Johnson Modinat — Data Analyst

About Me

The Story Behind
the Data

By the Numbers

Projects

$32M+

Revenue Insights

10K+

Rows Analyzed

Happy Clients

It started with curiosity.

I used to watch people analyze datasets, break down numbers, and pull out insights that actually made sense. I kept wondering how they were doing that. That curiosity got the better of me, so I signed up for a program to find out.

What I found was something I genuinely love. Cleaning messy data, exploring patterns, building dashboards that turn chaos into clarity — it all clicked. Today, I work with Python, SQL, Excel, and Power BI to help businesses make smarter decisions, whether that's tracking sales performance, understanding customer behavior, or simplifying complex datasets into insights anyone can act on.

I'm naturally curious, detail-oriented, and self-driven. I take my work seriously, stay organized, and I'm always open to learning new tools or refining my approach. What began as a simple desire to understand has grown into a career I'm intentionally and excitedly building for the long term.

If you have data that needs to make sense, let's figure it out together.

Book a Call See My Work

What I Do

Services

From raw, messy data to clear, actionable insights. Here's how I can help your business.

🧹

Data Cleaning and Preparation

Transform messy, unstructured datasets into clean, reliable data ready for analysis. I handle duplicates, missing values, inconsistencies, and formatting issues with precision.

🔍

Exploratory Data Analysis

Uncover hidden patterns, trends, and relationships in your data. I ask the right questions and let the numbers tell their story through rigorous EDA using Python and SQL.

📊

Interactive Dashboards

Build powerful, interactive Power BI and Excel dashboards that give your team real-time visibility into KPIs, performance trends, and business metrics.

📈

Business Reporting

Create clear, actionable reports that translate complex data into plain language. From sales performance to customer analysis, delivered with practical recommendations.

👥

Customer and Sales Analysis

Analyze customer behavior, segment your audience, and identify the drivers of your sales performance to power smarter marketing and growth strategies.

🎨

Data Visualization and Storytelling

Design compelling charts and visuals that communicate insights clearly to both technical and non-technical audiences. Data should tell a story and I make sure it does.

Sound Familiar?

Your Data Challenges,
Solved.

If any of these feel familiar, you're in the right place.

😩

Your reports take hours every week

You or your team spend valuable time manually compiling data instead of using it to make decisions. The process is slow, repetitive, and error-prone.

🤷

You have data but don't know what it means

Your spreadsheets are full of numbers but you can't clearly answer questions like "what's working?" or "where should we focus?" The data exists but offers no direction.

📉

Decisions are based on gut feeling, not data

Without reliable insights, your team defaults to guesswork. This leads to missed opportunities, wasted budgets, and strategies that don't reflect what your customers actually need.

Here's what working with me looks like

✅

Reports that run themselves

I build dashboards that replace hours of manual reporting with real-time, automated visuals. Your team gets answers in minutes, not hours.

✅

Insights in plain language

I don't just hand you charts. I explain what the data is telling you, in language your whole team can understand and act on immediately.

✅

Data-backed decisions, every time

From customer segmentation to sales trends, I give you the clarity to make confident, strategic decisions grounded in real evidence.

Case Studies

Projects

Four real-world data projects showcasing end-to-end analytical thinking, from messy data to meaningful insight.

Microsoft Excel

1 / 3

ExcelPivot TablesDashboardPower Query

Pharmaceutical Sales Analysis

The Problem

The company needed to evaluate sales performance across distributors, regions, and product lines to identify revenue drivers and improve operational efficiency. Without clear insights, management could not identify which products were performing, which markets were strongest, or how sales were trending over time. The dataset itself was messy — with null values, mixed formats, inconsistent customer names, and duplicate entries that made it unreliable for reporting.

What I Did

Before any analysis, I performed thorough data cleaning in Excel:

Removed duplicates to ensure accurate totals
Standardized the month and country columns from mixed abbreviations and numeric formats to full, consistent text values
Unified customer name variations (Pharm, Pharma, Pharmacy) into a standard format using Find and Replace
Replaced null sales values with the dataset median using Power Query's statistical function

I then built Pivot Tables to answer key business questions and created an interactive Excel dashboard for clear, visual reporting.

Key Findings

Total sales reached $1.45 million across the dataset
Ribabyclor is the top product, contributing over 16% of total revenue
Poland is the company's strongest market with $899,819 in sales
Sales increased by over 80% from 2017 to 2018, indicating strong market momentum
Several products recorded zero or negative sales and require urgent review
Germany shows strong potential for further growth

View on GitHub

Power BI

Power BIPower QueryDAXCustomer Analysis

Customer Segmentation Analysis

The Problem

The company lacked a clear understanding of its customer base. Without visibility into demographic profiles, regional performance, and growth trends, management could not identify high-value customer segments, develop targeted marketing strategies, or improve customer retention. Decision-making was reactive rather than data-driven.

What I Did

Using Power Query, I cleaned and transformed the dataset:

Extracted year from Join Date to analyze customer growth over time
Created a Tenure column to measure customer loyalty periods
Grouped customers into Age Categories (Youth, Young Adult, Adult, Senior) using conditional logic

I then built an interactive Power BI dashboard with KPI cards, demographic breakdowns, regional maps, and occupational analysis — all filterable across 4 business dimensions.

Key Findings

The customer base is mature, with an average age of 46 and average tenure of 5 years
South region has the highest customer concentration; West region is underperforming
Gender distribution is balanced, with a slight female majority
Engineers, teachers, doctors, and bankers form the core professional segment
Customer growth is inconsistent over time, signaling a need for more strategic acquisition efforts

Watch Live Demo GitHub

SQL

1 / 5

SQLMySQLEDAData Aggregation

Retail Sales Exploratory Analysis

The Problem

Despite collecting large volumes of transactional data, the organization had limited visibility into regional performance, product profitability, and customer segment behavior. Key questions remained unanswered: Which regions drive the most revenue? Which categories are truly profitable? Where are the opportunities for growth? Without answers, decision-making was reactive rather than strategic.

What I Did

I imported the dataset into MySQL and structured it for analysis. Using SQL I:

Used SUM, AVG, and GROUP BY to compute total sales and profit by region, category, and segment
Applied WHERE and HAVING clauses to isolate high-value orders and profitable categories
Used ORDER BY and LIMIT to rank top-performing sub-categories and revenue drivers
Reviewed the dataset for inconsistencies and data quality issues that could affect accuracy

Key Findings

Total sales of $2.29 million with $286,397 in profit across all orders
West region leads in sales; South region is the weakest market
Technology is the most profitable category with high sales and strong margins
Furniture generates high revenue but surprisingly low profit, suggesting cost inefficiencies
Home Office customers have the highest average order value per transaction

View on GitHub

Python

1 / 6

PythonPandasMatplotlibSeaborn

Amazon Sales Dataset Insight

The Problem

Management needed a clear picture of how the business was performing across different dimensions. They wanted to understand how revenue was evolving over time, which product categories and regions were performing best, how customers were behaving, and whether the business was operating profitably. The dataset had no direct cost or profit values, requiring additional assumptions and feature engineering.

What I Did

Using Python in Jupyter Notebook, I carried out a full EDA pipeline:

Loaded the dataset with Pandas and explored its structure, data types, and column distributions
Cleaned the data: handled missing values, removed duplicates, and converted date columns to datetime format
Engineered profit and total cost features since they were not provided in the raw dataset
Analyzed sales trends over time, category and regional performance, and customer purchasing patterns using Matplotlib and Seaborn visualizations

Key Findings

The business generated $32.87 million in revenue with 149,970 units sold
Sales performance was stable over time with no major fluctuations
Product categories contributed relatively evenly to total revenue with no single dominant category
A positive correlation exists between quantity sold, revenue, and profit — increasing sales volume directly improves profitability
Marketing focus on top-performing regions can help sustain demand and drive further growth

View on GitHub

Client Feedback

Testimonials

Real feedback from real people who have experienced what good data analysis can do for a business.

Dashboard and Reporting Impact

Modinat transformed our reporting process. She built a Power BI dashboard that replaced hours of manual reporting with clear, real-time insights. Our team can now track performance in minutes and make faster decisions.

A. B.

Operations Manager, Retail Startup

Data Cleaning and Accuracy

We finally trust our data again. Our dataset was messy and difficult to use until Modinat cleaned and structured it. Her attention to detail improved the accuracy of our analysis and gave us confidence in our numbers.

T. O.

Founder, E-commerce Brand

Turning Data Into Actionable Insights

She doesn't just analyze data. She explains what it means. Modinat highlighted key trends and opportunities we hadn't noticed and presented them in a way the whole team could understand. The insights were practical and easy to act on.

C. N.

Marketing Lead, SaaS Startup

Customer Segmentation and Strategy

Her analysis helped us understand our customers better. By segmenting our customers and identifying high-value groups, Modinat provided insights that support smarter marketing and retention strategies.

I. M.

Growth Manager, Fintech Startup

Professional and Easy to Work With

Reliable, communicative, and genuinely passionate about data. Modinat asked the right questions, delivered organized work, and made complex analysis feel simple and accessible for our team.

S. J.

Project Coordinator, Consulting Agency

Ready to Make Your Data Work for You?

Join businesses and teams who have turned messy data into clear insights and smarter decisions with Modinat in their corner.

Let's Work Together

Got Questions?

Frequently Asked Questions

I work with Microsoft Excel, Power BI, SQL (MySQL), and Python (Pandas, NumPy, Matplotlib, Seaborn). I also use Jupyter Notebook and GitHub for project documentation and version control.

I work on data cleaning, exploratory data analysis, interactive dashboards, business reporting, customer and sales analysis, and data visualization. If you have messy data that needs to make sense, I can help.

My work is ideal for small and medium-sized businesses, startups, and non-technical teams who need data translated into clear, actionable insights without the jargon.

Yes, I work fully remotely and can collaborate with clients from anywhere. All communication, file sharing, and delivery happens online seamlessly.

It depends on the complexity of the data and deliverable. A simple dashboard or analysis can take 3 to 5 days, while more detailed projects may take 1 to 2 weeks. I always communicate timelines upfront before starting any work.

Yes! I'm actively open to entry-level data analyst roles, internships, and freelance collaborations. Feel free to reach out and let's explore opportunities together.

Simply book a free call via my Calendly link or send me an email. We'll discuss your data needs and figure out the best approach together. No commitment required for the first call.

Yes, all my projects are documented and available on my GitHub profile for full transparency. You can review my code, methodology, and findings before reaching out.

Johnson
Modinat

The Story Behind
the Data

Services

Your Data Challenges,
Solved.

Skills

Projects

Testimonials

Certification

Frequently Asked Questions

Let's Work Together

JohnsonModinat

The Story Behindthe Data

Services

Your Data Challenges,Solved.

Skills

Projects

Testimonials

Certification

Frequently Asked Questions

Let's Work Together

Johnson
Modinat

The Story Behind
the Data

Your Data Challenges,
Solved.