Undergraduate Course: Introduction to Quantitative Data Analysis (LLLL07001)
|School||Centre for Open Learning
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 7 (Year 1 Undergraduate)
||Availability||Not available to visiting students
|Summary||We 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.
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
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)
||Other requirements|| None
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
||Lifelong Learning - Session 3
|Learning and Teaching activities (Further Info)
Lecture Hours 24,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||40% coursework, 4 components worth 10% each
60% final time-limited assessment
||Written feedback on will be provided on coursework.
Verbal feedback will be given in class.
|No Exam Information
On completion of this course, the student will be able to:
- collect, represent and interpret data using summary statistics
- use probability theory to calculate the likelihood of events
- use inferential statistics to estimate model parameters and test hypotheses
- use software to analyse data and create simple Monte Carlo models
|There is no core textbook; course materials will be provided on Learn |
A resource list will be supplied separately.
|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 organiser||Dr Angus Miller
|Course secretary||Ms Kameliya Skerleva
Tel: (0131 6)51 1855