Data Science Capstone

Eric Chuar has successfully completed Data Science Capstone for Personal & Professional Productivity Course Certificate from the IBM.

SCHOOL:
IBM

GRADUATED:
2019

DURATION:
6 Months

Data Science Skills Gained

Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders

Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors

Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation

Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model

Course Perspective

Just finished the Data Science Capstone course, and I’ve got to say, it was like finding the final piece of a jigsaw puzzle I didn’t even know was missing. Now, if you know me, you’d know that I’m the kind of guy who gets hands-on with a subject way before stepping into a formal course. Certificates? They’re just paper—what matters is the skills you actually gain.

I’ve been in digital marketing and programming for a while now, but this course was something else. It’s so complex and in-depth that it piques my curiosity like nothing else. I’m pretty sure not everyone could wrap their head around it, and that makes it even more fascinating to me.

So, what did I actually learn? For starters, I got to demonstrate my proficiency in data science and machine learning using real-world data sets. I mean, I actually prepared a report for stakeholders that showed I knew what I was talking about.

Then there’s Python—the backbone of any serious data science project. I learned how to write Python code for machine learning models, covering everything from support vector machines to decision tree classifiers and k-nearest neighbors. That’s like adding a whole new toolkit to my skill set, which is super useful for my work in digital marketing.

But that’s not all. The course also covered everything from data collection to data wrangling, exploratory data analysis, and even model evaluation. I got to weigh the strengths and weaknesses of different machine learning models for predictive analysis. Now, I can identify the optimal model for any given scenario.

Now, why does this matter, especially for someone living in Malaysia and Singapore for years? Because it’s not all about making money; it’s about adding real value to people’s lives. With these new data science skills, I can do just that and more.

As I get older and my family grows (yep, got a cute kid to think about), my focus is even more on helping others. Whether it’s digital marketing, programming, or my hobbies like badminton and red wine, sharing knowledge has always been my thing.

So here’s my next step: Apply these skills to real-world projects, analyze the data, and make life better for as many people as possible. Time’s ticking, and I’d rather spend it making a meaningful impact than chasing a few extra bucks. And this course? It just gave me the tools to do exactly that.