Getting Started with Excel for Data Analytics: From Basics to Data Cleaning.

vivian wawira — Tue, 08 Sep 2026 20:49:20 +0000

Introduction to excel

Microsoft Excel is a spreadsheet that allows one to collect, organize, analyze ,calculate and visualize data in a more efficient manner. When you open excel the first thing you see is a toolbar known as a ribbon, it contains toolbars with various options that helps to make changes on any data presented on the worksheet

Excel workbook

The excel work book has a worksheet that is made of rows and columns, the rows run horizontally and are numbered(1,2,3...) while the columns run vertically and are named with letters (A,B,B...).On an excel workbook there are several work sheet that you can work from and your required to name each worksheet for easier reference.

Data Cleaning

Data cleaning is an essential step in data analytics. Before any data is analyzed data cleaning must be done to ensure data is accurate and reliable. Data cleaning involves commands like filter, sort, remove duplicates, correct UPPER case, Lower case or Proper case, Aggregating etc. This clean data helps create correct pivot chart and tables and dashboards for better decision making.

common dirty data that could be found when data cleaning

1. Duplicates
Duplicate rows that repeat the same employee.
Fix: Select all data → Go to Data tab → Click Remove Duplicates.

2. Irregular Capitalization
Names and departments written in different cases like NAIROBI, nairobi, Nairobi.
Fix: Use =PROPER() for names or =UPPER() / =LOWER() to make it consistent.

3. Missing Data / Blanks
Some cells were empty, e.g., blank Department or Salary.

4. Inconsistency in Text
Same location or job written differently:

Locations: nainobbi, Nairobi, NAIROBI
Jobs: waitre, waitress, watrss Fix: Use Find & Replace (Ctrl + H) or clean with TRIM() and PROPER().

Conclusion