Data Science and Analytics Assignment

Assignment Task

Overview

The digital age has presented us with a priceless commodity – data, and lots of it. However, having lots of data is meaningless if we don’t know how to make sense of all this data. The power of Artificial Intelligence can be harnessed to extract meaning from voluminous amounts of information. This assignment offers you the opportunity to apply the techniques you learn in class to help you make sense of data.

In this assessment you will be analysing the crucial role that sleep plays in overall health and well-being, impacting various aspects of cognitive function, emotional regulation, and physical health. Understanding the factors influencing sleep patterns and quality is essential for promoting better sleep hygiene and preventing sleep-related disorders. The Sleep Health and Lifestyle Dataset offers a valuable resource for exploring the complex interplay between demographic factors, lifestyle habits, and sleep health metrics.

You work as a data scientist to analyse the sleep data collected by a wearable device. You are tasked with the role of analysing the data to improve the accuracy of sleep tracking and provide insights into how lifestyle factors affect sleep quality. By analysing this data, you have the opportunity to identify trends and correlations that can inform strategies for promoting better sleep hygiene and preventing sleep-related disorders. Your analysis could lead to the development of more effective interventions and treatments, ultimately improving the sleep health and overall well-being of individuals and communities.

Task

Despite the well-documented importance of sleep, there remains a need for comprehensive analysis of factors influencing sleep patterns and quality within diverse populations. The Sleep Health and Lifestyle Dataset presents an opportunity to address this gap by examining the relationships between demographic variables, lifestyle habits, and sleep-related metrics. By leveraging advanced data science techniques, we aim to uncover insights that can inform targeted interventions for improving sleep health and identifying individuals at risk of sleep disorders. This study seeks to answer key questions such as the predictors of sleep duration and quality, the clustering of individuals based on sleep behaviour and lifestyle factors, and the identification of patterns indicative of sleep disorders. Through rigorous analysis of the dataset, we aim to contribute to the growing body of knowledge on sleep health and provide actionable recommendations for promoting better sleep habits and overall well-being.

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