I developed an interest in Data Science and I started looking at options on where I could do it. Moringa School came to mind because I had heard of so many good reviews about their programs. My initial expectation was that I would be here to learn more about exploratory data analysis and understand more about machine learning using R.

Moringa school helped me in so many ways, but I think the most important one is the data science pipeline(in a nutshell). I am now able to do analysis projects with ease. I am now working for Eneza Education, an ed-tech company as a Business Intelligence Engineer. Yes! I would suggest the course to anyone who would want to pursue Data Science.

For sure Moringa school has really contributed a lot to my profession. First by equipping me with the proper knowledge and skill needed in the current industry. The process of them linking you to the employers made everything even better by giving us a smooth transition to the industry in terms of getting a job without any stress.

Before Moringa School, I was a computer science student but then I decided to join a coding boot camp to gain practical skills. After the program, I did so well that I got an offer to join as a technical mentor. Life for me has never been the same since them. I love coding and would definitely encourage more girls to join the program.

When I joined Moringa School, I was quite overwhelmed at first because it’s fast-paced and it’s not like the traditional learning model I was used to. Here we have Technical Mentors who guide you through the curriculum and are there if you need any help. They encourage you to think for yourself, take initiative and they guide you with your own learning pace. See, I’m not a math person but I managed to do this as I’m surrounded by very smart and passionate people who always support me. Through support and consistent practice, I have been able to understand the concept of Data Science and apply it in various scenarios. It’s actually a very interesting and fun concept as here, we’re able to experiment with different, real-life data sets.