One More Byte CS Devlog

     This week covered a large amount of topics related to data visualization and statistical analysis using pandas. We learned different plotting systems, how to display information in meaningful ways using , how to customize visualizations, and how to perform calculations using the data. At first it was a little overwhelming because there were many different visualization methods and understanding where each was best used depending on the different types of data. It took me some time and a decent amount of review before I became more comfortable recognizing where certain plotting systems are most useful or how to interpret them effectively.      We also covered concepts like correlation and covariance. One thing I found especially interesting was the idea that even though data may appear objective and straightforward, it still requires critical thinking to interpret correctly. Identifying correlation does not necessarily reveal the full truth behind the ...

     This week the focus was on working with data using the pandas library, and later an introduction to probability density functions. One of the main topics was pandas series and how flexible they are for working with and visualizing data. We covered indexing, vectorized operations, and how Series differ from standard numpy arrays. While both structures are similar, what stood out to me is how pandas series include labeled indices, which makes the data much easier to interpret and work with. Instead of just relying on positional indexing like arrays, being able to have meaningful labels allows for clearer data manipulation. This seems especially useful when working with large real world datasets where context is just as impotent as the values.     P andas dataframes and how they are used to organize data was also covered. This section felt pretty intuitive because it felt like working with tables in a database or spreadsheet. Along with that was an introducti...

     For our the first week, time was spent setting up Anaconda and learning the fundamentals of Python along with an introduction to NumPy. While the basic syntax was straightforward, it was good review to revisit the distinction between expressions and statements and how expressions produce values while statements perform actions.      An interesting topic this week was the difference between standard Python lists and NumPy arrays. took a look at how NumPy is faster for numerical operations largely due to how data is stored in memory. NumPy arrays store elements of the same type in a contiguous block of memory, which makes access much more efficient. This allows NumPy to take advantage of low level optimizations showing that that NumPy’s speed is not just about better algorithms, but also about how data is structured and processed.     P ractice with manipulating arrays using slicing and fancy indexing, as well as creating and working with mult...

What went well? My time volunteering as a developer for The Document Foundation was genuinely rewarding. The most valuable aspect was getting first hand exposure to a large, real world codebase, which is something that classroom projects can't replicate at scale. I had the opportunity to learn how to set up a professional development environment from scratch, navigate a complex project, and engage with an industry standard code review workflow through Gerrit. I also got experience with bug tracking tools and learned how to locating the right targets for code changes within a massive project structure. Altogether, the experience gave me a much clearer and more real sense of what day to day work as a software developer actually looks like. What was the most impactful part? The most impactful part of this experience was definitely working directly within a large codebase along with a real code review system. It pushed me out of my comfort zone in a productive way like becoming more...

     This week we first covered counting sort and radix sort. I was especially impressed by the design of radix sort because of how elegant and efficient it is when applied in the right context. It was interesting to see how a noncomparison based sorting algorithms was able to outperform the traditional approaches.      We then moved into dynamic programming. At first, I found the concept somewhat confusing because it did not immediately seem different from simply working through a problem step by step. However, it eventually clicked during the homework that dynamic programming is about intentionally breaking a complex problem into smaller, manageable subproblems, solving each of them once, and then combining their results to build the final solution efficiently. Using this, I was able to pull apart the coin problem logic into smaller pieces and recombine it once each part was solved.      After that, we covered the coin-row problem and work...

     This week started with covering AVL trees and 2-3 trees. At first the order of operations required to properly rebalance and maintain these trees after insertions or deletions was a bit tricky to follow but carefully working through the videos and demonstrations helped clarify the rotation and restructuring processes. Seeing multiple examples made it much easier to understand how these self balancing trees maintain structure.      After that was heaps, how to identify them, how to construct them while maintaining heap properties, and how heap sort works. The progression from understanding heap structure to applying it in heap sort helped connect the conceptual sides of the topic. The final lectures this week focused on hashing and hash tables. I found hashing interesting because the overall concept feels intuitive, which made the material easier to grasp while still demonstrating how powerful hash tables can be for achieving efficient lookups.  ...