Data Analysis and Visualization using Python & Matplotlib/Seaborn | Well Explained | Kundan Kumar |
In this video, we delve into the world of student performance analysis using Python and Matplotlib/Seaborn. From a little data cleaning to uncovering valuable insights, we cover various analytical techniques to understand student performance better. Here's what you'll find:
1 ) Descriptive Statistics Analysis: We start by cleaning the data and then dive into descriptive statistics to understand the distribution of student
scores.
2) Correlation Analysis: Explore the relationship between different assessment components to uncover any patterns or dependencies.
3) Performance Analysis: Evaluate student performance using various metrics to gain insights into their strengths and weaknesses.
4) Success Rate: Calculate the success rate to understand the proportion of successful outcomes in student performance.
5) Ranking Analysis: Assign ranks to students based on their performance to identify top performers and areas for improvement.
6) Performance Distribution: Visualize the distribution of student scores using Matplotlib to identify trends and outliers.
7) Comparative Analysis: Compare student performance based on gender, total scores, and other criteria to uncover disparities and trends.
Student Marks dataset Github link: https://github.com/Kundan-Rwanda/data...
===Activity/Assignment Mentioned to try by learners in this video are below===
i) Perform performance analysis by considering the students whose "Final Exam" marks are missing. Consider them as incomplete in the course, neither passing nor failing. [To Solve this activity Watch video at 45:12 Performance Analysis ]
ii) Conduct comparative analysis by plotting female students who are both above and below average, as well as male students, on a scatter plot. [Hints: To Solve this activity Watch video at 1:07:17 Timeline Comparative Analysis ]
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