Data Analytics After-Sales As an After-Sales Analyst, you will be confronted daily with many databases and systems related to the study, supervision, administration, etc. of the students. It is important for our service departments to understand who the student is and who contacts us to be able to deliver the best possible support. The goal is to always keep an eye on customer satisfaction and to understand what it largely depends on. This is what you will explore in this case study. Data sets You have 3 records in the form of csv files.

Task 1 Analyze the data with Python! Important KPIs in our field are: Study progress, contact rates and cancellation rates (they all affect customer satisfaction). Here are some sample questions you can ask yourself and answer with the data:

• Are there differences in study progress (ECTS earned) between study programmes?
• Which age group has the best study progression?
• Are women more likely to quit than men?
• Do emails have an influence on cancellations?
• And expand as you like :-) Feel free to define the KPIs as you consider appropriate and logical. Task 2 The team leader of student service department needs a new dashboard to monitor the daily performance of their team. Please provide the team leader with a first version of the dashboard. Use Power BI for this task.

1. Mails

Most of the Students only send mail once, either we are resolving their issue or they are not mailing again due to our services

Many students have mailed 15 number of mail in large quantity

Month over month

There is a trend of decrease in the mails over time

day of the mails

less mails are sent on monday tuesday ,most on friday

more mails are sent by people of age 30

1. Termination over time

Most termination occur in 2019 , which is decreasing over time

Note : as it has occured in march 2019 , it could be due to high correlation with covid-19 , but that will need extra data to analysis

Termination analysis

Personal Management course and DS has most termination percentage majorly in age 23-33

although more have more cancellation but that is also they have applied more

Field

Data correction : many course have termination date of time before the start

Mails

Many people still send us mail after their course termination, almost as much as when they are with us

Termination day

Most termination day is Wednesday ,Thursday

1. Age analysis

in Data science where we have 35-44 aged people more than people less than 25

while personal management course have higher people with age less than 25

Average age of Logistic management is much higher than other course , shows experienced people are most interested in it than younger people

Age wise best field

Power bi report

https://drive.google.com/file/d/1WrxdCqQyNF2qrLfDA40DuAr_YG8HfItj/view?usp=sharing