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CST383: Learning Probability Distributions and Data Visualization in Python

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 more accurately, which helped me better understand randomness and sampling variability.

At first, I was confused about the difference between a histogram and a density plot, especially when using parameters like density=True and bw_method. I also had to spend extra time understanding how normal distributions work and why sampled data does not perfectly match the theoretical PDF curve. After practicing more and reviewing the examples, the concepts started making more sense. I still want to improve my understanding of how bandwidth values affect density plots because sometimes it is hard to tell which bandwidth is considered the “best” choice for a dataset.

Overall, this week helped me become more comfortable with data visualization and statistical analysis in Python. I feel more confident reading graphs, understanding distributions, and writing plotting code compared to before.

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