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Derive insights into data sources a company can use and how to store that data and understand trading dynamics to risk management systems
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:
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 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.
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
How You Will Be Assessed The following rubric describes how the essay will be assessed:
1.Demonstrate an
2. Show clear
3. Show clear
4. Demonstrate
5. Create custom
understanding of
understanding of
understanding of
ability to
visualisations of
and apply key
how to store and
key concepts of
manipulate
the datasets and
concepts and
manipulate data in
Boolean logic,
multiple
from these
principles of
Python data
control flow and
DataFrames by
generate insights
various ways to
structures
loops in Python.
combining,
of the underlying
import data into
organising, joining
data.
Python.
and reshaping them
using Pandas
Distinction Criteria
Demonstrates strong
Demonstrates a
Demonstrates a
Demonstrates a
Demonstrates strong
ability to apply key
strong
strong
strong ability to
ability to create
concepts and
understanding of
understanding of key
manipulate multiple
visualisations from
principles of various
how to store and
concepts of Boolean
DataFrames by
datasets and derive
ways to import data
manipulate data in
logic, control flow
combining,
valuable insights.
into Python.
Python data
and loops in Python.
organising, joining
structures.
and reshaping them
using Pandas.
Merit Criteria
Able to apply key
Able to how to store
Demonstrates a good
A very good level
Demonstrates a good
concepts and
and manipulate data
understanding of key
ability to manipulate
ability to create
principles of various
in Python data
concepts of Boolean
multiple DataFrames
visualisations from
ways to import data
into Python.
structures.
logic, control flow
and loops in Python
by combining,
organising, joining
datasets and derive
valuable insights.
and reshaping them
using Pandas.
Pass Criteria
Adequate use of key
Makes adequate use
Makes adequate use
Adequate
Adequate
concepts and
Python data
of Boolean logic,
manipulation of
visualisations
principles of various
structures.
control flow and
multiple DataFrames
created and insights
ways to import data
loops in Python
by combining,
derived.
into Python.
organising, joining
and reshaping them
using Pandas.
Unsatisfactory Criteria
Response is partial or
Inadequate
Inadequate
Inadequate
Unsatisfactory
tangential. Requires
application of course
application of course
manipulation
visualisation and
greater depth, level
learning in Python
learning of Boolean
techniques of
insights derived from
of detail and discussion.
data structures.
logic, control flow and loops
DataFrames.
data.
Clear Fail Criteria
Little evidence of
Little evidence of
Little evidence of
Very little evidence
Little evidence of
knowledge importing
ability to apply key
ability to apply key
of self-reflection
ability to create
data into Python.
course concepts
course concepts in
manipulation of
visualisations or
data structures.
practice.
DataFrames.
derive insights.
No Attempt Criteria
No submission
No submission
No submission
No submission
No submission
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