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 how MongoDB and MySQL are both powerful tools for managing data, but they serve different purposes. MySQL is a relational database that organizes data into tables with rows and columns. It uses SQL (Structured Query Language) to define and manage data, which makes it very structured and reliable. MongoDB, on the other hand, is a NoSQL database that stores data as documents in a flexible JSON-like format . It does not require a fixed schema, so it is easier to change or add new data types as needed. Both databases are similar because they can handle large amounts of data, support indexing for faster searches, and allow users to perform queries to get specific information. They are also widely used in modern applications and can be connected to programming languages like Java, Python, or C++. However, the key difference is how they store and organize data. MySQL is best when data has clear relationships, such as in school systems, banking, or employee ...
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