DEV Community: Godfrey Babu

Introduction to Ms Excel for Data Analytics

Godfrey Babu — Thu, 12 Mar 2026 16:53:27 +0000

Microsoft Excel is a powerful tool for data analysis, which is perfect for beginners who want to start their journey into data analytics. This guide will take you through the fundamentals of Excel.

What is Data Analytics?

Data analytics is the process of cleaning, transforming, and modeling data to discover useful information and support decision making.

Getting Started With Excel for Data Analysis

1. Understanding the basics of Excel

Workbook: The entire Excel file
Worksheet: Individual tabs within a workbook
Cells: Individual data containers organized in rows and columns
Ribbon: The menu system containing all tools and functions

Essential Excel Features for Data Analytics

2. Data cleaning and preparation

Removing Duplicates

Go to Data -> Remove Duplicates
Choose which columns to check for duplicates

3. Basic Data Analysis

SUM Function

=SUM(A1:A10)

AVERAGE Function

=AVERAGE(B1:B50)

COUNT and COUNTIF Functions

=COUNT(C1:C100)        // Counts cells with numbers
=COUNTIF(D1:D100,">50") // Counts cells meeting criteria

MIN and MAX Function

=MIN(A1:A10)  // Finds smallest value
=MAX(A1:A10)  // Finds largest value

4. Sorting and Filtering Data

Sorting Data

Select your data range
Then go to sort
Given that you already chose a column sort (ascending/ descending)

Filtering Data

Select your data
Then go to data on your ribbon
Select filter
Use dropdown arrows in headers to filter

5. Creating a PivotTable

Select the required data
Go to Insert then select PivotTable
Place the PivotTable on anew sheet
Select the different areas how you want your table to show (Rows, Columns, Values, and Filters)

6. Conditional Formatting for quick insight
Select the specific column that you want to have a breakdown of t, then go to home on your ribbon the select conditional formatting and choose how you want the data in the column to be simplified.
You can choose the formatting rules like changing the colour of data distribution

7. Creating Pivot Charts
After creating a PivotTable select the table then go to Insert the go to pivot charts choose the type of chart you want (Column chart, Line chart, Pie chart)

Introduction to Ms Excel for Data Analytics

Godfrey Babu — Sun, 01 Feb 2026 23:30:45 +0000

Microsoft Excel is a powerful tool for data analysis, which is perfect for beginners who want to start their journey into data analytics. This guide will take you through the fundamentals of Excel.

What is Data Analytics?

Data analytics is the process of cleaning, transforming, and modeling data to discover useful information and support decision making.

Getting Started With Excel for Data Analysis

1. Understanding the basics of Excel

Workbook: The entire Excel file
Worksheet: Individual tabs within a workbook
Cells: Individual data containers organized in rows and columns
Ribbon: The menu system containing all tools and functions

Essential Excel Features for Data Analytics

2. Data cleaning and preparation

Removing Duplicates

Go to Data -> Remove Duplicates
Choose which columns to check for duplicates

3. Basic Data Analysis

SUM Function

=SUM(A1:A10)

AVERAGE Function

=AVERAGE(B1:B50)

COUNT and COUNTIF Functions

=COUNT(C1:C100)        // Counts cells with numbers
=COUNTIF(D1:D100,">50") // Counts cells meeting criteria

MIN and MAX Function

=MIN(A1:A10)  // Finds smallest value
=MAX(A1:A10)  // Finds largest value

4. Sorting and Filtering Data

Sorting Data

Select your data range
Then go to sort
Given that you already chose a column sort (ascending/ descending)

Filtering Data

Select your data
Then go to data on your ribbon
Select filter
Use dropdown arrows in headers to filter

5. Creating a PivotTable

Select the required data
Go to Insert then select PivotTable
Place the PivotTable on anew sheet
Select the different areas how you want your table to show (Rows, Columns, Values, and Filters)

7. Creating Pivot Charts
After creating a PivotTable select the table then go to Insert the go to pivot charts choose the type of chart you want (Column chart, Line chart, Pie chart)

GIT AND GITHUB FOR BEGINNERS:HOW TO TRACK CHANGES,PUSH AND PULL CODE

Godfrey Babu — Sun, 18 Jan 2026 09:32:40 +0000

For a data engineering and data science student knowing about git and github is mandatory. Git helps us collaborate with others who we share common interests with. It also helps us track changes in you code and avoid losing your work.

What is git

Git is an open-source version control system that tracks changes made to your files over time, allowing you to go back to a previous version in case of any mistake

What is github

Github is a cloud-based platform where developers store, manage and share their software code.

step 1:Install git bash

Install git
Run installer
Finish installation

verify installation

git --version

Step 2:Setup git

git config --global user.name "Your Name"
git config --global user.email "your_email@example.com"

Confirm configuration

git config --list

Step 3.Connect git bash and github through SSH key

ssh-add ~/.ssh/id_ed25519

Add SSH key to github

Go to Github
Go to settings
Click SSH key
Click new SSH key
Paste the key and save

Test connection

ssh -T git@github.com

Step 5.Clone repository

git-ssh-practice.git

Go to project folder

cd git-ssh-practice

Step 6.chek changes with git

Check status:

git status

Add files

git add .

Step 7. Push code to GitHub

Connect local repository to GitHub

git remote add origin https://github.com/username/repository-name.git

Push code

git push -u origin main

Now your code is active on GitHub

Step 8. Pull code from GitHub

This is advisable while working with multiple machines or while working with a team.

git pull origin main

This collects latest changes and updates your local files.