Unit information: Environmental Data Science in 2028/29

Please note: Programme and unit information may change as the relevant academic field develops. We may also make changes to the structure of programmes and assessments to improve the student experience.

Unit name Environmental Data Science
Unit code CADEM0005
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Rosolem
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

Basic knowledge of hydrology: Water Engineering (CENG20021) or Introduction to Hydrology (CADEM0004) or equivalent

Basic knowledge of Python: Engineering by Investigation (MENG10005) or Introduction to Hydrology (CADEM0004) or equivalent

Units you must take alongside this one (co-requisite units)

Basic knowledge of hydrology: Water Engineering (CENG20021) or Introduction to Hydrology (CADEM0004) or equivalent

Basic knowledge of Python: Engineering by Investigation (MENG10005) or Introduction to Hydrology (CADEM0004) or equivalent

Units you may not take alongside this one

None

School/department School of Civil, Aerospace and Design Engineering
Faculty Faculty of Engineering

Unit Information

Why is this unit important?

This unit is about understanding the importance of hydrological and environmental measurements. Key understanding of the hydrological cycle comes with good availability and use of data from monitoring stations. In addition, modelling applications often require datasets from observations for setting up experiments and evaluation. Students will be introduced to different monitoring techniques of various components of the water cycle, including in situ based and satellite remote sensing. They will also learn basic statistics and other data sciences tools applied to a range of environmental scenarios.

How does this unit fit into your programme of study?

The MSc Water and Environmental Management (WEM) programme has been designed around five core subjects: fundamental hydrology, data science, environmental modelling, environmental management, and decision making.

This optional unit covers the topics of the data science subject and expands the hydrological processes covered in fundamental hydrology elsewhere, focusing here on measurement technologies, monitoring capabilities, and hands-on application of statistics and other data analysis tools. The unit also familiarises students with relevant observations and datasets that can potentially be used together with hydrological and environmental models.

Your learning on this unit

An overview of content

The first part of this unit focuses on introducing the students to the various measurement techniques for hydrology and environmental applications. The students will be exposed to different examples of applications and will be able to recognise the opportunities and challenges of different monitoring technologies, including both in situ and satellite remote sensing techniques.

The second part of this unit allows the students to develop practical skills such as data analysis. The students will be able to learn new data science tools applied to real global challenges (e.g., climate change, water resources management, environmental pollution).

How will students, personally, be different as a result of the unit

A key aspect of water and environmental management is access to good quality data, whether for pure data science applications or for use with environmental models. The students will learn the opportunities and challenges of measuring key hydrological and environmental variables tailored with practical examples from real world scenarios. They will be trained to use the relevant tools for data analysis.

Learning Outcomes

  1. Explain the relevant applications and challenges of using different monitoring techniques for water resources and environmental management
  2. Evaluate readily available data resources in a range of practical real-world hydrological and environmental scenarios and applications
  3. Analyse and manipulate datasets using data science methods and tools

How you will learn

The students will learn primarily from the lectures with some which contain practical examples of applications for further support. Some datasets will be provided to the students initially, while others will require the students to obtain from the web. This gives the student the ability to critically search for relevant datasets depending on the application. Whenever appropriate, practical sessions will be offered to introduce new data analysis tools. Additionally, a series of articles will be provided discussing either broader context or technical aspects of a particular topic of the unit.

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

The students will be given small tasks to complete throughout the unit. General feedback to the cohort will help the students prepare for the summative tasks.

Tasks which count towards your unit mark (summative):

The unit will be assessed by a report corresponding to 100% of the unit assessment to be undertaken individually (ILOs 1-3).

When assessment does not go to plan

The reassessment for the unit will be a piece of individual coursework which tests all the unit learning outcomes.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. CADEM0005).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.