From Network Cables to Data Pipelines: My 8-Month Journey from IT Support to Data Analytics

Paul Onyango — Sun, 07 Jun 2026 15:22:43 +0000

May 25, 2026.

This is not just another date on my calendar.

This marks the beginning of one of the biggest professional transitions of my life.

After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time:

I am transitioning into Data Analytics.

And this is where I document that journey—publicly, honestly, and in real time.

Not when I become an expert.
Not when I feel “ready.”
Not when everything looks polished.

I’m starting now.

Because real growth is rarely clean, predictable, or perfectly planned.

Sometimes it starts with one uncomfortable decision:

To leave what you already know… and step into what your future requires.

Where My Journey Started

Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT.

For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations.

My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity.

Over the years, I’ve worked deeply in:

Computer troubleshooting and hardware diagnostics
Printer setup, configuration, and enterprise support
Wi-Fi deployment and hotspot installations
LAN design and structured network deployment
Fiber optic installations and network termination
Data cabling and structured cabling systems
CCTV surveillance installation and maintenance
Alarm systems and electronic security integration
Intelligent security systems
Electric fence installations and perimeter protection systems
Router, switch, and access point configuration
End-user support and enterprise technical troubleshooting
Systems maintenance and operational support

I’ve spent years on ladders, in server rooms, inside offices, on construction sites, inside schools, business centers, and homes—making sure systems stay connected, secure, and operational.

Technology has been my world.

And I’ve loved it.

Every cable terminated correctly.
Every network restored after downtime.
Every CCTV system brought online.
Every user issue resolved.

That work built me.

It taught me discipline.

It taught me how to think under pressure.

It taught me how to troubleshoot.

It taught me how to solve problems logically.

And most importantly…

It taught me that behind every technical problem lies data.

The Shift That Changed My Thinking

Over the last few years, I began noticing something.

The world of technology is evolving faster than ever.

Artificial Intelligence is changing industries.

Automation is replacing repetitive tasks.

Cloud infrastructure is reshaping how businesses operate.

And data…

Data is becoming the new language of decision-making.

Companies are no longer making decisions based on instinct alone.

They’re making decisions based on numbers, trends, patterns, behavior, forecasting, and insights.

And I asked myself:

Do I want to spend the next 10 years only supporting systems… or do I want to help drive the intelligence behind those systems?

That question changed everything.

Why Data Analytics?

Coming from networking and systems support, I already understand technology infrastructure.

I understand how systems generate information.

I understand how logs are created.

I understand performance metrics.

I understand users, operations, failures, bottlenecks, uptime, downtime, and infrastructure behavior.

What I want now is to understand the story behind the numbers.

I want to move from:

Fixing technical problems → Understanding business problems

From:

Supporting systems → Driving insights

From:

Installing infrastructure → Extracting intelligence from data

That’s why I chose Data Analytics as my next chapter.

My Official Start Date

On May 25, 2026, I officially begin an 8-month online data analytics program.

This is not casual learning.

This is not “watch a few YouTube videos.”

This is a structured commitment.

My training schedule:

Duration: 8 Months
Classes: Monday to Thursday
Daily Commitment: 3–4 Hours
Mode: Online live learning + practical assignments + projects

But class time alone won’t be enough.

Outside class, I’ll be investing additional hours in:

Practice exercises
Coding challenges
Personal projects
Case studies
Interview preparation
Portfolio development

Because my goal is not simply to complete a course.

My goal is to become skill-ready, project-ready, and interview-ready.

What I’ll Be Learning

Over the next 8 months, I’ll be building strong foundations in:

Microsoft Excel for data cleaning, analysis, and reporting
SQL for querying databases and extracting insights
Python for automation, analysis, and working with datasets
Microsoft Power BI for dashboards and business intelligence
Statistics and analytical thinking
Data storytelling and visualization
Business problem-solving using data

And beyond analytics…

My long-term vision doesn’t stop there.

Once I build strong analytics fundamentals, I plan to transition deeper into Data Engineering—learning how modern organizations build data infrastructure, pipelines, cloud workflows, and scalable systems.

In many ways, data engineering feels like a natural evolution of my networking background:

Infrastructure… but for data.

Systems… but at scale.

Architecture… but intelligence-driven.

Why I’m Building in Public

I know this journey won’t be easy.

There will be:

Coding errors.
Failed assignments.
Concepts that don’t click immediately.
Projects that break.
Moments of doubt.
Days where motivation drops.

But I’ve spent enough years in IT to know something important:

Growth happens in troubleshooting.

And that mindset is exactly what I’m bringing into data.

That’s why I’ve chosen to build this journey publicly.

Through this blog, I’ll be documenting:

My weekly progress
The tools I’m learning
My projects and case studies
Mistakes and lessons
Interview preparation strategies
Career transition challenges
What works—and what doesn’t

No filters.

No pretending.

Just real learning.

If You’re on a Similar Path…

Maybe you work in IT support.

Maybe you’re in networking.

Maybe you’re in operations.

Maybe you’re a system administrator, a field engineer, a telecom technician, or someone who feels your current technical skills may not be enough for the future.

I understand that feeling.

You’ve worked hard.

You’ve built experience.

But deep down, you know it’s time to evolve.

If that’s you…

You’re not alone.

This journey is for you too.

Day One Begins: May 25, 2026

This is the first article in what will become an ongoing series documenting my transformation:

From networking and systems support… to data analytics… and eventually to data engineering.

Ten years of infrastructure.

Now it’s time to build intelligence.

And this time…

I’m bringing the world along for the journey.

Day 1 begins: May 25, 2026.

Week 1 of My Data Analytics Journey: Discovering the Power of Excel in Real-World Data Analysis

Paul Onyango — Sun, 07 Jun 2026 13:45:17 +0000

How Excel is Used in Real-World Data Analysis

As I begin my journey into Data Science and Analytics, one of the first tools I have been learning is Microsoft Excel. Like many people with a background in IT support and networking, I always viewed Excel as a simple spreadsheet application used for storing information and performing calculations. However, after my first week of training, I have discovered that Excel is much more than that. It is a powerful tool used worldwide for organizing, analyzing, and interpreting data to support business decisions.

Businesses, financial institutions, healthcare organizations, marketers, and government agencies rely on Excel to transform raw data into meaningful insights. Analysts use Excel to track sales performance, prepare financial reports, monitor business operations, and identify trends that help organizations make informed decisions.

During my first week, I learned the fundamentals of Excel, starting with the Excel Interface Overview, including ribbons, worksheets, rows, columns, and cells. Understanding how data is structured is essential for effective analysis. I also learned how to sort and filter data, which allows analysts to quickly find specific information within large datasets. Features such as Data Validation help ensure data accuracy, while Freeze Panes makes it easier to navigate large spreadsheets without losing sight of important headers.

I was also introduced to several important Excel functions that form the foundation of data analysis. Basic functions such as SUM(), AVERAGE(), and COUNT() help analysts calculate totals, averages, and record counts efficiently.

Beyond the basics, I learned Conditional Functions that allow analysts to work with specific criteria. These include:

SUMIF() and SUMIFS() to add values that meet one or multiple conditions.
COUNTIF() and COUNTIFS() to count records matching specific criteria.
AVERAGEIF() and AVERAGEIFS() to calculate averages based on selected conditions.

These functions are particularly useful when analyzing business data, customer information, sales records, or financial transactions.

I also explored Text Functions such as LEFT(), RIGHT(), LEN(), and CONCAT(), which help clean, extract, and combine data. In addition, Date and Time Functions such as TODAY(), NOW(), DAY(), MONTH(), and YEAR() allow analysts to work with time-based data and identify patterns over specific periods.

Together, these functions help analysts clean data, identify trends, generate reports, and create meaningful insights that support decision-making.

How This Changed My Perspective on Excel

This first week has completely changed how I view Excel. I now see it as a data analysis tool rather than just a spreadsheet application. As someone who has spent nearly a decade working in networking, technical support, systems operations, and infrastructure deployment, I am beginning to understand that behind every business process, system, and technology platform lies data waiting to be analyzed.

Excel has shown me that data is not just information—it is a resource that can reveal patterns, measure performance, and drive better decisions.

Final Thoughts

This is only the first step in my Data Science and Analytics journey, but it has already opened my eyes to the importance of data in today's world. I look forward to building on these foundations and learning more advanced analytical skills in the coming weeks.

What was your first impression of Excel when you started learning Data Analytics or Data Science? I'd love to hear your experience and lessons in the comments below.

DEV Community: Paul Onyango

From Network Cables to Data Pipelines: My 8-Month Journey from IT Support to Data Analytics

Week 1 of My Data Analytics Journey: Discovering the Power of Excel in Real-World Data Analysis