FPYT1-PF04 Project on financial data analysis
FPYT1-PF04 Project on financial data analysis
- Course description
- Course CodeFPYT1-PF04
- Program of StudyPython for Finance
- Credits4
- Study Plan CoordinatorTor Kringeland
As part of the second project, the candidates will work on a project of their own choosing, related to financial data analysis. Each candidate will provide evidence of their ability to handle large financial datasets, put them into context and extract actionable insights from them. The project opens opportunity for teamwork, real-world client projects or interdisciplinary cooperation across disciplines. The project will be graded A-F.
From previous courses, the candidate is able to collect, manipulate and visualize 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:
- has knowledge of basic concepts, processes and tools for data collection and statistical analyses
- has knowledge of Python libraries used for data manipulation, visualisation and reporting
- has insight into regulations, standards, agreements and quality requirements relevant to data manipulation and reporting
- has insight into data manipulation and data visualisation best practices
- can update their vocational knowledge of Python, third party libraries and frameworks
- understands the importance and value of Python proficiency in data analytics and reporting for the finance industry
The candidate:
- can apply vocational knowledge to identify and analyse problems in the finance industry that can be solved with Python
- can apply vocational knowledge to write Python analyses that process data and generate automated reports
- can apply vocational knowledge to create financial models and simulations in Python
- masters Python libraries for numerical analysis, machine learning and data visualisation
- can find information and material that is relevant to a larger project in the finance industry
- can study a situation in the finance industry and identify data analytics that can be performed with Python scripts
The candidate:
- understands the ethical principles and restrictions associated with analysing and reporting on financial data
- has developed an ethical attitude in relation to financial data analytics
- can carry out work based on the needs of decision makers in the finance industry
- can build relations with other developers in a manner that follows the ethical guidelines, social norms and conventions of online forums and knowledge bases
- can build relations, collaborate and communicate with development teams building financial solutions
- can develop work methods, products or services of relevance to developing Python scripts for data analytics in the finance industry
Digital Learning Resources
The learning management system (LMS) is the primary learning platform where students access most of their course materials. The content is presented in various formats, such as text, images, models, videos or podcasts. Each course follows a progression plan, designed to lead students through weekly modules at their own pace. Exercises and assignments (individual or in groups) are embedded throughout the courses to support continuous practice and assessment of the learning outcomes.
Campus Resources
In addition to the digital learning resources, campus students participate in physical learning activities led by teachers as part of the overall delivery.
Guidance
Guidance and feedback from teachers support students' learning journeys, and may be provided synchronously or asynchronously, individually or in groups, via text, video or in-person feedback.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Semester Project | Grade A-F | Individual | 2 Week(s) |