Understanding a Slow Index in Database Queries

In database systems, indexes are used to make queries faster by helping the system find data quickly, similar to how a book index helps you find pages faster. However, the author says an index can still be “slow” when the database needs to do extra work after using the index. For example, if the query matches many rows or if the data is stored in different parts of memory, the database must open and read each row separately, which takes time. This idea connects to what I’ve learned in computer science and my experience with systems like SAP and MES, where performance depends not only on structure but also on how data is accessed. So, a “slow index” means the index works, but the process of fetching all the related data makes the query slower overall.

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CST383: Learning Probability Distributions and Data Visualization in Python

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...

Choosing Between MongoDB and MySQL

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 ...

CST383 Week 1: Python for Data Science

This week, I learned the basics of Python for data science and how tools like NumPy are used. I already have programming experience from my computer science classes, but Python feels different from languages like Java or C++. It is easier to write and more flexible because it does not require strict data types. This makes coding faster, but I also need to be careful to avoid mistakes. We also learned about the Python data science ecosystem, such as NumPy, Pandas, and tools like Google Colab and Jupyter Notebook. I liked using Google Colab because it is simple and runs in the browser, so I don’t need to install anything. However, I am curious when it is better to use local tools like Spyder or Jupyter instead of Colab. The most important concept for me this week was NumPy. I learned that NumPy arrays are much faster than Python lists because they store data in a continuous block of memory and use the same data type. This connects to what I learned in my algorithms class, where performan...

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