FPYT1-MR06 Modelling and Reporting
FPYT1-MR06 Modelling and Reporting
- Course description
- Course codeFPYT1-MR06
- Level of study5.1
- Program of studyPython for Finance
- Credits6
- Course coordinatorTor Kringeland
Modelling and reporting builds on Data manipulation and visualisation and introduces the candidates to statistical modelling of data and reporting modelling results and visualisation in the form of reports. Focus will be put on making lucid reports and communicating results in a meaningful way for the uninitiated. To achieve this, candidates will gain more experience in using the libraries already produced and will have more flexibility in the tools they choose to use for their analysis and reporting.
From previous courses, the candidate is able to collect, manipulate and visualise financial data. This course builds upon the candidates’ knowledge and skill to create model based on collected data and furthermore write reports on the data analysis and modelling which can be consumed by other analysts or stakeholders in the finance industry.
The candidate:
- can update their vocational knowledge of Python libraries and frameworks for reporting
The candidate:
- can apply vocational knowledge to develop interactive widgets, usable outputs and automated reports to present information
- masters sharing and publishing Jupyter Notebook in a variety of formats
- masters relevant Python libraries for machine learning
- masters Python libraries to build, test and use financial models
- can study a situation and identify problems from the finance industry that need data visualisation, modelling and reporting
The candidate:
- understands the ethical principles that apply in the trade/field of work
- has developed an ethical attitude to data visualisation in the finance industry
- can perform data visualisation and modelling tasks based on the needs of financial decision makers
- can build relations with fellow analysts and stakeholders in the finance industry
- can contribute to the development of basic software solutions for the finance industry
- can improve their own productivity by automating routine data visualisation tasks
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.