Business Analytics/Statistics
There are six questions to answer and each answer should be a minimum of 250 words, which is why I put 6 pages on the order. Responses should be detailed with examples, if applicable.
1) What is machine learning? How does it differ from statistically learning? Give an example of each. Are both still relevant and important when making business decisions? Explain your answer.
2) There are a number of learning scenarios, or types of learning algorithms, that can be used depending on whether a target variable is available and how much labeled data can be used. These approaches include supervised, unsupervised, and semi-supervised learning. Explain the difference between each type of machine learning. Give an example of how each is used.
3) What are todays primary ways through which machine learning tasks are tackled? Explain the concept of deep learning and how it differs from machine learning. How are organizations using deep learning to help make business decisions?
4) Most organizations are just scratching the surface of what they can learn and accomplish through the analysis of unstructured text. Opportunities for large and small businesses, as well as applications, are expanding. How can text analytics be applied to solve todays business problems? What are some of the challenges organizations face when implementing text analytics solutions?
5) Suppose your company takes orders for its products, which are supplied to your company by vendors. You want to create a relational database of this information. Discuss the relationship between orders and products and between products and vendors. What tables would you create for this database and what would you include in these tables?
6) Suppose you are an HR (human resources) manager at a big university, and you sense that the university is becoming too top-heavy with full professors. That is, there do not seem to be as many younger professors at the assistant and associate levels as there ought to be. How could you study this problem with a simulation model, using current and/or proposed promotions, hiring, firing, and retirement policies?
