Description
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Restricted – مقيد
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Description and Instructions<> “Error*” “Description and Instructions Description and Instructions |
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Description and Instructions
Introduction:
In this group project (max. 3 students per group), you will explore one dataset from a selection of Ten Phenomenal Resources for Open Data (From Module 6 Slides). Your objective is to develop a deep understanding of the dataset by thoroughly describing its structure and technical details. Additionally, you will reflect on key topics introduced in the course to demonstrate how these concepts can be applied to the dataset. This project will help you strengthen your skills in data comprehension and relate them to the theoretical foundations you’ve learned in this course.
Project Guidelines:
1. Dataset Selection and Technical Description (4Marks)
i.Dataset Selection (2 marks): Choose 3 datasetsfrom the provided Ten Phenomenal Resources for Open Data (From Module 6 Slides) and explain the reason that makes you choose it.
ii.Technical Description (2 marks):
Provide a detailed description of the dataset’s structure:
• Number of instances (rows).
• Number of features (columns).
• Data types for each feature (e.g., numerical, categorical).
• Indicate the target variable if applicable, or any key features of interest.
Objective: The goal is to understand the dataset technically without performing Python-based analysis, focusing on understanding the raw data characteristics.
2. Reflection on Course Concepts (7 Marks)
Based on the topics you’ve learned in class, particularly from Module 5: Probability and Statistical Modeling, reflect on how these concepts can be related to your chosen dataset:
• Statistics: Differentiate how you could apply descriptive statistics (e.g., mean, variance) to understand your dataset, and how inferential statistics could be used to make predictions about a larger population. (1.5 marks)
• Correlation: Identify potential correlations between variables in your dataset, discussing how these relationships might be quantified. (1.5 marks)
• Dimensionality Reduction: Discuss whether techniques like Principal Component Analysis (PCA) could be used to simplify the dataset while retaining meaningful information. (1.5 marks)
• Regression Methods: Consider how you might apply linear regression or other regression models to predict outcomes based on certain features. (1.5 marks)
• Outlier Detection: Hypothesize where outliers might exist in the dataset and explain why addressing these might be important. ((1 mark)
3. Project Report Presentation and Structure (3 Marks)
You will submit a well-structured report that demonstrates both your technical description of the dataset and your conceptual reflections. The report should be appended to this file and include:
• Introduction: Provide an overview of the dataset and what you aim to achieve in this project.
• Technical Description of the Dataset: Explain the dataset’s key features and characteristics.
• Reflection on Concepts: Describe how course topics like statistics, correlation, and regression apply to the dataset.
• Conclusion: Summarize the key insights and findings from your project.
• References: Provide proper citations for the dataset and any sources you used.
Submission
• One group member (group leader/coordinator) must submit all files (project report, Dataset file, source code (if any)and presentation slides) on blackboard. One submission per group by a group leader. Individual group members do not need to submit the duplicate report. Marks will be given based on your submission and the quality of the content.
o Show screenshots of your derived results in the report.
o Each Report will be evaluated according to the marking criteria mentioned in each question section.
Restricted – مقيد
Project
Deadline: Day 02/12/2024 @ 23:59
[Total Mark is 14]
Student Details:
CRN:
Name:
Name:
Name:
ID:
ID:
ID:
Instructions:
• You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on
Blackboard via the allocated folder. These files must not be in compressed format.
• It is your responsibility to check and make sure that you have uploaded both the correct files.
• Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between
words, hide characters, use different character sets, convert text into image or languages other than English
or any kind of manipulation).
• Email submission will not be accepted.
• You are advised to make your work clear and well-presented. This includes filling your information on the cover
page.
• You must use this template, failing which will result in zero mark.
• You MUST show all your work, and text must not be converted into an image, unless specified otherwise by
the question.
• Late submission will result in ZERO mark.
• The work should be your own, copying from students or other resources will result in ZERO mark.
• Use Times New Roman font for all your answers.
Restricted – مقيد
Description and Instructions
Pg. 01
Description and Instructions
Introduction:
In this group project (max. 3 students per group), you will explore one dataset from a
selection of Ten Phenomenal Resources for Open Data (From Module 6 Slides). Your
objective is to develop a deep understanding of the dataset by thoroughly describing its
structure and technical details. Additionally, you will reflect on key topics introduced
in the course to demonstrate how these concepts can be applied to the dataset. This
project will help you strengthen your skills in data comprehension and relate them to
the theoretical foundations you’ve learned in this course.
Project Guidelines:
1. Dataset Selection and Technical Description (4 Marks)
i.
Dataset Selection (2 marks): Choose 3 datasets from the provided Ten
Phenomenal Resources for Open Data (From Module 6 Slides) and explain the
reason that makes you choose it.
ii.
Technical Description (2 marks):
Provide a detailed description of the dataset’s structure:
•
Number of instances (rows).
•
Number of features (columns).
•
Data types for each feature (e.g., numerical, categorical).
•
Indicate the target variable if applicable, or any key features of interest.
Objective: The goal is to understand the dataset technically without performing
Python-based analysis, focusing on understanding the raw data characteristics.
Description and Instructions
Pg. 02
2. Reflection on Course Concepts (7 Marks)
Based on the topics you’ve learned in class, particularly from Module 5: Probability
and Statistical Modeling, reflect on how these concepts can be related to your
chosen dataset:
•
Statistics: Differentiate how you could apply descriptive statistics (e.g., mean,
variance) to understand your dataset, and how inferential statistics could be
used to make predictions about a larger population. (1.5 marks)
•
Correlation: Identify potential correlations between variables in your dataset,
discussing how these relationships might be quantified. (1.5 marks)
•
Dimensionality Reduction: Discuss whether techniques like Principal
Component Analysis (PCA) could be used to simplify the dataset while
retaining meaningful information. (1.5 marks)
•
Regression Methods: Consider how you might apply linear regression or other
regression models to predict outcomes based on certain features. (1.5 marks)
•
Outlier Detection: Hypothesize where outliers might exist in the dataset and
explain why addressing these might be important. ((1 mark)
3. Project Report Presentation and Structure (3 Marks)
You will submit a well-structured report that demonstrates both your technical
description of the dataset and your conceptual reflections. The report should be
appended to this file and include:
•
Introduction: Provide an overview of the dataset and what you aim to achieve
in this project.
•
Technical Description of the Dataset: Explain the dataset’s key features and
characteristics.
•
Reflection on Concepts: Describe how course topics like statistics, correlation,
and regression apply to the dataset.
Description and Instructions
Pg. 03
•
Conclusion: Summarize the key insights and findings from your project.
•
References: Provide proper citations for the dataset and any sources you
used.
Submission
•
One group member (group leader/coordinator) must submit all files (project report,
Dataset file, source code (if any) and presentation slides) on blackboard. One submission
per group by a group leader. Individual group members do not need to submit the duplicate
report. Marks will be given based on your submission and the quality of the content.
o
Show screenshots of your derived results in the report.
o
Each Report will be evaluated according to the marking criteria mentioned in each
question section.
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