DataCamp-Ishango.ai Scholarship: Alex Akorli’s Data Science Journey

Please share a bit about yourself and your background.

My name is Alex Akorli from Ghana. I am an aspiring data scientist with a background in data analytics. I currently provide consulting on academic research and engineering simulations as a freelance research assistant. I recently completed an internship with the Ghana Standards Authority in the area of metrology, applying data analytics and statistical knowledge to the science of measurement. 

2. What did you study, and how did you first become interested in data science?

I studied Production Engineering and have a BSc. in Mechanical Engineering, with a focus on mechanics as my specialization. My introduction to data science was quite challenging, as it happened at a crucial juncture during my final year of school. The context was a final year project where I undertook an energy audit for a building. Within this project, I ventured into the realm of data science by creating an Artificial Neural Network model. This model was aimed at predicting the energy consumption of the building for the next one to two years.

Though the scope was on energy and the project was engineering-focused, data science knowledge and skills were crucial. The workflow involved data gathering and processing, building models and forecasting which was really fascinating. We’re told that the way data that is treated can reveal unknown information and tell us what to expect in the future.

I consider data to be the pivotal element, and the field of its scientific exploration is unavoidable. This is what sparked my fascination with data science, leading me to firmly believe that we have only scratched the surface of what data can achieve. 

3. What has been your experience with the DataCamp-Ishango.ai scholarship so far?

I’m currently taking the Data Scientist with Python track on DataCamp, and I’m really enjoying how the courses are set up for beginners like me. When I started, I didn’t know much about Python, but in just a few months, I’ve been able to learn a lot. The courses are hands-on and practical, which helps me understand what I need for data science. My experience has been really smooth, and I’ve even joined meetings for learners organized by Ishango.ai. I’ve attended these meetings twice, and it was really motivating to see other people excited about their unique presentations. I hope that one day I can do the same and inspire others too.

Slack community of learners has also been really beneficial. I have reached out to other members and know I able to get support to learn when I need it. This is the kind of support that is needed to succeed. I must also acknowledge that it has been a serious journey. It is helping me build more interest, commitment, and discipline in data science. As the saying goes “Hard Training! Easy Battle!”. The constant reminders to finish the course have been super helpful as they provide a great push.

4. What are your learning goals, and what do you hope to achieve through the DataCamp-Ishango scholarship?

My Learning goals include: mastering the technical knowledge and analytical abilities needed to complete jobs in the real world, acquiring sufficient practical knowledge to support independent learning, and gaining expertise in managing data science projects. Using Python, I am developing solid knowledge and skills for data science, which I can then apply to other programmes like R programming. For future use, it is crucial for me to collect and take ample notes from the courses. I want to use data science to address mechanical engineering problems in a novel way, so I’m going to finish the data scientist with Python track on DataCamp and get certified.

By the end of this learning curve, I hope the DataCamp-Ishango scholarship will give me the solid technical foundation I need to become officially recognised as a data scientist with the necessary skills to work in the field. I truly hope that with the help of this scholarship, I will be able to start projects, have an impact through practical outcomes, and effectively share data-driven insights.