I am currently pursuing my btech in mechanical engineering and would like to pursue industrial engineering for my master's. the following link will give you the course curriculum of my university. I would like you to analyze it and suggest to me the subjects based on my interests. my interests I would like to learn and delve deeper into the field of data analytics, especially AI & ML but I also like design when it comes to mechanical engineering. i will be doing thesis so suggest the best courses that suites my interests http://catalog.wichita.edu/graduate/engineering/industrial-manufacturing-engineering/industrial-engineering-ms/#requirementstext

Data analytics plays a pivotal role in various AI applications, enabling intelligent decision-making and problem-solving. Discuss three specific examples of how data analytics is utilized in AI applications, such as machine learning, natural language processing, and computer vision.

How will your selected course-Data Analytics with case study help with your goals?Answer as an college student pursuing MBA

Analyse the impact of data analytics on the performance and effectiveness of these AI applications

To understand the reach and scope of business analytics, let’s undertake an independent activity. Understand the following scenario and follow along: For the purpose of this discussion, pick a company/institution of your choice - you are free to choose the firm that you are working with, or, if you are unemployed, you may choose one from an industry that you know about well. If you are a student, you can take the case of your college/university as well - after all, every institution uses data to draw insights today. Now suppose the accessibility of data as a barrier has been removed. You can collect all the data you need - resources are not an obstruction. Note down five possible outcomes that you can now achieve for your company/institution. To keep it relevant, think about them from a business perspective. Now evaluate those outcomes in the present context - keeping in mind the constraints of your company, the resources you might have, the problems with accessing the data, the business viability, etc. Consider what your competitors might be doing and whether the solutions are possible at scale. For all your possible outcomes, discuss why it is possible or why it is not possible. Discuss the areas in which your company/institution is lagging behind and the developments that could help improve the outcomes. Note that you are not expected to know all about the capabilities of either your institution or analytics in general. The purpose of this discussion is to stimulate your analytical thinking. Comment on your peers’ answers and develop a critical review of all points presented.

When using data analytics in engineering projects, it's important to consider:Select one:a. The cost of acquiring and storing large datasets.b. The level of technical expertise needed to interpret the results.c.The potential for bias in the data or algorithms used.d. The computational power required to run the analysis.