Data Analysis and Visualization using Python & Matplotlib/Seaborn | Well Explained | Kundan Kumar |
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- čas přidán 29. 04. 2024
- 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: 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 ]
Your explanation of data analysis is clear and really helpful. Thanks for making it easy to understand
Glad to know that this video explanation is clear and very helpful to you as well ❤️