Learning Journal

Part 1: Review Other Team's Final Video Projects Group 3 - Web Weavers: Long Presentation: https://youtu.be/EUv2vh-8NFg Short Presentation: https://youtu.be/qOHv2pQNABQ The presentation on SB53, California's AI safety Bill, provided solid coverage of the topic by clearly explaining the scope of legislation, its focus on larger AI models, and the requirement for developers to disclose their safety practices and incident response. For the shorter video the graphics used were friendly for the general public and especially children and the video was informative to the point that needed to be addressed. For the longer video, the presentation shows how throughly the topic was researched and covered the main areas for this topic. Both videos showed how well the work was evenly distributed by the voice changes throughout the video. Overall the team addressed the topic and were informative as well as made it engaging to learn.  EduOtter: Long Presentation: https://youtu.be/N5...

 Post - Interview Reflection Report:  Introduction:  For this assignment, I interviewed Amy Bloch, my current technical lead at IBM. Amy is an accomplished software engineer with extensive experience in leading teams, building complex systems, and mentoring early-career engineers. Before moving into her current leadership role, she spent years working directly on software design and development, which gives her a strong technical foundation that she still relies on today. Currently as a technical lead, she not only contributes to solving technical challenges bit also guides the direction of projects and supports the professional growth of those on her team.  I choies to interview Amy because I respect the way she balances technical expertise with leadership. Working under her has given me a firsthand view of her ability to keep projects on track, resolve challenges efficiently and empower team members to grow. As someone who is still in early paths of their career, I...

 Three Most Important Things Learned this Course:  1. SQL Querying and Data Manipulation: Learning how to use SQL to create, query and manage relational databases was the number one thing I learned this course. Writing single-table queries, performing joins, working with subqueries and using view gave me the ability to extract meaningful information from large datasets. I also learned the importance of constraints to maintain data integrity. These were essential skills learned especially since SQL is heavily used in the industry.  2. Database Design and Normalization:  Another key takeaway was the importance of ER modeling in designing efficient databases. By learning how to normalize data and reduce redundancy, I understood how good database design not only saves storage but also improves consistency and scalability. The labs on ER diagrams made it clear how theory directly applies to practical database design.  3. Advance Topics: Indexing, Transactions and Dis...

Part 1: Team Final Research Video Project Planning Our team's planning process for the final research video project was a valuable learning experience in collaboration. We divided the responsibilities by giving each teammate a section of our presentation. We have collaborated through google videos. Communication was maintained primarily thought slack and weekly team meetings through zoom. The process was smooth in terms of idea sharing, but there were moments where coordinating schedules was difficult. I would suggest so every team member had the same timeline that we should have had regular check-ins. Overall, collaboration taught us the importance of balancing flexibility with structure. While creativity thrived in open discussions, timelines benefited from clear accountability.  Part 2: Reflections on Lectures and Activities This week’s materials offered me a chance to connect technical knowledge with broader professional skills. The readings reminded me how important it is to c...

Comparison of MongoDB and MySQL Similarities:  Both are widely used database management systems for storing and managing data. They support querying, indexing, and data replication for performance and reliability.  Both can be integrated into a variety of applications, from small projects to large-scale enterprise systems.  Differences:  Data Model: Mongo DB is a NoSQL, document-oriented database that stores data in flexible JSON-like documents, while MySQL is a relational database that organizes data into structured tables with rows and columns.  Schema: MongoDB allows for dynamic, flexible schema, whereas MySQL enforces a predefined schema with relationships between tables.  Query Language: MySQL uses SQL (Structured Query Language), while MongoDB uses its own query language with JSON-style syntax.  Use Cases: MongoDB excels with unstructured or semi-structured data and rapid development needs, while MySQL is ideal for structured data with clear rela...

Part 1: Help Your Teammates to Develop Capstone Ideas Glenn's Capstone Ideas: I think all three capstone ideas are creative and show strong potential, but the pet adoption SaaS product stood out to me the most because it not only demonstrates technical skills but also has a meaningful real-world impact. The CAD software and video game concepts are also exciting, and I admire how they connect to personal passions while offering opportunities to grow in areas like graphics, optimization, and game mechanics. Vincent's Capstone Ideas: I think the research-focused ideas, especially on AI programming and how AI can create other AI, are fascinating because they explore cutting-edge topics that could lead to deeper understanding of emerging technology. The programming project ideas also stood out to me, particularly the game concepts, since they combine creativity with practical learning opportunities and could be both engaging and educational. Tim's Capstone Ideas: I think all thr...

 Key Concepts Learned this Week:  This week, I focused on Lab19. The main thing I learned is building an application in fill-stack Spring Boot with integration of MySQL. The process reinforced her to define an ER model and translate it into SQL create table schemas, ensuring that entities like patients, doctors, prescriptions and pharmacies are well-connected with proper primary and foreign keys. On the backend, controllers were implemented to handle patient registration, updates, prescription creation and prescription filling, highlighting the use of Spring MVC, request mappings, JDBC for database access. Testing the application involved running it locally, interacting with forms via a browser and handling both successes and error cases. The lab focused on connecting database design, backend logic and user interfaces into a working web application. 

Prompt: The web site   "Use the Index Luke"  has a page on "slow indexes".    https://use-the-index-luke.com/sql/anatomy/slow-indexes Links to an external site. If indexes are supposed to speed up performance of query, what does the author mean by a slow index?  When the author writes about a "slow index", he means that even though a query uses an index, the overall performance can still be poor. An index lookup is not just a simple tree traversal - two other factors can slow it down:  1) Leaf-node chaining: If multiple entries match, the index must follow the linked leaf nodes to gather all matches 2) Table access per hit: after finding matching index entries, the engine often has to fetch the actual rows from the table, which may be scattered across disk pages.  Since these latter steps can require many random I/O operations, they can dominate cost and make even an "indexed" query run slowly. 

Part One: Support and Comment on Teammates' Goals https://rrh-cst349.blogspot.com/2025/09/week-4.html?sc=1759766413145#c3682610505999987379 https://annies-cs-journey.blogspot.com/2025/09/week-4-my-csumb-journey.html?sc=1759766670451#c6148798155532794758 Part 2: Possible Capstone Ideas Idea 1: Data Pipeline for Real-Time Analytics on Public Data Description: Build an end-to-end engineering pipeline that ingests, cleans and processes real-time data (ex: stock market, or sports stats). Store the data in a structured format, then build an API for real-time analytics.  Idea 2: Machine Learning-Data Cleaning Assistant Description: Create a tool that detects and corrects anomalies, duplicates or missing values in datasets using both statistical rules and machine learning models. (Ex: applying clusters to detect outliers to fill missing values). Package the tool into a library or web interface.  Idea 3: Graph Analytics Platform for Network Data Description: Build a platform to analyze...