Part 1
1. Explain the differences between DSS, BI, and BI Analytics application types.
2. Explain the difference between descriptive analytics, predictive analytics and prescriptive analytics and provide one example of each type from the reading material.
Part 2
1. What are the use cases for Big Data/Hadoop and data warehousing/RDBMS?
2. In the era of Big Data, are we about to witness the end of data warehousing? Why?
Part 3
1. Differentiate between operational and decision support data. Distinguish between fact table attributes and dimension table attributes (for example, measures and descriptive fields).
2. Explain the concept of a star schema. Summarize the advantages of implementing a star schema in data warehouse design. Provide an example that provides the following: 1 fact table, 3 dimension tables, and attributes in all tables.
Part 4
1. Explain the standardized data mining processes and the steps involved in data pre-processing for data mining.
2. In your own words, please describe your concerns regarding the Big Data movement in terms of privacy issues (e.g. what is okay and what is not okay).
Part 5
1. How can sentiment analysis be used in predicting financial markets?
2. Describe the relationships between Web analytics, text mining, and sentiment analysis?
Part 6
1. List and briefly discuss the three major components of linear programming.
2. Compare and contrast several common optimization models.
Part 7
1. Explain the benefits and limitations of rule-based expert systems.
2. When would you not use an expert system?