task is to learn more about self-balancing BSTs and to report about what you have learned. Keep in mind that the end goal here is to learn what you can about these data structures and describe what you have learned. You task is not to find and copy or re-word information from the Internet or other sources. Your work should start by reading the document Notes about Binary Search Trees, then go on from there. Wikipedia is usually a good place to start for computer-related technical information, but you should not rely only on Wikipedia. The references cited by Wikipedia are often good sources of information. The National Institute of Standards and Technology's Dictionary of Algorithms and Data structures is also a good source of information, although it tends to be programming-oriented. (See: https://xlinux.nist.gov/dads (Links to an external site.) and https://xlinux.nist.gov/dads/HTML/binarySearchTree.html (Links to an external site.)) First, briefly describe what self-balancing Binary Search Trees are, and why they are important. Pay particular attention to the time efficiency of a self-balancing BST compared to a BST that is not self-balancing. Self-balancing trees often involve rotations. Your work should tell us about this and should describe a left-rotation and a right-rotation. You should also briefly describe how AVL trees and red-black trees determine when to perform these rotations. You should tell us something about each of the other data structures near the end of document Notes about Binary Search Trees. In particular, try to capture the essential characteristic of each structure. For example, what is it about splay trees that makes them splay trees? how does each data structure differ from or how is it related to each of the other structures mentioned?

Your task is to learn more about self-balancing BSTs and to write a report about what you have learned. Keep in mind that the end goal here is to learn what you can about these data structures and describe what you have learned. You task is not to find and copy or re-word information from the Internet or other sources. Your work should start by reading the document Notes about Binary Search Trees, then go on from there. Wikipedia is usually a good place to start for computer-related technical information, but you should not rely only on Wikipedia. The references cited by Wikipedia are often good sources of information. The National Institute of Standards and Technology's Dictionary of Algorithms and Data structures is also a good source of information, although it tends to be programming-oriented. (See: https://xlinux.nist.gov/dads (Links to an external site.) and https://xlinux.nist.gov/dads/HTML/binarySearchTree.html (Links to an external site.)) First, briefly describe what self-balancing Binary Search Trees are, and why they are important. Pay particular attention to the time efficiency of a self-balancing BST compared to a BST that is not self- balancing. Self-balancing trees often involve rotations. Your work should tell us about this and should describe a left-rotation and a right-rotation. You should also briefly describe how AVL trees and red-black trees determine when to perform these rotations. You should tell us something about each of the other data structures near the end of document Notes about Binary Search Trees. In particular, try to capture the essential characteristic of each structure. For example, what is it about splay trees that makes them splay trees? how does each data structure differ from or how is it related to each of the other structures mentioned?The data-driven decision-making approach reflects John's educational and work experiences, and the data-driven environment encouraged by his company and the financial services industry. John is definitely an advocate of data and analytics and sees them as a way to challenge judgment and make stronger decisions, but he also recognizes some of their limitations and the way they can be misused or misunderstood. Question to answer: What kind of issues John might have when working with people who are not analytics-bent? How are the issues different when he works with those from executive management?\fI do not want to see text cut and pasted form Websites or slightly re-worded text from Websites. I do not want to see the above notes used as a list of questions with answers for each data structure. I want you to use these notes as a guide to learn about the different BST-related structures and tell us about them. You should not use opinion-based bulletin board type forums, such as Stack Overflow, or language tutorials for this assignment. We do not need a lot of technical data, nor technical descriptions of algorithms. Basically, you just need to describe why the self-balancing aspect of AVL trees and red-black trees is desirable, generally how it works - including describing rotations, and then discuss some other approaches people have tried, telling us how these other approaches relate to one another or how particular approaches are different. It would be good to make historical references to who created these approaches or when they did their work, similar to the brief information about AVL trees and red-black trees in Notes about Binary Search Trees. You can mention anything else interesting that you discover. Perhaps you will learn that certain structures tend to be used in certain situations or with certain languages. You might learn about structures not mentioned in Notes about Binary Search Trees. Be careful not to get carried away with your report. Make sure you include the essential information described above, but it does not need to be a very long report, nor filled with highly technical details. The size is up to you. Learning about these things and telling us what you learned is what this is all about. Imagine that you are tasked with telling the rest of the class about these topics in a presentation.\fSocietal influences: Regulated. legal reporting requirements. data security and privacy restrictions John is a Type A, analytics-bent, managerial decisionmaker. He is a business analyst working for a large nancial services company. The company is New ZealandbasedJ but has important connections with international partners. John's bent for analytical thinking was strongly influenced by his university undergraduate studies in mathematics. and statistics during his Master's degree in Business. 1|While he has had no formal training in data and analyticsr he has five to six years of work experience with them. Currently he manages a small analytics team that often contributes to strategic decisionmaking in the organization. He explains: I report directly to the CEO and provide key analytics, key data, for making strategic decisions forthe business. My group oversees all the business performance reporting on a daytoday, weektoweek, and monthto-month schedule that guidesthe understanding of where the business is at. By contributing these data insights. John is an integral part of his organization's strategic decision making.The decisions he is involved with are usually highdata decisions, but also include baianced decisions with high data use. While this highdata decisionmaking process usually begins with using iudgment as an initial assessment, it is guicklyfollowed by the gathering of as much information and data as possible. John critically examines his initial assessment for any potential biases he might bring to the decisionmaking process and challenges them before beginning his analysis of the problem using data analytics. He reflects on this process: F' ""I What's my preconceived idea about this? I'm not afraid to recognize and challenge it. I try and remind. myself not to by to find only the information to prove my initial assessment. I think a lot of people use data that way; they think: 'Cih yes, I know what the answer is, letls find some ciata to support what I already believe.' I thinkthat's how people misuse data. Better to ask what is the situation here? Can data bring clarity to it? If yes, how am I going to gather this data? And then I make a quality observation and analysis. It's like I follow an almost experimental methodology every time I use big data to make a decision. John finds that using data is a balancing act and that even decisionmakers with the analytics bent have to be mindful oftheir extent of data use: r| think there's always a danger of dismissing it or over relying on it. There's always a fine line, even if someone is analytical.' As a last step in the decision making process, he also consults others to sense-check his analytics results: r| usually bounce it off other people in my team.' His colleagues are open to this process. Furthermore, the analytics that John develops are used in organizational strategic decisionmaking, and are also checked and challenged by his superiors. John needs confidence and communication skills to defend them when this happens. John emphasized that he has always had the analytics bent, and describes the influence of the company culture on his decisionmaking approach as a good fit ratherthan an influence. He explains: 'l'm not sure ifthe company culture influences me as much as it supports how I already think and work.' The financial services industry also relies heavily on data analytics: again a good match for his nalytical personality. 4