This week, I focused on understanding the Merge Sort algorithm and how the divide and conquer strategy works in practice. From the lecture video, I learned that Merge Sort repeatedly splits an array into two halves until each subarray has only one element, which is already sorted. The key step is the merge operation, where two sorted subarrays are combined by comparing elements and placing them in order. Using the recurrence relation T(n) = 2T(n/2) + Θ(n) and applying the Master Theorem, I learned that the overall time complexity of Merge Sort is Θ(n log n). This helped me connect the algorithm steps with formal time-complexity analysis.
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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