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

Panyako Clinton — Sat, 29 Aug 2026 22:13:53 +0000

Data Analytics With Excel

Excel is a spreadsheet column developed by Microsoft. It is the most used tool for data analytics and it serves as a foundation for every data analyst's journey.
Excel is used for:

Analysis
Data entry
Data management
Accounting
Budgeting
Data analysis
Visuals and graphs
Programming
Financial modeling etc

Excel Interface overview

Major Parts of Excel Interface

Title Bar

It show the workbook name, the application name(Excel), window control buttons, minimize, maximize and close. It helps in identifying the type of file you're working on and mange the working space

The Ribbon

The command center for the excel containing tabs such as Home, Insert, Formulas, Data, Review and View. Most analysis tools are found here.

Home Tab

Mostly used for cleaning operations.
Tools contains include clipboard Group, Font Group, Alignment Group, Number Group, Styles Group, Editing Group.

Data Tab

Major tools for analysis and data cleaning
Tools contain includes Sort and Filter, Remove Duplicates, Data Validation, Text to Columns, Flash Fill, Refresh and Connections.

Worksheet

The main working space made up of cells arranged in rows (numbers) and columns (letters). This where the data is entered, structured, formatted and analyzed.

Formula Bar

It display the formula in the current sheet selected and allow editing of the formulas and text.

Column and Row Headers

Columns are labeled A, B, C, D across the top vertical while Row Headers are numbered 1,2,3,4.. down the left side horizontally.

Worksheet Tabs

They are located at the bottom of the workbook for example (Sheet1, Sheet2) that switch between spreadsheets within a workbook

Status Bar

Is located at the bottom of the Excel window and serves as always on mini dashboard. It show quick insights such as Sum, Average, and Count.

Analytical Best Practices

Preserve Raw data - Do not work directly on the raw data, always duplicate it first and keep the original as back up reference
Use Clear Naming - Consistent sheet naming for example Raw_data
Color-Code Sheets - Use tabs color for visual structure.
Separate Calculation from Preparation - This makes it easier to update analysis without breaking the reports.

Data Entry

Avoid Leading spaces, always check on hidden spaces
Maintain Consistent Formats, each column should contain one data type only.
Use Ctrl + Enter, Fill multiple selected cells at once with the same value.
Double-Click the Fill Handle, auto fill the formulas down the large dataset.

Freeze panes

To improve readability and navigations in a large dataset
For example

Freeze Top Row
Freeze First Column
Freeze Panes (custom)

Data Cleaning

Process of preparing raw data for analysis.
Issues commonly faced are:

Blank Rows, where entries are skipped.
Mixed Capitalization, different key entries.
Extra Spacing, caused manual Typing
Inconsistent phone formats, mixed hyphens, spaces or missing
Amount formatting issues, with words, commas, or numeric text.
Multiple data formats, based on each data entry setting.
Duplicate header rows and merged cells

Data Cleaning Steps

Sheet Preparation

Copy pasted the original data to a new sheet and rename it cleaned.
Assign a tab Color by right click and choose tab color and choose the color.
Freeze the Top Pane, for visibility
Auto fit All Columns -for full visibility of each cell.

Remove Irrelevant Elements
Blank rows, row 12.
Duplicate rows, row 6 and 15.
Remove missing Values, cell B8.
Covert phone number to Text, the phone number column to avoid dropping zero.
Standardize the date formats for consistency.
Text fields are text branch column to text.
Use TRIM() Function, to remove the extra spaces
Use PROPER() Function, to standardize the CustomerNames column
Bold Header row to distinguish field names from data.
Apply subtle fill color to headers
(light blue).
Use borders sparingly — only where they add clarity.
Ensure consistent alignment: Text left, Numbers right, Dates right
Apply number formatting: Currency, percentages, decimals
Adjust column widths so data is fully visible without excessive whitespace

Cleaned Dataset

Git Workflow

Panyako Clinton — Sun, 23 Aug 2026 13:22:22 +0000

Overview

Git is a version control system that helps developers to track changes made to files as system or project is being developed.
GitHub is a website where all the projects build in folders and files are put for collaboration or tracking purposes.
This overview show the steps of pushing codes, files and folders to github repository step by step.

Working Directory

Working directory is the project folder with all the files you are working on.
File can be modified or deleted but not saved on Git, changes are saved until you commit.

Staging (git add)

git add Puts changes made in the staging area
git add . stage all changes new, modified, and deleted files

Commit (git commit)

Saves staged changes to the local repository
_git commit -a -m "Message" _ to stage and commit all modified and deleted files in one step.

Push(git push)

git push Sends the commits to the remote repository e.g github

Checking Status(git satus)

git status used to check which files are staged, not staged or untacked.
It helps to keep track of what is needed to add or commit.

git status
git add analysis.py
git status git commit -m "Add sales analysis"
git push

conclusion

The git workflow work as a sequence.
Working Directory is where changes are made.
Staging Area it help to select changes to be included.
Commit records the staged changes in the local git history .
Push sends the commits to the git hub
Example of the workflow

Working Directory
git add
Staging Area
git commit
Local Repository
git push
GitHub

Example

git init, initialize the git repository to manage the project
git status, show the current happening on the repository
git add .,stages project ready for commit
git commit -m "Done"
git log, view commit
git log --oneline, shows project history
git branch, confirms the branch
git branch -M main, change branch from master to main
git remote add origin "ssh GitHub link", connect local repository to the GitHub repository.
git remote -v, check, check the remote repository connected to local project
git push -u origin main, uploads locals commits to GitHub

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