided on Learn

A resource list will be supplied separately.
Additional Information
Graduate Attributes and Skills The ability to confidently apply mathematics to real-world situations, handle data and assess the reliability of statistical results; expertise in the use of Microsoft Excel.

Digital Literacy; Numeracy; Knowledge Integration and Application; Critical Thinking
Course organiserDr Angus Miller
Course secretaryMs Kameliya Skerleva
Tel: (0131 6)51 1855
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