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Assignment Briefs 11-08-2022

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:

  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.

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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