Write a research paper in APA format on a subject of your choosing that is related to Business Intelligence. Integrate what you have learned from the course resources (.e.g. Textbook Readings, Discussion Board Posts, Chapter Presentations) into your document.
As you consider the topic for your research paper, try and narrow the subject down to a manageable issue. Search for academic journal articles (i.e. peer reviewed) and other sources related to your selected subject. Because this is a research paper, you must be sure to use proper APA format citations.
Your paper must include an introduction stating what you paper is about and a logical conclusion.
This paper must contain a minimum of 1500 words of content and use at least 5 peer reviewed sources. Peer reviewed sources include: Academic Journal Articles, Textbooks, and Government Documents. At least one of the textbooks for this course could be used as a source for this paper.
- Topic 1. Data science and AI roles continue the trend towards specialization.There is a practical split is between ‘engineering-heavy’ data science roles focused on large production systems and the infrastructure and platforms that underpin them (‘Data/ML/AI Engineers’), and ‘science-heavy’ data science role that focus on investigative work and decision support (‘Data Scientists/Business Analytics Professionals/Analytics Consultants’). Your paper should contrast the skill sets, different mental models, and established department structures that make this a compelling pattern.
- Topic 2. Executive understanding of data science and AI becomes more important.The realization is dawning that the bottleneck to data science value may not be the technical aspects of data science or AI (gasp!), but the maturity of the actual consumers of data science. While some technology companies and large corporations have a head start, there is a growing awareness that in-house training programs are often the best way to develop internal maturity. Is there research to back this approach up?
- Topic 3. End-to-end model management becomes best practice where production is required.As the actual footprint of data science and AI projects in production gets larger, the problems that need to be solved have coalesced into the discipline of end-to-end model management. This includes deployment and monitoring of models (‘Model Ops’), different tiers of support, and oversight on when to retrain or rebuild models when they naturally entropy over time. What are the major issues that this model present?
- Topic 4. Data science and AI ethics continue to gain momentum and are starting to form into a distinct discipline.Second order effects of automated decision making at scale have always been an issue, but it is finally gaining mind share in the public consciousness. This is courtesy of the prominence of incidents like the Cambridge Analytica Scandal and Amazon scrapping its secret AI recruiting tool that showed bias against women. IS AI going toward specialization or generalization? Why?
- Topic 5. Efforts to ‘democratize’ and ‘automate’ data science and AI redouble, with parties that over-promise failing.With talent being somewhat elusive (or at least mis-allocated), automated data science and AI is an attractive idea. However, the reality remains that the boundaries of technology only enable certain well specified tasks to be automated. Taking a typical data science project, there is a lot that goes on around the activity of model building. What is the issue and how can this be helped?
Can any one please prepare a Research paper on one of the above 5 topics, by following all the instructions mentioned above. Will be able to provide you a reference, if needed.