This week, I learned more about working with data using Python, Pandas, NumPy, and visualization tools. I already have some experience with coding, so some parts felt familiar, especially reading code, testing outputs, and understanding how variables work. However, this week helped me practice applying those skills specifically to data analysis and visualization. One important thing I learned was how to choose the correct type of plot based on the variables. For example, a histogram is useful for showing the distribution of one numeric variable, a boxplot is helpful when comparing a numeric variable across categories, and a bar chart or count plot works well for categorical data. I realized that making a graph is not just about writing the code correctly. It is also about understanding what the question is asking and choosing a visualization that clearly answers it. I also practiced problems involving discrete distributions, such as binomial probability and expected value. These proble...
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...