This week, I learned the importance of clear communication and teamwork during sprint planning. Our team successfully reviewed feedback, confirmed that all tasks and user stories for Iteration 2 are well understood, and ensured that responsibilities are evenly distributed through the GitHub project board. I also learned how regular check-ins, early scheduling, and consistent updates can help keep everyone aligned and prevent delays as we move forward with the project.
This week, I learned more about probability distributions, density plots, histograms, and how to visualize data using Python libraries such as Pandas, Matplotlib, Seaborn, and SciPy. I practiced creating density plots, box plots, cumulative density plots, and histograms using real datasets. I also learned how changing things like bin width, bandwidth, transparency, and sample size can affect the appearance and interpretation of graphs. Another important topic was understanding skewness and how transformations such as log10 can help make heavily skewed data easier to analyze. One thing I found interesting was how probability density functions (PDFs) and histograms can represent the same data differently. Before this week, I thought graphs mostly showed the same information in different styles, but now I understand that each type of plot has a different purpose and can make patterns easier or harder to notice. I also learned that larger sample sizes tend to reflect the true distribution...
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