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 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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