My Journey To Become a Professional Data Analyst
Why I Started My Journey into Data Analytics
Hello! My name is Joel Chuah. I have a degree in Information Technology. After I graduated, I wanted to become a web developer. I enjoyed building websites and learning how they work, especially the part where websites store and manage data using databases. While working on personal projects, I often dealt with Database Management Systems (DBMS), and I found myself constantly researching how data is stored, retrieved, and optimized.
That’s when I started noticing something important, "Data is not just something we store, it is also something we can learn from".
The more I explored DBMS, the more curious I became about what’s possible with data. I realized I wasn’t just interested in storing it, I also wanted to analyze it, find patterns, make predictions, and solve real-world problems with it. That curiosity pushed me in a new direction.
That is when I decided to take a big step, I enrolled in a Master of Data Science program, to build a stronger foundation and upgrade my skills. My goal was to learn how to work with data more effectively, sharpen my technical skills, and eventually transition into a data analyst role.
What I Knew (and Didn’t Know) Before Starting
Before I started learning data analytics, I already knew a few things from my IT and web development background. I was comfortable working with databases, writing simple SQL queries, and using Excel for basic tasks. I also had some experience with programming, especially in web development languages like HTML, CSS, and JavaScript.
But when I stepped into the world of data analytics, I quickly realised there was still a lot I didn’t know. For example, I didn’t fully understand how to clean and prepare raw data for analysis. I wasn’t familiar with data visualisation tools or techniques. I also didn’t have a strong understanding of statistics, which is very important in data analytics.
Another thing I didn’t know much about was how to use Python for data analysis. At the time, I hadn’t worked with libraries like pandas, matplotlib, or scikit-learn. I also didn’t know how to build a full project or how to tell a story using data.
Even though I had a technical background, learning data analytics felt like starting from scratch in many areas. It was a bit scary at first, but also exciting.
The Tools I Learned As a Starting Point (+ Resources That Really Helped Me)
When I started learning data analytics, I focused on tools that were commonly used in the industry and beginner-friendly. These tools helped me build a strong foundation.
1. Excel
Excel was one of the first tools I learned. It helped me understand how to clean data, use formulas, and create basic charts. I learned that Excel is actually super powerful! There are so many things you can do with it, from basic formulas to data cleaning, analysis, and even dashboards.
One of the best learning resources for me was a YouTube channel called Chandoo. Big shoutout to Chandoo! He is not only a great content creator but also an excellent teacher. His lessons and exercises are very detailed, clear, and easy to follow. I learned a lot from his videos, and I highly recommend his channel to anyone starting with Excel.
2. SQL
Since I already had some experience with databases, learning SQL felt quite natural. I improved my skills by writing more complex queries like joins, subqueries, and aggregate functions. SQL is very useful when working with large databases, and I realised it’s a must-have skill for any data analyst.
As for SQL, I also learned a lot from Chandoo’s tutorials. Since I had some experience from my web development background, SQL was a bit easier to pick up. But going deeper into writing better queries like joins, filtering, and aggregation really helped me understand how powerful SQL is for data analysis.
3. Python
Python was quite a big hurdle for me at the beginning. Programming in Python was never really my strong point, so it took time and practice to get comfortable with it.
If you're just starting out, I would suggest first learning basic Python, things like variables, loops, and functions and then moving on to data analysis with Python using libraries like:
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pandas (for data manipulation),
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matplotlib and seaborn (for data visualisation)
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scikit-learn (for basic machine learning)
One YouTube channel that really helped me is Luke Barousse. His content is fun, clear, and practical. He explains concepts in a way that’s easy to understand, especially for beginners in data.
4. Power BI
I also learned how to use Power BI to create dashboards and visualise data. It was interesting to see how raw data can be turned into something interactive and easy to understand. Power BI taught me how to communicate my findings in a clear way.
Just like with the other tools, I learned Power BI mostly through YouTube. One channel I found really helpful is Learnit Training. They explain how to use Power BI in a simple way, from importing data, creating visuals, to building full dashboards. Watching step-by-step videos made it easier for me to practice and understand how to tell stories using data.
My Current Certifications
- Google Data Analytics Professional Certificate (Coursera)
- This course gave me a good foundation in data analysis. I learned about data cleaning, using spreadsheets, SQL, and basic data visualization.
- The course provide 1- week free trail as well as financial aid around 15 days ago for each of the 8 courses (need to apply).
- Master of Data Science (Graduated in Sunway University)
Data preprocessing – how to clean and prepare data
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Machine learning – building models to make predictions
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Data visualization – turning data into charts and dashboards
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Big data tools – like Apache Spark and Hadoop
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SQL and Python – two key tools I use a lot in assignments and projects
- Microsoft Certified : Power BI Certificate (In-progress)
- Current working on it
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