Virtual Internship – Week 1

• Total Tasks Completed: 3
• Time Spent: 30 hours
• Tools Used: Microsoft Excel, MySQL Workbench, Jupyter Notebook

Overview of Tasks

• - Data Cleaning and Ad-hoc Analysis
• - Conversion of Dataset from Denormalized to Normalized Structure
• - Basics of Scrum

Task 1: Data Cleaning & Ad-hoc Analysis

The HR department wanted to analyze the Work From Home (WFH%) trends for employees. As a Data Analyst, my responsibility was to clean the dataset and provide actionable insights on WFH patterns.

Python (Pandas) Cleaning

Completed cleaning and transformations in a few lines of Python for efficiency.

• - Imported CSV into Jupyter Notebook.
• - Used commands such as:
  • - to_datetime() for date parsing
  • - .dt.strftime('%B') to extract month names
  • - .dt.dayofweek to map weekdays
  • - .str.rstrip() for removing trailing spaces

Excel Cleaning Process

• - Dropped duplicates
• - Standardized date formats
• - Removed unwanted special characters from IDs
• - Capitalized employee names and formatted them properly
• - Created a dynamic alias formula for employee status:
  =UPPER(LEFT(G2, 1) & IFERROR(MID(G2, FIND(" ", G2)+1, 1), " ") & IFERROR(MID(G2, FIND(" ", G2, FIND(" ", G2)+1)+1, 1), " "))
• - Dropped duplicates again after transformations
• - Added helper columns and built pivot tables for analysis

Task 2: Data Normalisation

Overview

• - One of client's dataset is currently in a de-normalized form, and they need our help to transform it into a normalized form.
• - My task was to create proper fact and dimension tables based on the dataset
• - I have imported fact_order_lines.csv to my local SQL workbench, and used below queries to create sepearate tables

Virtual Internship – Week 2

• Total Tasks Completed: 3
• Time Spent: 56 hours
• Tools Used: Power BI, SQL, Microsoft Excel

Overview of Tasks

• - Variance Analysis
• - SQL Query Debugging
• - Report Automation

Task 1: Variance Analysis