Exploratory Data Analysis (EDA) and Visualization - Identifying Insights in a Dataset
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wrote on 30 Jul 2023, 19:48 last edited by
Hey everyone,
I recently came across an interesting dataset that contains information about the performance of various students in a fictional school. The dataset consists of several features such as student ID, age, gender, test scores, study time, internet usage, and more. Dataset Source from here.
My goal is to perform Exploratory Data Analysis (EDA) and visualize the data to gain insights that could help us better understand the factors influencing student performance. As a data scientist, I want to explore and answer the following questions:
What is the distribution of test scores among the students? Are there any patterns or outliers that stand out?
How does study time relate to test scores? Is there a clear correlation between the two?
Can we identify any differences in performance based on gender or age groups?
Is there any connection between internet usage and test scores?
Are there any interesting relationships between different features in the dataset that we can leverage to improve student performance?
For this task, I plan to use Python and its various libraries such as Pandas for data manipulation, Matplotlib and Seaborn for visualization, and NumPy for numerical computations.Could anyone guide me on how to approach this EDA and visualization process efficiently? Also, if you have any coding tips or sample code to perform specific analyses mentioned above, it would be highly appreciated.
-
Hey everyone,
I recently came across an interesting dataset that contains information about the performance of various students in a fictional school. The dataset consists of several features such as student ID, age, gender, test scores, study time, internet usage, and more. Dataset Source from here.
My goal is to perform Exploratory Data Analysis (EDA) and visualize the data to gain insights that could help us better understand the factors influencing student performance. As a data scientist, I want to explore and answer the following questions:
What is the distribution of test scores among the students? Are there any patterns or outliers that stand out?
How does study time relate to test scores? Is there a clear correlation between the two?
Can we identify any differences in performance based on gender or age groups?
Is there any connection between internet usage and test scores?
Are there any interesting relationships between different features in the dataset that we can leverage to improve student performance?
For this task, I plan to use Python and its various libraries such as Pandas for data manipulation, Matplotlib and Seaborn for visualization, and NumPy for numerical computations.Could anyone guide me on how to approach this EDA and visualization process efficiently? Also, if you have any coding tips or sample code to perform specific analyses mentioned above, it would be highly appreciated.
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