Mckenzieking Designs

Last Tuesday I received my Certificate of Achievement from NPower Canada's Data Analyst Program.

Over the past four months, I've spent days and weekends learning data analytics, Python, SQL, Excel, dashboards, visualization, and AI while continuing my work in UX, Product Design.

It wasn't always easy. There were moments when the workload felt overwhelming, especially balancing multiple courses, assignments, team projects, and deadlines. But pushing through those challenges made this achievement even more meaningful.

One of the best parts of the experience was the people. I met an incredible group of teammates who supported each other, learned together, and became genuine friends along the way. I’m grateful for the connections I've made and hope to stay in touch long after this program.

This experience has strengthened my belief that great products are built at the intersection of customer insight, design, data, and AI. I'm excited to continue applying these skills to create more thoughtful, data-informed experiences.

Thank you to NPower Canada, the instructors, mentors, and my teammates for being part of the journey.

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I’ve been working through a data analytics project exploring immigration trends to Canada from 1980 to 2013.

What stood out to me was how clearly the data reflects real-world events. For example, there’s a noticeable spike in immigration from Haiti around 2010, which aligns with the earthquake. Seeing that connection visually really brings the data to life.

It’s been a great way to practice not just analyzing data, but actually telling a story with it.

Github file here: https://github.com/DKTODesigns/immigration-trends-data-storytelling

GitHub - DKTODesigns/immigration-trends-data-storytelling: End-to-end analysis of Canada immigration data (1980–2013) using Python. Includes data preprocessing, exploratory analysis, and time-series visualizations with Matplotlib to uncover... End-to-end analysis of Canada immigration data (1980–2013) using Python. Includes data preprocessing, exploratory analysis, and time-series visualizations with Matplotlib to uncover trends and comp...

I’ve been getting more hands-on with web scraping using Python, BeautifulSoup, and Pandas, and just completed my certification as part of my data analytics training.

In this project, I worked through:
• parsing HTML and navigating page structure
• extracting links and images
• scraping structured data from tables
• converting raw web data into clean, usable DataFrames

Honestly, seeing all that raw HTML turn into structured, usable data felt a bit like a magic trick.

What stood out to me is how quickly unstructured web content can be transformed into something usable for analysis, dashboards, or even AI-driven features.

As someone working at the intersection of UX, data, and AI, this kind of workflow opens up a lot of possibilities for building more intelligent, data-informed products.

I’ve shared the full notebook on GitHub here:
https://github.com/DKTODesigns/web-scraping-beautifulsoup-pandas/tree/main

Curious to keep building on this, especially around how scraped data can feed into user-facing experiences and insights.

github.com

Added another small project to GitHub as part of my data analytics journey.
This one focuses on Python file handling and data cleanup — separating active and inactive records while preserving structure and formatting.

It’s a simple use case, but a good reminder that a lot of real-world data work starts with cleaning and organizing raw data before any analysis or AI comes into play.

Also validated the solution with a test to ensure data integrity.
Learning a lot through hands-on practice and building as I go.

It’s a simple use case, but a good reminder that a lot of real-world data work starts with cleaning and organizing raw data before any analysis or AI comes into play.

Here’s the project: https://github.com/DKTODesigns/member-data-cleanup

More to come as I continue building.

GitHub - DKTODesigns/member-data-cleanup: Python script for cleaning and organizing membership data by separating active and inactive users. Python script for cleaning and organizing membership data by separating active and inactive users. - DKTODesigns/member-data-cleanup

Starting to share more of what I’m building as I work through my data analytics + AI learning journey.

Just pushed a small project to GitHub, my first time posting code there, so definitely a learning experience on that front as well.

I built a simple text analyzer in Python that:
• cleans and normalizes text
• counts word frequency
• surfaces patterns in unstructured data

It’s a basic example using sample text, but it helped me understand how raw text can be structured and analyzed.

As a product designer, I’ve always worked with qualitative insights. Now I’m exploring how to approach that kind of data more systematically using code.

Here’s the repo:
https://github.com/DKTODesigns/text-analyzer-python

GitHub - DKTODesigns/text-analyzer-python: Simple Python text analyzer for word frequency and preprocessing Simple Python text analyzer for word frequency and preprocessing - DKTODesigns/text-analyzer-python

Just wrapped up Data Visualization and Dashboards with Excel & Cognos as part of the IBM Data Analyst Professional Certificate 🎉

Finished Excel Basics for Data Analysis last week.

This one was all about turning data into insight — not just making charts, but learning how to tell a clear story with numbers.

I worked on building:

• Line, bar, and pie charts in Excel
• Advanced visuals like Treemaps, Histograms, Scatter Plots & Filled Maps
• Interactive dashboards using Excel and IBM Cognos Analytics

What I love about this area is how it connects to my UX work — good visualization is really about clarity, structure, and helping people make better decisions.

On to the next module 📊✨

Excited to share a new milestone — I’ve officially completed my Introduction to Data Analytics certificate on Coursera.

This program focused on the fundamentals of the data analytics process, from understanding different data roles to working with data structures, sources, and visualization techniques. I also strengthened my skills in data cleansing, dashboards, statistical analysis, and big data concepts using tools like Hadoop and Hive.

For my business, this means bringing even more data-informed thinking into the work I do — combining strategy, design, and analytics to help projects move from ideas to measurable outcomes.

Always learning. Always evolving. Thank you to everyone who continues to support the journey.

Completion Certificate for Introduction to Data Analytics This certificate verifies my successful completion of IBM's "Introduction to Data Analytics" on Coursera

Most Shopify stores don’t struggle because of bad products.

They struggle because of small, invisible setup, branding, and UX issues.

I’ve been working recently on a Shopify site for a small vintage glassware business, and what looked like a “simple store setup” turned into real UX, branding, and technical problem-solving:
• Fixing navigation so customers don’t land on dead ends
• Cleaning up product taxonomy and collections so the store actually makes sense
• Reducing friction in checkout-adjacent flows that quietly affect trust
• Auditing and fixing system-generated Shopify pages (like gift cards) that don’t follow theme settings
• Writing custom theme-level code where templates fall short
• Adding specialized branding beyond the theme so the site feels intentional and premium, not templated
• Handling IT setup — connecting GoDaddy/Outlook email to Shopify so the business has a proper branded email experience (since Shopify doesn’t provide email hosting)

None of this shows up in a theme demo.

All of it affects whether someone feels confident enough to buy — or even trust the business.

I love working with early-stage businesses and small Shopify stores where:
– budgets are tight
– there’s no in-house IT
– the idea is solid, but the ex*****on needs structure and polish

That’s where thoughtful UX, practical technical help, and brand-level attention to detail make the biggest difference.

If you’re running a Shopify store and something feels “off” — navigation, branding, checkout, email setup, or overall clarity — that’s usually fixable.

Happy to chat.

Videos | Library | Loom - 15 February 2026

I just created a chat bot for a florist site. This is not a real florist by the way. It is merely for the purpose of training my bot. I just finished a course on IBM Watson, Building AI Powered Chatbots Without Programming on Coursera.

Feel free to give her a go! She's set up to answer such questions as Where are your stores located? I want to buy some flowers. Can you help me find flowers for a special occasion? What time is your store open? What are your store hours? I'm looking to buy roses. etc. And she will answer thank you and good bye.

j2c6hk: What's on Flowers – Site description