FPYT1-MV06 Data Manipulation and Visualisation
FPYT1-MV06 Data Manipulation and Visualisation
- Course description
- Course CodeFPYT1-MV06
- Program of StudyPython for Finance
- Credits6
- Study Plan CoordinatorTor Kringeland
Data manipulation and visualisation expand on the first two courses by guiding the candidates to use their knowledge of Python to process and analyse data. Principles of data manipulation (including data cleaning and transformation) and visualisation are studied and practised in detail. The candidates will learn about the general methods and principles of data science and how to use different Python libraries to process and gain insight into data. The candidates will learn some basic statistical methods and principles in the process. The focus will be on financial data sets.
After the candidates have gained proficiency in Python programming and completed an independent project to solidify their skills, the program structure transitions to a focus on data manipulation and visualisation, with a particular focus on financial data. The candidates will be able to identify issues in the finance industry for which data manipulation and visualisation can help solve.
The candidate:
- has knowledge of the steps in the data analysis life cycle to extract useful information from raw data
- has knowledge of basic descriptive and inferential statistical methods
- has knowledge of Python libraries suitable to financial analytics
- has knowledge of Python libraries suitable to graphic representation of data
- has insight into the regulations, standards, agreements and quality requirements regarding data collection and analysis
- has insight into the regulations, standards, agreements and quality requirements regarding graphing
- can update their vocational knowledge of Python libraries and frameworks for data manipulation and visualisation
- understands the importance of data analytics and visualisation to support financial decision making
The candidate:
- can apply vocational knowledge of the data analysis in performing financial analytics
- masters relevant Python libraries for numerical analysis, data preparation, scientific computing, statistics and for creating and formatting plots
- masters importing data from external file sources
- can find information and material that is relevant to data collection, analysis and visualisation with Python libraries
- can study a situation and identify problems from the finance industry that need data manipulation
The candidate:
- understands the ethical principles that apply in financial data analytics
- has developed an ethical attitude to data manipulation in the finance industry
- can perform data analysis coding tasks based on the needs of financial decision makers
- can build relations with fellow developers 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 manipulation tasks
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 |
|---|---|---|---|
Course Assignment | Pass / Fail | Individual | 1 Week(s) |