Skip to main content

Meet, Click, Repeat: How Our Team Survived Discord and Found Harmony

Part One

For our team's Final Research Video Project planning, we collaborated primarily through Google Meet and Discord. These platforms helped us coordinate meetings, share ideas, and finalize our plans effectively. The process went smoothly, with each team member actively engaged and offering insightful suggestions that helped shape the direction of our project.

Looking back, one improvement we could make for future large collaborative projects is to incorporate a dedicated project management tool like Smartsheet, Monday.com, or ClickUp. While our communication was effective, having a centralized platform to assign tasks, set deadlines, and track progress would have made the process even more organized and transparent. Overall, our teamwork was strong, and this experience emphasized the importance of both communication and structured planning in collaborative work.

Part Two

This week’s lectures and activities offered valuable insights into both human-AI collaboration and effective presentation skills. The TED Talk Why People and AI Make Good Business Partners explored how AI isn’t here to replace people but to work alongside us to improve decision-making and productivity. It was a compelling reminder that the best outcomes often come when technology complements human strengths rather than tries to mimic them.

Similarly, Fei-Fei Li’s talk How We're Teaching Computers to Understand Pictures offered a fascinating look into the development of computer vision and how machines are beginning to interpret the visual world. Her talk helped me understand the complexity behind what seems like a simple task for humans—recognizing objects in images—and the real-world implications of training machines to “see.”

We also explored presentation skills, focusing on clarity, engagement, and storytelling. One of the biggest takeaways for me was the importance of connecting with the audience on a personal level rather than simply listing information. Effective presentations require not only clear content but also the ability to tell a story that resonates, keeps the audience interested, and makes the message memorable. Techniques such as varying tone, using pauses strategically, and maintaining eye contact were highlighted as essential for creating a strong connection with listeners.

Additionally, I learned how to use visual aids effectively to enhance a presentation without overwhelming the message. Visual elements should complement and reinforce what the speaker is saying, helping to clarify complex points or emphasize key ideas. It’s important to strike a balance—too many visuals or distracting graphics can detract from the main message and confuse the audience. This insight will be crucial when designing slides and props for our upcoming presentations to ensure they support our communication goals rather than detract from them.

Overall, this week helped me understand the synergy between people and technology, while also improving my awareness of how to communicate ideas more effectively in a visual and engaging way. On a personal note, I celebrated Father’s Day by having lunch and going on a short walk with my family. It was a refreshing and meaningful moment, and I’d like to share a photo from our time on the trail together.



Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

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 ...
Post a Comment
Read more

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...
Post a Comment
Read more