# 1.1 Assignment

course: Academy — 65-GENAI-for-Engineers
module: Academy/65-GENAI-for-Engineers
type: pdf
source_url: https://personal-learn.armco.dev/files/Academy/65-GENAI-for-Engineers/assignments/1.1_Assignment__Understanding_Tabular_Data/1.1_Assignment.pdf
pages: 1

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1.1 Assignment Questions 
 Short Answer / Conceptual Questions 
 1.  Explain the difference between rows and columns in tabular data with a real-life 
 example. 
 2.  List three common data types found in tabular data and describe their importance. 
 3.  Why is it important to have a header row in tabular datasets? 
 4.  How can understanding the structure of tabular data improve data analysis? 
 Hands-On / Practical Tasks 
 5.  Create a simple tabular dataset (minimum 5 rows, 4 columns) to store employee 
 information: EmployeeID, Name, Department, and Salary. Represent it using either a 
 table format (Excel, Google Sheets) or using Python’s Pandas DataFrame. 
 6.  Download any open dataset in CSV format (e.g., from Kaggle or data.gov) and answer 
 the following: 
 a. How many rows and columns are there? 
 b. What types of data does each column contain? 
 c. Are there any columns that can serve as a unique identifier? 
 7.  Using Python and Pandas, write a script to: 
 a. Load a CSV file 
 b. Display the first 5 rows 
 c. Show column names and data types 
 d. Identify and count any missing values in the dataset