Written Assignment

How-To Guide

The Written Assignment represents 100% of the overall course grade.

Instructions

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:

1. Derive insights into data sources a company can use and how to store that data and understand trading dynamics to risk management systems
2. Outline fundamentals of Python data structures such as lists and arrays and learn powerful ways to store and manipulate financial data to identify trends
3. Outline investment strategies to calculate risk based on stock price data and display this data in easy to read plot
4. Identify ways to use Python data structures, execution control statements, and DataFrames to manipulate financial data
5. Derive meaningful financial decisions using Python to compare potential projects and how to make rational, data-driven financial decision

Milestones

1. Data

• The project should use a real-world dataset and include a reference to the source in the report

2. Importing

• Import data from a flat file (.csv, .xls, xlsx, .txt, etc.)
• Retrieve data using online SQL, APIs, or web scraping

3. Preparation

• Create pandas DataFrame
• Sorting, indexing, grouping
• Drop duplicates, replace missing values
• Merge DataFrames

4. Analysis

• Conditional statements, looping, groupby
• Define a custom function to create reusable code
• Use NumPy functions
• Dictionary or Lists

5. Visualisation

• Generate at least two charts using Matplotlib or Seaborn

6. Insights

• Derive five valuable insights from the analysis
• Justify your insights with reference to the charts or analysis

7. Machine Learning

• Describe what kind of prediction you could perform in future using machine learning and/or deep learning.
• Would you use classification or regression methods?

Additional Guidance

Any quotes from external sources should be properly referenced. Choose a referencing style and use it consistently. Poor referencing may affect your grade, and lack of referencing makes the integrity of your entire assessment questionable. We recommend that you use the Harvard Referencing Style, which is well documented in the UCD Library pages: h ttps://libguides.ucd.ie/harvardstyle. 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. 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

Regarding the Project Report, a 10% tolerance above or below the stated word limit is admissible. Referencing does not count towards assessment length limits. Derive insights into data sources a company can use and how to store that data and understand trading dynamics to risk management systems.

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