From Electrical Engineer to Aspiring Data Scientist

Meet Cyprian Fusi. After starting as a teacher in Cameroon, he moved to Germany to study electrical engineering. When the 2016 market crash disrupted his career, he embraced data science, embarking on a journey of self-learning and transformation. This is his inspiring story.

What sparked your interest in data science, and how did you begin your learning journey?

My journey started in 2007 when I graduated with a Master’s in electrical engineering. After working as a Greenhouse Gas (GHG) auditor and experiencing industry shifts, I found myself at a crossroads in 2016. I decided to learn database administration and SQL, which sparked my interest in data science. By 2018, I was encouraged by a colleague to explore data science more deeply, leading me to discover Dataquest.

What challenges did you face with self-learning before finding Dataquest?

Before finding Dataquest, I explored various resources, including Dr. Angela Yu’s 100 Days Python Bootcamp and Dr. Fred Baptiste’s Deep Dive series. Dr. Yu’s course was my first real exposure to coding. For statistics, I initially relied on books and a Michigan State University YouTube course. I even translated a 600+ page SPSS-based statistics book into Python, which helped me close knowledge gaps.

Although these courses were valuable, they often felt disjointed, and I struggled to bring everything together. This is where Dataquest became crucial.

How did you discover Dataquest, and what convinced you to choose it as your primary learning platform?

I came across an email from Dataquest offering a yearly subscription, and I didn’t hesitate to purchase it. The platform’s structured and comprehensive approach fit my learning style perfectly. Without video courses, I was forced to read and engage with the material actively, ensuring I truly understood the concepts.

The interactions with learners, moderators, and Chandra, the AI assistant, have also been invaluable. The community has provided support and encouragement, making my learning experience more enriching.

Can you describe a project you’re particularly proud of?

One of my proudest projects is the heart disease classification project I completed on Dataquest. It was an exciting challenge that allowed me to apply my skills in a meaningful way.

The Guided Projects provided a structured way to apply my skills, integrating the knowledge I’d gained from other sources. Dataquest offered the cohesion I was missing, allowing me to practice and reinforce my learning fully. It brought everything into focus, organizing my scattered knowledge into a clear, structured path. This approach boosted my confidence and skills significantly, making Dataquest an essential part of my learning journey.

What have been some of your most significant achievements since joining Dataquest?

Since joining Dataquest, I’ve completed over 40 certificates, which has significantly boosted my confidence in data cleaning, machine learning, and deep learning. I’m actively engaged in the Dataquest community, providing feedback to help improve the platform. Additionally, I’ve been accepted into Oxford University for a Master’s degree in data science, which I’m thrilled about.

Dataquest has been instrumental in shaping my career goals. It has equipped me with the skills and confidence to pursue data science and has inspired me to give back by providing training in my home country.

How do you plan to continue using Dataquest in your career?

I’ve invested in a lifetime premium membership because I see Dataquest as a long-term partner in my learning journey. I’ll continue to use the platform to stay updated and enhance my skills.

What advice would you give to others looking to transition into data science?

Be resilient and determined. It’s important to stay disciplined and embrace the learning journey. Use structured resources like Dataquest to organize your knowledge and skills. Don’t be afraid to take on challenges, and always strive to learn and grow. With the right mindset and tools, anyone can succeed in data science.