The Problem Setting The setting is a privately run Business School outside the United States that offers a full time MBA program. Students enter the program, stay full time for two years and then graduate. With increased demand for MBAs, such schools have sprung up in many regions of the world, particularly in Asian/Southeast Asian countries such as China, Korea, India and Malaysia. Our setting is drawn from one such country. Next year’s admission is coming up, and the Chancellor of the School wants to ensure that the school admits the ‘right’ set of students to its MBA program. But she is not sure about the criteria that should be used to identify the right set of students for this program because there are multiple objectives as described below. The school receives applications for its MBA program from around the country in which it is located and admits approximately 350 students every year. There has been a steady increase in the number of applications received by this school over the years due to its rising reputation and it has reached a stage where it can be quite selective in choosing the students admitted to the MBA program. Reputation of a B-School is always important but is even more critical for this one as it is private and heavily reliant on tuition. The most important driver of reputation is placement. Placement has two dimensions, the first being whether a student was placed. The school has a well-staffed placement office which serves as a broker between campus recruiters and the students. Ideally, the school would like to see all graduating students placed through this campus recruiting activity at the time they graduate. But that never happens. Each year, a proportion of the students are not placed at graduation time. The second dimension of placement is the salary at which a student is placed. Between the two dimensions of placement, the first is much more consequential for reputation. This is because while salaries vary by company and position, the companies that signup to come to campus for recruiting are usually well established strong companies that generally offer good salaries. So, the percentage of graduating students placed, followed by average starting salary, are important bragging rights used by B-Schools within the country to compete against each other. Although there is no penalty if a (ultimately) non-placeable student is admitted, it would weigh negatively on the institution’s reputation. A wrong pick at admission time could eventually contribute toward an increase in the number of unplaced students as well as a reduction in the median salary. On the other hand, there is the possibility of rejecting a placeable candidate and hurting both the reputation and the revenues of the school. While placement is critical, there is another desirable goal in the admission process. The school would like to improve diversity in its student population to enrich the learning experience. Of course, all this must be done with revenues in mind. Too strict an admissions filter would choke off revenues. 2 Dataset.
The admissions committee at this BSchool collected data on the students who had been admitted in 2019 and graduated in 2021.
The data are provided in the attached MS Excel file. The variables in the dataset are organized in the sequence of a student’s natural progression as follows: HighSchooldegree → UG Degree → Job Experience (if any) → BSchool Entrance Test → MBA program → Placement → Salary.
Further details of this progression are as follows. In this country, there are multiple boards that administer high school curricula and exams, and each high school is affiliated with one of these boards. Moreover, once they get to high school, students must choose one of three broad streams – Science, Arts or Commerce. The boards specify the curricula to be followed in each of these areas and schools must comply. So, a student’s high school certificate notes not only their numeric performance, but also the name of the board under which that certificate was obtained, and their stream. Two of these boards, Board1 and Board2, are much more reputed than the other ones. After completing their high school, students get an undergraduate degree in some stream such as engineering or commerce. Thereafter, a student may or may not work for a few years before they apply to the MBA program. When they apply, they must take an entrance exam. After being admitted to the MBA program, they specialize in some area such as finance, take courses, and then go through the placement activity. Hopefully, they are placed, but not all are. The marks obtained in three important required MBA courses are also recorded in the dataset. A description of all the variables in the dataset are provided at the end of this document.
YOUR TASK You are required to conduct an analysis of the data and write a report to the Chancellor with your recommendations on the profile of students to admit. The focus of your recommendation is to create a profile of applicants with the highest placement potential – i.e. whether they are placed at graduation (the first dimension of placement mentioned above). However, in making your recommendation, you must also consider the two other issues that are important to the school – i.e. starting salary and diversity – and incorporate them into your recommendation. To back up your recommendations, you will build appropriate RapidMiner processes to analyze the given dataset. Details regarding the contents and structure of the report are given below.
THIS STEP MUST BE FOLLOWED PRECISELY: The link for the final exam on Blackboard is unique to each student. Therefore, the dataset that you see on Blackboard is unique to you. You must import the Excel datafile as given to you, into RapidMiner, without doing any transformations or editing (e.g. numeric to polynominal, dropping columns etc.) during the import process. There are no missing values in the data. Any transformation or preprocessing of the data that you feel is necessary, must be done using RM operators as part of an RM process after you import the file. Otherwise, I will not be able to run your rmp files. The report must contain the following sections [You may certainly write less than the limit, but violation of word limits will incur penalty. The limits are generous. You should not need to use it all.]:
1. Data Exploration and Preprocessing [ 20 points] Explore the characteristics of the different variables in the dataset and discuss any preprocessing that needs to be done. Using appropriate RM output, briefly justify why these preprocessing steps need to be done. If you conclude that no preprocessing needs to be done, justify why you came to that conclusion using appropriate RM output. [no more than 250 words, excluding any tables/graphs]
2. Data Analysis [40 points] You are free to use whatever method you feel is appropriate to analyze your data for the given problem setting. You may use more than one method if you feel that it aids your analysis. For each method that you have used (in case you used more than one) provide (i) a screen shot of the RM process model (ii) the name of the corresponding RMP file – you should name it as Finallastname-method (E.g. Final-Ricardo-LogisticRegression) (iii) a list of the variables from the dataset that are used in the model (iv) a discussion of why you used the model and what it tells you about the profile of students who should be admitted [no more than 250 words/method excluding tables/figures]. Support your summary with appropriate screen shots of RM output.
3. Recommendation to Chancellor [40 points] Your recommendation to the Chancellor (no more than 500 words) on the profile of students who should be admitted the coming year. Use your results from [2] to justify your recommendation. Since there are multiple objectives of the admissions, your recommendations should clearly identify what, if any, tradeoffs you have made among the different objectives.
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