RE1INTD: Introduction to Data Analysis

Module code: RE1INTD

Module provider: Real Estate and Planning; Henley Business School

Credits: 20

Level: 4

When you'll be taught: Semester 1

Module convenor: Dr Yiquan Gu, email: yiquan.gu@henley.reading.ac.uk

Additional teaching staff 1: Dr Nurjihan Idris, email: nurjihan.idris@henley.edu.my

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 19 November 2024

Overview

Module aims and purpose

The module introduces basic statistical concepts and data analysis. It introduces statistical terminology and basic concepts, allowing students to systematically analyse and describe quantitative data. Using basic statistical tools, students will learn how a research hypothesis is developed and tested. The module focus is on applications in a real estate and planning context using Microsoft Excel, including the integrated use of AI. Therefore, the module further focuses on the use of Excel and on ways to visualise data.  

The module will lay the foundation of analytical methods for real estate and planning practice. It will enable students to interpret, display and explore data, understand the concept of probabilities, and conduct simple hypothesis testing. It will apply these concepts to real estate and real estate development analyses. 

The module lead at the University of Reading Malaysia is Dr Nurjihan Idris.

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

1. Describe data using both tables and a graphic representation 
2. Analyse data and display and explore trends and key summary statistics 
3. Identify different types of probability distributions 
4. Apply simple hypothesis testing 

Module content

• Introduction to different types of data 
• Data description (central tendency, dispersion, correlation etc.) 
• Probability distribution 
• Data visualisations 
• Hypothesis development 
• Hypothesis testing 
• Basic econometric modelling 

Structure

Teaching and learning methods

The module is taught using a combination of lectures, seminars, and practical workshops. Each week, one lecture will be devoted to presenting and discussing key concepts and methods. This will be followed by a seminar where concepts and methods are applied. Students are expected to complete all work sets before their seminars, and they may be called upon to answer questions. In addition, there will be practical workshops guiding students on the use of Excel for data analysis.  

For students studying at our campus in Malaysia: This module may be taught in a different semester and the breakdown of study hours may differ to those set out in the Study Hours table (please refer to the Module Handbook for the correct breakdown). In addition, you will be required to complete an additional 40 hours of study, taking the total number of study hours to 240 for this module. This is to comply with the Malaysian Quality Agency (MQA). 

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative assessment includes a series of seminar exercises where students attempt to solve problems. Participation is essential to acquire full knowledge. Seminars will provide feedback to students who want to improve their knowledge and they should be regarded as an effective feedback system to test abilities and ask for clarifications. 

Reassessment

Additional costs