COM759 Applied Data Science Assignment Brief 1 | Wrexham University

Published: 06 Feb, 2025
Category Assignment Subject Computer Science
University Wrexham University Module Title COM759 Applied Data Science

Assessment task details

Students will conduct a small-scale data science project, where they will explore the end-to-end process of data analysis, from data collection to analysis and visualization. This project will test students' ability to apply the fundamental concepts of data analytics, including data cleaning, transformation, exploratory analysis, visualization, and statistical interpretation.

Assignment Structure:

Data Collection (20%):

  •  Students must collect their own dataset relevant to a real-world problem or area of interest (e.g., social media trends, public health data, environmental data, etc.).
  • The dataset should include at least 200 data points and multiple variables.
  • Provide a brief description of the data source, collection methods, and any limitations in the dataset.

2. Data Cleaning and Transformation: (10%)

  • Clean the collected dataset to handle missing values, duplicates, and inconsistencies. 
  •  Transform the data where necessary (e.g., scaling, normalization, encoding categorical variables) to prepare it for analysis.

3. Exploratory Data Analysis (EDA)(10%):

  • Perform EDA to understand the structure and characteristics of the dataset. 
  •  Include summary statistics (mean, median, standard deviation, etc.) and relevant visualizations (e.g., histograms, scatter plots, box plots) to uncover patterns, trends, and relationships in the data.

4. Data Visualization(20%):

  • Create meaningful visualizations to communicate key insights from the dataset. 
  •  Use visualization tools such as bar charts, line graphs, heatmaps, or any other relevant graphical representations to showcase important findings.

5. Statistical Analysis (30%):

  • Conduct statistical analysis to interpret the data, applying methods like correlation analysis, hypothesis testing, or regression analysis, depending on the nature of the dataset.
  •  Provide a detailed interpretation of the statistical results, connecting them to the real-world problem you are investigating.

6. Report: 

Write a concise report (1000 +/- 100 words) that includes the following:

  • A brief introduction to the dataset and the problem being addressed.
  • A step-by-step explanation of your data cleaning and transformation process. 
  •  Key insights from the EDA and visualizations. 
  •  Results and interpretation of your statistical analysis.
  • Conclusion and recommendations based on your findings. 
  •  Reflect on the limitations of your dataset and the methods used.
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