This week’s module focused on algorithm design techniques and graph algorithms. I learned how divide-and-conquer is applied in QuickSort, including how pivot selection affects performance and how the Median-of-Three strategy helps avoid worst-case scenarios. I also studied decrease-and-conquer through binary search and understood why sorting first can improve efficiency. In addition, I learned about Directed Acyclic Graphs (DAGs) and topological sorting. Using Kahn’s algorithm, I practiced calculating in-degrees, identifying source vertices, and detecting cycles. The quizzes and HW4_2 helped reinforce how algorithm steps and data structures like queues affect the final output order.
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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