Reference no: EM132965394

Question 1:

A successful data analytics project is the combined result of the various software/tools used as well as the domain knowledge and reasoning skills of the data analyst.

Discuss the key message in the above statement based on your knowledge of "business analytics" in general. Support your discussion by providing specific examples from the application of the different techniques (supervised and unsupervised).

Question 2:

As a data analyst, you should perform data exploration, visualization, and pre-processing before proceeding directly to applying any high-powered predictive technique.

a) Using these techniques (i.e., decision tree, logistic regression, artificial neural network), explain how data exploration, visualization, and pre-processing steps impact your data analysis process and your potential findings from these techniques. Be sure to provide sufficient details and examples in your explanation.

b) Explain how your approaches and methods differ when applying data exploration, visualization, and pre-processing methods for numerical versus categorical variables in your data. Use specific and relevant examples in your explanation

Question 3:

One of the major challenges in data mining (machine learning) techniques is choosing the final model(s) and the best result(s). This is in contrast to traditional techniques such as linear programming and integer programming which often give you one best result if there is any feasible solution. Discuss the unique characteristics, advantages and disadvantages of data mining (machine learning) techniques in comparison to those traditional techniques such as linear programming and integer programming. Use related concepts as well as sufficient examples to expand on your discussion

Question 4:

You are approached by an admission and enrollment planning team to help them with the application of business analytics with student data. They are particularly interested in the application of cluster or segmentation analysis to understand the profiles of students and develop different strategies. Although they are aware of the benefit of this analysis, their knowledge is limited in terms of where they can start and how they can fully exploit it.

a) If you are to help them with an application of cluster analysis, what are the relevant attributes (variables) of student data you would be interested in? What are the potential challenges (constraints) in using student data? Discuss with sufficient examples

How does college exploit the potential findings of cluster analysis for its enrollment and retention strategies? Discuss with sufficient examples

Question 5:

The potential benefits of Business Analytics is not limited to mainstream profit-making sectors and businesses. Several public or government sector applications have been documented in practice.

a) Discuss with sufficient examples some of the opportunities and potential areas of business analytics applications in the public/government sector. What are the potential benefits?

b) Compared to the private sector, what do you think could be the unique challenges (constraints) for successful business analytics practices in the public/government sector?

Several questions that need to be answered and are theoretical which are related to deep knowledge and understanding of business analytics