1.

(10 points)

Find an article (or related articles) referencing a situation where Business Intelligence/

University Network (TUN), from the Information Management website, from another trade or news

publication, or any other source.

Summarize the scenario in 2-3 paragraphs, including a characterization of the analytics employed in

the scenario as descriptive, predictive, or prescriptive (and why).

Note that this would be an excellent opportunity to identify and study applications of BI/BA/Data

Mining in an area where you might be interested in pursuing a semester project. Identifying what

has been done in an area related to your interests is an excellent way to begin your project

research.

2.

(20 points)

a. How many cases/observations are in the dataset? How many variables?

b. Classify each of the variables as either categorical or numeric. If necessary, justify your

c. Discuss data cleaning steps that would be necessary for this data, suggesting methods for

dealing with the anomalies you identify in the data.

d. Calculate the descriptive statistics for the numeric variables (ignoring missing data) using the

Excel Analysis ToolPak. Include the resulting table with your homework.

e. Which measures of central tendency and dispersion would you recommend to characterize the

different numeric variables?

f.

Which of the variables has the greatest skewness? Is it right or left skewed?

g. Looking at the analysis results (i.e., without graphing the data), which of the variables seems

most symmetric? Why?

h. Which variable has the greatest kurtosis? What does this mean?

3.

(20 points)

Using a visualization tool of your choice (e.g., Microsoft Power BI, Tableau, Qlik, etc.), create four

basic graphics using the Titanic Passengers_new data set. You may choose bar charts, line graphs,

scatterplots, maps, etc. – use at least two types. Write up a paragraph explaining each

chart/graph. Indicate what question the visualization answers, and what additional insights you

gained from looking at the chart/graph vs. examining the data itself. These charts/graphs do not

have to be complex, but you should use good visualization principles.