Environmental Data and Python programming

Code

SE-TMP2

Version

1.0

Offered by

Supply Engineering

ECTS

Prerequisites

Main purpose

The purpose of the course is to give the students skills and competencies in collecting, organizing, analyzing and presenting data to design and optimize technical solutions and processes within the climate and utilities sectors.

Knowledge

After successful completion of the course, the student will be able to:
- Describe concrete examples of typical data sources in climate and supply applications ranging from discrete sampling events to continuous logging.
- Define digital transformation, front end, back end and typical database terms such as record, field, relationship, query, primary and foreign keys.
- Use a Python programming environment such as Visual Studio Code together with its debugger
- Write Python syntax including wrapping code in functions and using libraries
- Methodes and strategies for data management in the utilities sectors.

Skills

After successful completion of the course, the student will be able to:
- Construct a simple normalized data model that conforms to the first, second and third normal forms of database design.
- Explain and exemplify the most used data structures (List & Dictionary) and to identify when to use them
- Extract data from data sources used throughout the course
- Construct plots using Python libraries after filtering the data

Competences

After successful completion of the course, the student will be able to:
- Assess data quality based on quality characteristics/dimensions including accuracy, completeness, consistency, and relevance.
- Filter and analyze data using Python
- Design a solution for a data driven problem
- Discuss and explain the chosen solution
- Discuss applied examples of data utilization and digital transformation in the climate and utilities sector
- Analyze cases on data management in the utility sector including censors, sources, ethics and governance.

Topics

Teaching methods and study activities

- Practical exercises and cases
- Classroom teaching
- Guest lectures

Topics
- Data collection
- Data modelling/database design
- Design of various types of database queries
- Data quality
- Python environment and syntax
- Data structures in Python
- Conditions and iterators
- Functions and libraries
- Data filtering and visualization
- Data management and governance.

Resources

Evaluation

Examination

Exam prerequisites:
None

Type of exam:
Individual oral exam, 20 minutes.

The exam is on the basis of two course assignments found by lot and without preparation.

Ten course assignments must be uploaded in WISEflow approx. one week before the exam.

If the student does not upload the course assignments in WISEflow, the student is offered to solve the course assignments during the exam.

The assessment is based solely on the students oral performance.

Internal assessment.

Tools allowed:
None

Re-exam:
Same as the ordinary exam

Grading criteria

Grading based on the Danish 7-point scale.

Additional information

Responsible

Carsten Nielsen (carn)

Valid from

2/1/2025 12:00 AM

Course type

Keywords

Data collection, backend, data analysis, frontend, data management and governance