Develop a Python project to analyse real world finance scenarios and generate valuable insights by visualising information. The project aims to analyse data from different data sources, manipulate information and visualise to generate insights.
You can use any open-source dataset available online for analytics. Each bullet point for every learning outcome is a milestone to be achieved.
The project should be submitted on the LMS under the Assessments section. You will need to include one ZIP file and a document, as described below.
1. Project Report
Report describing your process, dataset, different sources, graphs and
insights
Includes justification for the use of each learning outcome concept, for
example: Why did you use list over dictionary?
The report should contain between 1,500 and 2,000 words
Please use the template provided (see Assessments section to download)
Submit this document under “Submit Your Written Report Here”
2. Project ZIP File (upload to LMS)
Code and Data
Include your entire Python project along with all the code and data
files and upload as part of your submission
The project should cover all milestones in each learning outcome
to gain full marks (see below)
GitHub repository URL
Create a new repository on GitHub as [UCDPA_yourname]
Keep committing to the repository
Remember to include the URL of your repository at the beginning
of your Project Report document
Submit this file under “Submit Your ZIP File Here”
The goal of the assignment is to demonstrate how you are thinking about putting course concepts and learning into practice to demonstrate the course learning outcomes:
Derive insights into data sources a company can use and how to store that data and understand trading dynamics to risk management systems
Outline fundamentals of Python data structures such as lists and arrays and learn powerful ways to store and manipulate financial data to identify trends
Outline investment strategies to calculate risk based on stock price data and
display this data in easy to read plot
Identify ways to use Python data structures, execution control statements, and DataFrames to manipulate financial data
Derive meaningful financial decisions using Python to compare potential projects and how to make rational, data-driven financial decisions
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