module specification

The Data Science, Research and Analysis module provides a fundamental grounding of basic knowledge of data science and computer software to facilitate the collection, analysis and presentation of accounting data. The module prepares learners for the accounting & finance and related professions. This will be achieved through learning relevant academic and practical skills which will enable learners to succeed academically and develop key workplace research skills. It also provides skills to analyse data, interpret and communicate qualitative/quantitative results in the form of information. Furthermore, the module deals with the collection, analysis, interpretation and presentation of accounting and financial data through measuring changes and associations of variables.

This module also provides basic skills in gathering and understanding of financial and non-financial data/information to develop complete knowledge of the client business and the environment in which it operates. It develops students’ basic skills and understanding to help them prepare business plans and advise on the actions to implement these plans. The skills developed through the understanding of data science and researching to provide solutions of issues raised in the accounting and finance sectors.

Part-1

1. Data Science, Digital & Technology (big data and data analytics)
- Data science and Artificial Intelligence: current developments and challenges; adding value into businesses & organisations; the role of AI in data science.
- The use of technologies to visualise data clearly and effectively,
-  Data science: sources & types of data; accessing big data and presentation for business & organisations using scientific methods.

2. Data Analysis and Statistical Techniques (analysing and evaluating data using appropriate technologies and tools)
- Sampling methods; forecasting techniques; summarising and analysing data; spreadsheet.

3. Basic skills set for numeracy leading to the ability to deal with more abstract mathematical concepts.

Part-2

4. Research skills: identifying current issues in accounting; define the problem; identify gaps; critical analysis; the use of primary data; survey tools.

5. Research skills and conducting basic research in accounting & finance

- use of research skills related database

6. Ethical judgement to the use of data and data technology

Learning Outcomes LO 1 - 3

Lectures and Tutorials:

The module will consist of lectures to introduce new topics and themes relevant to the accounting, economics and other related courses. The seminar sessions will provide the opportunity to practice the content covered in the lecture with and without the use of a computer software. The practice will help the students to understand the applied aspects of each topic covered in the lecture, particularly the quantitative analysis relevant to the subject areas. Learning will be supported by a developed Weblearn site, which students will be required to access on a frequent basis to prepare for lectures and seminars.

On successful completion of this module, students will be able to:

LO1: Explain the use of data science and how information technology can be used to inform and implement organisation strategy,

LO2: Identify and analyse data using mathematical & statistical techniques embedded in information technology for planning, decision-making, performance evaluation and control.

LO3: Apply their understanding of data and business analytics to research current issues in accounting using digital technologies.

This module will have two assessment components. The first assessment comprises two parts:  coursework not exceeding 250 words that comprises questions on data science that includes mathematical and statistical applications. For this assessment the students are required to demonstrate knowledge and understanding of the applications of quantitative methods learned in the first section of the module.  Students will be asked to reflect critically on the task in 500 words.  This first assessment, with a 30% assessment weighting, will help to monitor students learning progress before moving to the second section of the module. 

The second assessment, weighting 70%, is in the form of an unseen exam for 1 hour that will focus on a scenario based quantitative and qualitative research methods questions.

The Data Science, R.Reading List Talis Link:

https://bblearn.londonmet.ac.uk/webapps/blackboard/content/launchLink.jsp?course_id=_43565_1&tool_id=_2924_1&tool_type=TOOL&mode=cpview&mode=reset

 Core Text:

Thornhill, Lewis & Saunders, Research Methods for Business Students, Pearson 8/E, 2019. ISBN-10: 1292208783 • ISBN-13: 9781292208787

Oakshott L, Essential Quantitative Methods for Business, Management and Finance, 978-1137518552, Palgrave MacMillan, 6th Ed, 2016.

Swift L, Piff S, Essential Quantitative Methods: For Business, Management and Finance, 978-1137376558, Palgrave MacMillan, 4th Edition, 2014

Other Texts:

Cottrell, The Study Skills Handbook, Palgrave, 2013.

Periodicals:

• Financial times

• The Wall street Journal Europe

Websites:

http://www.accountingtoday.com

http://www.restore.ac.uk/srme/www/fac/soc/wie/research-new/srme/modules/videos/index.html

http://www.bbc.co.uk/learning/subjects/maths.shtml

Journals:

Journal  of information systems

The Accounting review

Issues in accounting Education

Electronic Databases:

• Science Direct

• FAME (UK Companies)

• Education Research Complete