INMR95 Assessment Details
Section 1 – Descriptive Analytics
Introduction
Case study: Student satisfaction is a KPI for most, if not all higher education institutes. There are a range of reasons why students may or may not be satisfied with their courses. The Turkiye Student Evaluation Datasets gives us a small insight into the complexities that drive student experience; you have been hired by a company called HigherEdCo ltd. as a higher education consultant to perform. several multivariate analyses that will indicate the factors that impact student experience (according to the data collected). Your analysis should be approached critically, and variable as well as method selections should be justified. You MUST reduce the dimensionality of this dataset.
The dataset you will be working with is the Turkiye Student Evaluation Data Set (Gunduz & Fokue, 2013). The dataset is made up of the following variables:
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instr: Instructor's identifier; values taken from {1,2,3}
class: Course code (descriptor); values taken from {1-13}
repeat: Number of times the student is taking this course; values taken from {0,1,2,3,...}
attendance: Code of the level of attendance; values from {0, 1, 2, 3, 4}
difficulty: Level of difficulty of the course as perceived by the student; values taken from {1,2,3,4,5}
Q1: The semester course content, teaching method and evaluation system were provided at the start.
Q2: The course aims and objectives were clearly stated at the beginning of the period.
Q3: The course was worth the amount of credit assigned to it.
Q4: The course was taught according to the syllabus announced on the first day of class.
Q5: The class discussions, homework assignments, applications and studies were satisfactory.
Q6: The textbook and other courses resources were sufficient and up to date.
Q7: The course allowed field work, applications, laboratory, discussion and other studies.
Q8: The quizzes, assignments, projects and exams contributed to helping the learning.
Q9: I greatly enjoyed the class and was eager to actively participate during the lectures.
Q10: My initial expectations about the course were met at the end of the period or year.
Q11: The course was relevant and beneficial to my professional development.
Q12: The course helped me look at life and the world with a new perspective.
Q13: The Instructor's knowledge was relevant and up to date.
Q14: The Instructor came prepared for classes.
Q15: The Instructor taught in accordance with the announced lesson plan.
Q16: The Instructor was committed to the course and was understandable.
Q17: The Instructor arrived on time for classes.
Q18: The Instructor has a smooth and easy to follow delivery/speech.
Q19: The Instructor made effective use of class hours.
Q20: The Instructor explained the course and was eager to be helpful to students.
Q21: The Instructor demonstrated a positive approach to students.
Q22: The Instructor was open and respectful of the views of students about the course.
Q23: The Instructor encouraged participation in the course.
Q24: The Instructor gave relevant homework assignments/projects, and helped/guided students.
Q25: The Instructor responded to questions about the course inside and outside of the course.
Q26: The Instructor's evaluation system (midterm and final questions, projects, assignments, etc.) effectively measured the course objectives.
Q27: The Instructor provided solutions to exams and discussed them with students.
Q28: The Instructor treated all students in a right and objective manner.
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It’s up to you to choose your independent and dependent variable(s), as well as the tests you will run. However, everything you do needs to be justified, i.e., you need to explain why you chose to use that particular test, why you treated a certain variable as e.g., categorical, and why you transformed variables (if applicable). In short, a good project will critically analyse the results obtained and identify its limitations. The more detailed and exhaustive your analysis, the more likely you are to score a high grade (see marking scheme). Make sure to include figures and tables to support your findings.
Expected Project Output
In the end you need to submit a section with the following headings:
1. Introduction (briefly what your aim was for the analysis and your research question)
2. Process (what types of statistical testing did you use to answer the research question and the rationale for using said methods).
3. Results (the results of all the analyses, including figures, tables, and test outputs). The output needs to be written for an academic audience.
You must also submit:
· Your new csv file with any new variables you extracted/modified from the data. You must name this exploratory.csv
· Your R script. that shows, with comments, step by step the process you took to analyse the data. You must name this exploratory.R
· Anything else that you feel is relevant is welcome (but not required)
Section 2 – Predictive Analytics
Case study: You have been hired as a consultant to provide data-driven recommendations to the marketing department of the German-Hellenic bank. The bank has supplied you with anonymised data (the data we will be using has been supplied by Moro et al. (2014), and can be found in the UCI website).
Here is a list of the variables:
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Input variables:
# bank client data:
1 - age (numeric)
2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 – balance: Account balance
7 - housing: has housing loan? (categorical: 'no','yes','unknown')
8 - loan: has personal loan? (categorical: 'no','yes','unknown')
# related with the last contact of the current campaign:
9 - contact: contact communication type (categorical: 'cellular','telephone')
10 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
11 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
12 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
# other attributes:
13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
15 - previous: number of contacts performed before this campaign and for this client (numeric)
16 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
# social and economic context attributes
Output variable (desired target):
17 - y - has the client subscribed a term deposit? (binary: 'yes','no')
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Note that variable 12 should be discarded. The original data set has outcome variable 17 as the desired output. However, you do not necessarily need to focus on this variable. You are expected to explore other relationships in the data and present interesting findings to your client (hint: look at balance for example). You should build multiple models for comparisons, but present two final models on two different outcome variables. All actions taken need to be critically analysed and justified. The more detailed and exhaustive your analysis, the more likely you are to score a high grade (see marking scheme). Your outcome variables can be categorical, continuous, or a mix of both (i.e., one model as a classification model, one as a regression model).
Expected Project Output
In the end you need to submit a section with the following headings:
1. Introduction (briefly what your aim was for the analysis, along with your research question)
2. Process (what types of models did you use to answer the research question and the rationale for using said modelling techniques).
3. Results (the results of the analyses and the models, including model performance and model comparisons). All the output needs to be written for an academic audience.
You must also submit:
· Your new csv file with any new variables you extracted/modified from the data. You must name this analysis.csv
· Your R script. that shows, with comments, step by step the process you took to analyse the data. You must name this analysis.R
· A R shiny app or anything else you feel is relevant (optional)
Section 3 – Prescriptive Analytics
In this section you will form. data-driven recommendations using your findings from Section 1 and Section 2 for your two clients. You can expect your audience to be a layman audience with little to no understanding of statistics and modelling. Therefore, unlike the results is section 1 and 2, your report needs to be written in such a way that a layman audience can understand it. Ultimately you need to make a convincing argument that states how your client should proceed based on the results of your findings. You are expected to use a critical approach by using the results obtained to both generate recommendations and identify limitations. You can include an interactive Shiny R app, which is optional but will increase your likelihood of delivering a more robust solution.
Expected Project Output
In the end you need to submit a document with the following headings:
1. Client: HigherEdCo ltd. - Executive summary
2. Aims and Objectives
3. Analysis
4. Recommendations
5. Limitations
1. Client: German-Hellenic Bank. - Executive summary
2. Aims and Objectives
3. Analysis
4. Recommendations
5. Limitations
References
Gunduz, G. & Fokoue, E. (2013). UCI Machine Learning Repository [[https://archive.ics.uci.edu]]. Irvine, CA: University of California, School of Information and Computer Science.
S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014