This week, I learned how several algorithms solve optimization and graph problems, including dynamic programming for the coin-collecting and coin-row problems, Floyd and Warshall algorithms for shortest paths and transitive closure, and Prim’s algorithm for minimum spanning trees. I practiced tracing tables step by step and understanding how intermediate states evolve, which helped me better connect the concepts across topics like sorting and greedy methods. I also started reviewing for the final exam by going through the review materials and key topics such as algorithm analysis, sorting, graph algorithms, and problem-solving strategies to reinforce my understanding and identify areas that need more practice. Additionally, I watched a video review of Dijkstra’s algorithm (https://www.youtube.com/watch?v=Gd92jSu_cZk), which helped reinforce how to trace the algorithm step by step and understand how shortest paths are computed in practice.
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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