BSc Environmental Management with Professional Placement / Course details

Environmental management professionals frequently require the ability to understand and work with quantitative data. This course unit starts by introducing the practical and ethical implications of working with quantitative data. Following this, content provides grounding in different data sources, exploring varied data types and the processes required before any visualisation or analysis can occur. The course unit then explores different analytical methods that can be used to facilitate interpretation and presentation of outputs related to environmental management professions, including inferential statistics and the foundations of basic computer coding

The unit aims to:

• Introduce foundational concepts about data (metadata, ethics, disclosure, anonymity), practical skills and methodologies needed to develop a critical and organised approach to data analytics for environmental management.
• Provide grounding on generating, retrieving, manipulating and visualising quantitative data.
• Develop quantitative data handling skills for use in environmental management.
• Enable students to understand quantitative data to facilitate the use of statistics.
• Introduce a range of basic coding skills and relevant software for data analytics.

• Understanding quantitative data: metadata, ethics, anonymity and disclosure
• Accessing and retrieving secondary data
• Generating quantitative data for environmental management
• Data processing and exploratory data analysis
• Data visualisation
• Using data to generate descriptive statistics
• Interpreting and manipulating data for inferential statistics
• Introduction to basic computer coding relevant to environmental management

Lecture-based sessions: 4 x 1 hour sessions (4 hours)

Core content on ethical implications of working with data and an introduction to understanding and working with quantitative data is taught as an introduction to the course unit (alongside workshops sessions). E-learning content is provided on Blackboard including interactive material using a range of multimedia sources.

Workshop (computer cluster) sessions: 11 x 2 hours (22 hours)

The majority of the taught components of this course unit are taught in the computer cluster so students can apply their knowledge and skills as they are learning. The course unit teaches knowledge of digital methodologies throughout the course unit, including introduction to different software packages, and opportunities for creative visualisation of data.

Directed reading: 6 x 1 hours (6 hours)

Students are encouraged to extend their knowledge of specific research methods and to consider the ethical implications of these, ahead of interactive lecture sessions. Links to readings will be provided through appropriate e-learning tools, e.g. Reading Lists Online.

Assessment and independent learning (68 hours)

Students will be assisted with independent learning through the provision of different multimedia sources available on Blackboard.

• Describe and summarise data using descriptive and inferential statistics.
• Demonstrate data literacy including knowledge of data types, distribution, visualisation and manipulation.

• Explain some of the ethical, scientific and technological issues related to the use of quantitative data for environmental management
• Evaluate the suitability of data for different analyses, including interrogating sources, sampling and techniques for manipulation.

• Retrieve and manipulate quantitative data from a variety of sources for use in built environment research.
• Analyse data, including screening, cleaning and transforming data for use in a range of situations and applications.

• Read and write basic computer code.
• Identify and use appropriate software to perform basic quantitative methods of data analysis to help understand environmental management challenges.

Written or audio feedback via Turnitin within the standard timeframe specified by the University

• Bissett, B.D. (2007) Automated Data Analysis using Excel. Chapman and Hall/CRC.
• Dormann, C. (2020) Environmental Data Analysis: An Introduction with Examples in R. Springer Nature.
• Emetere, M.E. (2022) Numerical Methods in Environmental Data Analysis. Elsevier.
• Ennos, R. (2007) Statistical and Data Handling Skills in Biology. Pearson Education
•  Emetere, M.E. (2022) Numerical Methods in Environmental Data Analysis. Elsevier.
• Harris, R. (2016) Quantitative Geography: The basics, pp.1-328.
• McCormick, K. and Salcedo, J. (2017) SPSS statistics for data analysis and visualization. John Wiley & Sons.
• McKinney, W. (2012) Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc.
• Shukla, S., George, J.P., Tiwari, K. and Kureethara, J.V. (2022) Data Ethics and Challenges. Springer Singapore Pte. Limited.