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. 1. 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 Understanding the dataset structure matters because it determines what types of analysis you can perform later. 2. Inspecting the Dataset before Cleaning A common mistake we make when working with data is changing values immediately without first understanding the dataset. The first step should always be data profiling for a data analyst. I inspected the HR dataset to determine its size, column types, missing values, duplicate records, and inconsistent entries by using the filter function. The dataset contains 876 rows and 21 columns . It also contains missing values across several fields. In total, there are approximately 291 missing cells . For example, the Employee ID column contains missing values, while fields such as Hire Date, Gender, Bonus, Department, and Annual Training Hours also contain incomplete records. For example, some department names may appear as "HR", "H.R", "Human Resources", or "Human Resource". Although these values refer to the same department, Excel treats them as different text values. Similarly, office locations contain variations such as_ "London", "Londn", "San Francisco", "San Fransisco", and "SF" . These needed cleaning for the department; I retained “HR” and sorted the location, for example, _“Londn” to “London” .