Analysis of Alumni Career Choices: A Comparative Study of Two Universities

Analysis of Alumni Career Choices: A Comparative Study of Two Universities

Introduction:

The purpose of this project is to analyze the career choices of alumni from two universities, referred to as College A and College B. The career growth and choices of alumni play a vital role in an institute's ranking and networking activities. By examining the alumni records, including higher studies, self-employment, and service/job records, we aim to gain insights into the career paths pursued by the alumni. This documentation provides a step-by-step guide on how to perform the analysis using MySQL and Python.

Dataset:

The dataset consists of six CSV files:

• College_A_HS.csv: Higher Studies Record of College A
• College_A_SE.csv: Self Employed Record of College A
• College_A_SJ.csv: Service/Job Record of College A
• College_B_HS.csv: Higher Studies Record of College B
• College_B_SE.csv: Self Employed Record of College B
• College_B_SJ.csv: Service/Job Record of College B

Database Setup:

• Create a new schema called "alumni" in MySQL to store the data.
• Import the CSV files into MySQL using appropriate commands.

SQL Commands:

• Use SQL commands to view the structure of the six tables.
• Display the first 1000 rows of the tables: College_A_HS, College_A_SE, College_A_SJ, College_B_HS, College_B_SE, College_B_SJ using Python.

Importing Data into MS Excel:

• Import the first 1500 rows of the tables: College_A_HS, College_A_SE, College_A_SJ, College_B_HS, College_B_SE, College_B_SJ into MS Excel for further analysis.

Data Cleaning:

• Perform data cleaning on each table to remove null values and create views for each table:
  • College_A_HS_V: Cleaned data for College A Higher Studies
  • College_A_SE_V: Cleaned data for College A Self Employed
  • College_A_SJ_V: Cleaned data for College A Service/Job
  • College_B_HS_V: Cleaned data for College B Higher Studies
  • College_B_SE_V: Cleaned data for College B Self Employed
  • College_B_SJ_V: Cleaned data for College B Service/Job

String Function Conversion:

• Create a procedure to convert the records of Name, FatherName, and MotherName columns into lowercase for each view.

Importing Views into MS Excel:

• Import the created views (College_A_HS_V, College_A_SE_V, College_A_SJ_V, College_B_HS_V, College_B_SE_V, College_B_SJ_V) into MS Excel and create a pivot chart to visualize the location of alumni.

Procedures to Fetch Names:

• Create two procedures using cursors to fetch names of all students from College A and College B:
  • get_name_collegeA
  • get_name_collegeB

Calculation of Percentage:

• Calculate the percentage of career choices (Higher Studies, Self Employed, and Service/Job) for alumni of College A and College B.a.

Conclusion:

By analyzing the career choices of alumni from two universities, this project provides valuable insights into the career paths pursued by the graduates. The documentation outlines the steps to import, clean, and analyze the alumni data using MySQL and MS Excel. The calculated percentages of career choices provide a comparative overview of the alumni's professional trajectories in both universities.