DEV Community: David Gitonga

Building an Interactive Excel Dashboard for E-commerce Product Analysis , A Jumia case Study

David Gitonga — Sat, 05 Sep 2026 14:19:46 +0000

Introduction

For my first data analytics project, I have had the chance to work with a Jumia products dataset and used Excel to turn the raw data into something that could actually be used to understand product performance.

The main goal of the project was not just to clean it and make a pretty dashboard. I wanted to use the data to answer some simple questions about product pricing, discounts, ratings and reviews.

some of the questions answered within the project were:

Do products with bigger discounts get more reviews?
Do highly rated products get more engagements?
Is there a relationship between price and rating?
Which products have the highest rating and reviews?
which products might need better pricing or marketing strategies? This project also gave me a chance to practice some of the basic data analytics skill I have been learning, especially data cleaning, Excel formulas, PivotTables, Charts and Dashboard creation

THE DATASET

The dataset contains products information from Jumia. The main columns I worked with were:

Product
Current price
Old price
Discount
Review
Rating The data was not completely ready for analysis, so I first had to inspect it and make some changes before building the dashboard

One important thing I kept in mind is that reviews are being used as a measure of engagement, not as actual sales data. A product having more reviews does not automatically mean its more products.

The Raw Data

I started by creating a Raw_Data sheet.

I kept this sheet as the original version of the dataset so that I could always go back to the starting point if something went wrong during cleaning.

While checking the data, I noticed some issues such as duplicate records, price stored with currency text, price range and some missing reviews and rating values.

I also found some products that appeared more than once with exactly the same information.

Cleaning the Data

After checkinh the raw dat, I created a Cleaned_Data sheet.
The main cleaning steps include:

Removing exact duplicate records. -Working with the price fields so the be used for calculations
Making sure discount values could be used in calculation
Checking rating and review values
Keeping missing values blank instead of automatically treating them as zero

I also added some calculated fields that would help with the analysis.

One of them was Discount Amount,which shows the difference between the old price and current price.
For example:
=old price-Current Price
I also used categories to make some of the analysis easier to understand, such as rating categories, discount categories and price categories.

Using Excel for the Analysis

once the data was prepared, i moved on to the analysis section.
I calculated some of the basics statistics such as:

Total number of products
Average current price
Average old price
Average discount
Average rating
Total reviews
Highest product price
Lowest product price I also used correlation to look at the relationship between some of the variables. which I looked at: Discount vs Reviews Rating vs Reviews Current Price vs Rating

The purpose was to see whether there was a noticeable relationship between these variables.
However, correlation doesn't mean that one variable caused another. It only helps show whether two variables tend to move together.

PivotTables and Charts

After the calculation, I created PivoTables to make it easier to summarize the data.
Some of the PivotTables looked at:

Rating categories
Discount categories
Price categories
Reviews by discount category
Top products by rating
Top products by reviews

I then used these results to create charts.

Building the Dashboard

The final step was putting the important information into or dashboard.
I included KPI sections for things like:

Total Products
Average Price
Average Discount
Average Rating
Total Reviews

I also included charts for the different relationships I investigated and tables showing some of top-performing products.

I added slicers so that the dashboard could be filtered by categories such as Rating category, Discount Category and price category.

Conclusion

Overall, this project was a good way for me to practice the basic data analytics process using Excel.

I started with raw Jumia product data, Cleaned and prepared it, analyzed the information, created PivotTables and finally build an interactive dashboard.

This project also showed me that even a relatively simple dataset can provide useful insights when it is properly when it is properly organized and analyzed.

This is one of my first steps in building my data analytics portfolio, and I plan to continue improving my skills by working on more projects using Excel, SQL, Python and other data analytics tools.

My GitHub project

The complete project, including the dataset, Excel dashboard and project documentation, is available in my GitHub repository. [https://github.com/gitosh-star/JUMIA_PRODUCTS_PERFOMANCE_DASHBOARD]

GETTING STARTED WITH EXCEL FOR DATA ANALYTICS: FROM BASICS TO DATA CLEANING

David Gitonga — Sat, 29 Aug 2026 18:32:41 +0000

EXCEL OVERVIEW

What is Excel? These is a spreadsheet tool for organizing, analyzing, calculating, and visualizing data. Wiledy used for personal and professional data management.

EXCEL INTERFACE

This is an Excel workbook that usually subdivided into work sheets

Ribbon: Toolbar with commands(Home, insert, data..)
Quick Access Toolbar: save, undo, redo.
Formula Bar: displays cell content
Rows, columns, cells.

Data validation

The restrictions on the type of data user can input into a cell. (Dropdown lists, Numbers , ranges, date limits, text length)

THE DATA CLEANING

Data cleaning is the process where you prepare raw datasets so they are accurate, consistent, and ready for analysis. This is a very crucial set because one gets to correct small errors , duplicates which may lead to misleading results in the formulas. Clean data ensures reliability and save time when performing deeper analysis.

Raw uncleaned data

Cleaned data

DATA CLEANING TECHNIQUES

Removing duplicates: Prevents inflated results in analysis.
Mombasa
Mombasa

Standardizing text this ensures consistency

NAIROBI
NairoBi

checking dates: corrects inconsistent format and invalid entries, fixing words format:

use PROPER() to standardize capitalization in names.

DATA FORMARTTING & CLEANING

Number Formatting

this changes how numbers display without altering their actual value Conditional formatting
This highlights cells automatically based on trends or criteria

CONCLUTION

In conclusion data cleaning in excel is more the a technical step, it is the foundation of reliable analysis. This is a effective data cleaning turns Excel from a simple spreadsheet tool into a powerful engine for analytics.

Workflow Flow From Local Folder To GitHub

David Gitonga — Fri, 21 Aug 2026 19:40:00 +0000

THE GITHUB PROJECT

Imagine while creating your repository on GitHub, you remember that you have to include your excel file or any file that is all the way tacked in your folders and you want to use Git. How do you go about it.

GIT

How does Git work;

It use Linux , where you use SSH (Secure shell) which let you opens an encrypted terminal session to a remote machine in our case GitHub.

THIS IS THE TYPICAL WORKFLOW FROM START TO FINISH

Get Git bash first
Great now open
we need a local folder in our Git Bash we run the following commands;

- ssh - to create a remote login
- ssh-keygen -t <> -c <adress> - with the correct server_address
- ls -al ~/.shh- check if you have added the new sshkey
- ls - which is listing the files and  directories in the active directory
- cd - which opens a directory
- mkdir <name> - make a new directory
- touch file.text - to create an empty file
- nano file.text - write a comment or context of the data use in place or -echo-
{CREAT REPOSITORY ON GITHUB COPY SSH}
- git remote add origin <ssh_key> - link to the remote server
- git commit - commit the link
- git status - check the branch
- git fetch - download a latest change 
- git push - to GitHub

CONCLUTION

Git is easy once you understand the commands.
Refresh your GitHub and watch as you have connected.

Onboarding Session At Lux DevHq

David Gitonga — Tue, 11 Aug 2026 12:10:02 +0000

Introduction to lux dev

This are all the things I've learned today about how to write articles it's roles and how to ensure you write a good article
asterisks for bold
dash for italics

greater than for quotes

line of code

SELECT * FROM Cohort9,application;

writing a block of code

SELECT student name_
use three up commas for a block of code

photo session

how to upload select from the upload icon, devto converts it to marklaguage

how to add a URL link

This is the link to the luxdev git hub link
link:luxdevHQ