Introduction
Microsoft Excel is the most popular tool for data work and analysis. Its user-friendly environment makes it easy to understand, organize, clean, and explore data.
For a data analyst or scientist, knowing how to work with Excel goes beyond entering information into cells. Excel can be used to inspect datasets, identify errors, standardize values, filter records, sort information, remove duplicates, and prepare structured data for further analysis; in other words, clean data to draw meaningful insights from it.
This article demonstrates the key Excel concepts covered during Week 1 from the Human Resources dataset. The dataset contains employee information such as employee ID, department, salary, hire date, age, gender, performance score, employment type, office location, project count, training hours, and manager feedback score.
The original dataset contains 876 employee records and 21 columns. It is intentionally named "dirty", meaning that it contains data that an analyst should be able to clean to be able to use for insights and analysis.
- Understanding the Excel Environment
The first step in working with Excel for analytics is understanding how information is organized.
An Excel worksheet consists of:
Rows, which normally represent individual observations or records.
Columns, which represent variables or attributes.
Cells, which contain individual values.
Worksheets, which allow related tables to be organized within the same workbook.
In the HR dataset, one row represents an employee record while each column represents an employee attribute.
For example, the columns include:
| Column | Description |
|---|---|
| Employee ID | Unique identifier for an employee |
| First Name | Employee's first name |
| Last Name | Employee's surname |
This article was originally published by DEV Community and written by sam manox.
Read original article on DEV Community