My Data Science Journey and How to Get Started
By: Ivy Barley
Tags: Data Scientist, Women In Tech, Python, Microsoft, Data Science

My early days — I didn’t know about Data Science then :)

Two things that have come naturally to me since childhood have been Math and Entrepreneurship. I was actually preparing to become a Math professor at my university. Now, I’m a data scientist at Microsoft. What happened?

For most of my life, I was known as an excellent student. However, for the first time ever, due to a hiccup, I failed in my 3rd and 4th years of university while studying Actuarial Science. This had a significant impact on me that I almost gave up on everything. I felt so defeated. So when my professor suggested that I apply to do a Master’s in Mathematical Statistics, I couldn’t be bothered. But I also thought to myself that the worst had already happened so doing that Master’s will just help me pass time.

While in the Master’s program, I got the idea to start a non-profit called Developers in Vogue (DiV), helping African women to get started in the tech industry. As the CEO of the organization, I spent most of my time on the day-to-day activities and very little time on the technical details. I love Developers in Vogue so much and the impact it is making in the lives of young African women; providing them with training, mentorship and job opportunities in the tech industry.

Through my work at DiV, I got featured in a Women in Tech book by the German Ministry of Economic Cooperation and Development

I also got more and more interested in data science so I started spending time researching on how one can become a data scientist. One thing that fascinated me about data science is the ability to derive actionable insights from complex datasets that helps to solve real problems. I was hooked! I spent more time doing research, taking online courses on websites like freeCodeCamp, DataCamp and O’Reilly, and working on projects. I also signed up for a Data Science bootcamp with Practicum.

Navigating the data science landscape has brought me to the realization that everything one needs to get started in data science can be found online. So today, I want to share 10 tips that can help you get started:

You can find all the learning resources to get started with data science online and mostly for free on websites like freeCodeCamp, DataCamp, Codecademy, Coursera, Udemy, etc. Sharpen your technical skills and go deep, be it Python or R and SQL. Build your skills in Data Science using a language of your choice — you could explore more languages, if needed once you’re well versed with your chosen language. Even if you’re not from a Math background, a foundational course in Mathematics and Statistics will go a long way. Remember that data science is more of a tool so have an understanding of the business problem you’re trying to solve using data science so that your solution aligns with the business objectives. Build projects for your portfolio. You can also create your own projects by going onto platforms like Kaggle and finding datasets with suggested use cases. Soft skills like communication, collaboration and empathy are very important as a data scientist. If possible, do at least one data science internship. This really helps in giving you an industrial perspective to how data science can be applied. Network with other data scientists (peers and mentors) on platforms like LinkedIn, Reddit and Kaggle. Join hackathons like the Kaggle 30 Days of ML and attend data science conferences and meetups; there are many online events happening every week on Eventbrite, LinkedIn events and Meetup. Don’t remain stuck on a problem for too long. Your roadblock may not be unique and there may be several available solutions online. Google and Stack Overflow are your friends.

I joined Microsoft just before the pandemic started.

Want to learn more about my journey as a data scientist at Microsoft as well as useful resources and opportunities for people who are new to the field? Be sure to follow so you don’t miss any of my upcoming Medium posts.