Data Analytics for PRE

PLAN26041 Data Analytics for PRE 10 Semester 1 Undergraduate Level 2 1 FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors ' European Credit Transfer & Accumulation System Rating : 5.0 Planning and real estate professionals frequently require the ability to understand and work with quantitative data. This course unit will develop skills in understanding data sources, types and analytical methods to facilitate interpretation and presentation of outputs related to the built environment professions. Core content includes:<ul> <li style="margin-left:8px">Understanding quantitative data</li> <li style="margin-left:8px">Accessing and retrieving secondary data</li> <li style="margin-left:8px">Generating quantitative data for planning and real estate</li> <li style="margin-left:8px">Data screening, cleaning and preparation</li> <li style="margin-left:8px">Visualising and exploring data</li> <li style="margin-left:8px">Using data to generate descriptive statistics</li> <li style="margin-left:8px">Interpreting and manipulating data for inferential statistics</li> <li style="margin-left:8px">Data presentation</li> <li style="margin-bottom:11px; margin-left:8px">Data ethics: accuracy, security and risk</li></ul> Planning and real estate professionals frequently require the ability to understand and work with quantitative data. This course unit will develop skills in understanding data sources, types and analytical methods to facilitate interpretation and presentation of outputs related to the built environment professions. Core content includes:<ul> <li style="margin-left:8px">Understanding quantitative data</li> <li style="margin-left:8px">Accessing and retrieving secondary data</li> <li style="margin-left:8px">Generating quantitative data for planning and real estate</li> <li style="margin-left:8px">Data screening, cleaning and preparation</li> <li style="margin-left:8px">Visualising and exploring data</li> <li style="margin-left:8px">Using data to generate descriptive statistics</li> <li style="margin-left:8px">Interpreting and manipulating data for inferential statistics</li> <li style="margin-left:8px">Data presentation</li> <li style="margin-bottom:11px; margin-left:8px">Data ethics: accuracy, security and risk</li></ul> The unit aims to:<ul> <li style="margin-left:8px">Develop quantitative data handling skills for use in planning and real estate</li> <li style="margin-left:8px">Increase confidence in generating, retrieving, manipulating and presenting quantitative data.</li> <li style="margin-left:8px">Enable students to understand quantitative data to facilitate the implementation of descriptive and inferential statistics. </li> <li style="margin-left:8px">Introduce a range of relevant software for data analytics</li> <li style="margin-bottom:11px; margin-left:8px">Provide training in practical skills and methodologies that are needed to develop a critical and organised approach to data analytics for planning and real estate.</li></ul> On completion of this unit successful students will be able to: <ul> <li style="margin-left:8px">Understand how to describe and summarise secondary data using descriptive and inferential statistics.</li> <li style="margin-bottom:11px; margin-left:8px">Demonstrate data literacy including knowledge of data types, distribution, visualisation and manipulation.</li></ul> <ul> <li style="margin-left:8px">Appraise the suitability of data for different analyses, including interrogating sources, sampling and techniques for manipulation.</li> <li style="margin-bottom:11px; margin-left:8px">Appreciate some of the ethical, scientific and technological issues related to the use of quantitative data for planning and real estate.</li></ul> <ul> <li style="margin-left:8px">Retrieve and manipulate quantitative data from a variety of sources for use in built environment research.</li> <li style="margin-bottom:11px; margin-left:8px">Analyse data, including screening, cleaning and transforming data for use in a range of situations and applications.</li></ul> <ul> <li style="margin-bottom:11px; margin-left:8px">Select and use appropriate software to perform basic quantitative methods of data analysis to help understand planning and real estate challenges.</li></ul>  <ul> <li style="margin-left:8px">Understanding quantitative data</li> <li style="margin-left:8px">Accessing and retrieving secondary data</li> <li style="margin-left:8px">Generating quantitative data for planning and real estate</li> <li style="margin-left:8px">Data screening, cleaning and preparation</li> <li style="margin-left:8px">Visualising and exploring data</li> <li style="margin-left:8px">Using data to generate descriptive statistics</li> <li style="margin-left:8px">Interpreting and manipulating data for inferential statistics</li> <li style="margin-left:8px">Data presentation</li> <li style="margin-bottom:11px; margin-left:8px">Data ethics: accuracy, security and risk</li></ul> Computer Cluster Workshops:The majority of the content will be taught in workshops with students supported to access, generate, manipulate, analyse and present data through hands-on and interactive learning. Lecture-Based learning:Four weeks of lectures will provide theoretical and ethical context to data analytics, which will be taught in conjunction with PLAN26011 Data analytics for Environmental Management.Directed Reading:Students will evolve their understanding of core syllabus content through directed reading to expand their understanding of the theory and ethics of data analytics for planning and real estate. Independent study:Students will carry out independent study to develop their confidence in data handling and manipulation. 6 Project output (not diss/n) 100% Formative in class, summative via Blackboard N <ul> <li style="margin-left:8px">Bissett, B.D., 2007. Automated Data Analysis using Excel. Chapman and Hall/CRC.</li> <li style="margin-left:8px">Emetere, M.E., 2022. Numerical Methods in Environmental Data Analysis. Elsevier.</li> <li style="margin-left:8px">Harris, R., 2016. Quantitative geography: The basics. Quantitative Geography, pp.1-328.</li> <li style="margin-left:8px">McCormick, K. and Salcedo, J., 2017. SPSS statistics for data analysis and visualization. John Wiley & Sons.</li> <li style="margin-bottom:11px; margin-left:8px">Shukla, S., George, J.P., Tiwari, K. and Kureethara, J.V., 2022. Data Ethics and Challenges. Springer Singapore Pte. Limited.</li></ul> Lectures 4 Practical classes & workshops 22 174