Reflection

Midterm two was less difficult than expected, as we were not tested on sorting algorithms since we did not have as much practice. However,  the remaining of the class will probably be the most important concept to derive.
At this point of time, I think it's a good think to reflect on the knowledge that I have acquired so far. We have derived sufficient knowledge to bridge a map together, or at least make sense of the steps to formulate a route in the existing midst of vast knowledge. Thus the reason why I am reflecting now. From simple functions to object- oriented programming, we have established a good foundation. But at one point, it will be self-absorbing, by which I refer to the point where enough foundation is built and we will need to learn to solve our own problems.
Look forward to mastering sorting algorithms! So far, in lab, the running time of different sorting was quantitively recorded to make comparison easier. More explicit than intuitive, which I think is gold rule behind iteration/practice. This process physically lays out what you know and allows a concrete layout.

Big O!

I forgot to mention my favorite type of data structure is linked list. The fact that each node is linked to the next one and the next one is linked once again, interests me. It is the simplest representation of recursion to my understanding; solving the same question over again and again or forming the same links repetitively. Each node is indirectly related to another, and being able to trace back to the originator is through one relevancy node. This idea is intriguing to me and would like to further apply this concept in the future. One interesting concept that we learned was that the access time of a dictionary key-value pair is constant

The analysis of sorting methods is in coordination with csc165. We were introduced to Big O notations where algorithms can be bounded by a limit, this characterizes the behaviour of functions. The key behind this concept is the behaviour over a large input and this relates to reality accurately. Business handle large amounts of data, and utilizes the data to operate their business, whether consumer orders or statistical data. The data needs to processed nevertheless, and in a more complicated way than sorting obviously. For example, MapReduce is a programming model for processing large data sets. Optimization is necessary for the most efficient processing as computer programs are made to make life simpler. We are the future computer scientists who will integrate technology with day to day actions. Time does not wait on us, nor do we want to waste time. We need to save time.