MA4058 - Data Analysis (2023/24)
| Module specification | Module approved to run in 2023/24 | ||||||||||||
| Module title | Data Analysis | ||||||||||||
| Module level | Certificate (04) | ||||||||||||
| Credit rating for module | 15 | ||||||||||||
| School | School of Computing and Digital Media | ||||||||||||
| Total study hours | 150 | ||||||||||||
|
|||||||||||||
| Assessment components |
|
||||||||||||
| Running in 2023/24(Please note that module timeslots are subject to change) |
|
Module summary
This module introduces methods of analysing data using appropriate statistical software. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about data are also introduced. A selection of suitable software (e.g. Excel, SPSS, R) will enable students to analyse data in order to make informed decisions.
Prior learning requirements
None.
Available for Study Abroad? NO
Syllabus
Descriptive Statistics (measures of central tendency and variability).
Discrete and continuous probability distributions.
Inferential Statistics: Estimation and Hypothesis testing.
Hypothesis testing for qualitative and quantitative data.
Goodness of fit and test of significance.
Balance of independent study and scheduled teaching activity
Students’ learning is supported by blended learning via face-to-face learning activities that include lectures, seminars, individual and group-based case studies investigations, and real data analysis resourced by Weblearn. There is full provision of documents related to the module in electronic format that can be accessed by students all the time. The documents include lecture notes, slides, guidance to packages, exercises, self-assessed tests, data for analysis and feedback. Students are motivated to analyse real data sets made available to them.
Learning outcomes
On successful completion of this module, students should be able to:
LO 1. Classify data and employ basic statistical methods to get insight from data.
LO 2. Identify different types of data, summarise and present data; use sample data to make inference about population parameters.
LO 3. Understand the ideas of probability and the basic probability distributions, discrete and continuous.
LO 4. Use an appropriate software package (such as Excel, SPSS) to investigate and interpret data and fit statistical models to a set of data.
Assessment strategy
The assessment consists of an in-class test and a coursework.
In the in-class test, students are tested on their understanding of descriptive statistics, probability distributions and introduction to SPSS. The coursework examines the understanding gained by the students regarding probability distributions in Excel and inferential statistics in SPSS. Students will use Excel and SPSS to analyse data involving estimation and hypothesis testing.
Formative assessments are given every week during the tutorial/seminar sessions while summative assessment feedback will be made available at the end of each test.
Bibliography
https://rl.talis.com/3/londonmet/lists/97D559DA-8655-0F16-74C1-1DCD9209EE11.html?lang=en-GB
Core Text:
Veaux, R. and Vellman,P. (2004) Intro Stats, Addison Wesley ISBN 0-201-70910-4.
Recommended Readings:
McClave, T. and Sincich, T. (2003) A First Course in Statistics, Prentice Hall.
Field, A. (2009) Discovering statistics using SPSS. 3rd edition. Sage.
Daly, F. Hand, D., Jones, Lunn, M. and McConway, K. (1995) Elements of Statistics, Addison Wesley, ISBN 0-201-42278-6.