There are probably more than a thousand manuals on how to write a great thesis (some of my favorites can be found here, here and here). They will stress the importance of structure, substance and style. They will urge you to write down your methodology and results first, then progress to the literature review, introduction and conclusions and to write the summary or abstract last. To write clearly and directly with the reader's expectations always in mind. All of these tips are very valuable, but which tips apply to writing academically in the domain of data science?
Today, both humans and machines generate the amount of data. Far outpacing humans' ability to absorb, interpret, and make complex decisions based on that data. Artificial Intelligence has made a lot of advancements recently. There is a lot of research happening in almost all fields of AI like quantum computing, healthcare, autonomous vehicles, computer vision, the Internet of Things, robotics, and more. This article features the top 10 research and thesis topics for AI projects in 2022.
Nearly all you are probably knowledgeable about the dread that accompanies the act of having an essay topic given to you. Nothing's been written, nothing's been thought of; all you've got is an interest, a page that is blank together with instructions to "write." As an old high school English teacher, I'm convinced the anxiety of not knowing things to write is excatly why a lot of of my students hold back until the last possible minute to create an essay. Under pressure, you have no choice but to get started. But at the same time, it's too late to write the essay that is best you can've written.
You'll be doing a lot of work on whatever you choose here, so why not propose something related to your own interests? Like for example, say you are interested in the financial side of things, maybe propose to improve the accuracy of city revenue forecasting by using data such as traffic camera data from commercial areas, pedestrian footfall data from the city center, passenger data from public transit in commercial areas, etc., cross-referenced with sales tax receipts data to predict sale tax trends based on increased or decreased traffic to the businesses that contribute sales taxes. Or maybe money is boring but sports are fun, so propose some analysis of traffic and transit data of all kinds to suggest changes to improve the gridlock when the local team has a big game.