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? Thread 👇🏾 (1/6)
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 severe impact that I almost gave up on everything. I felt so defeated. 😞(2/6)
So when my professor suggested that I apply to do a Master's in 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 program, I got the idea to start a business. (3/6)
It was a different kind of business: a social enterprise called @devinvogue helping African women to get started in tech. While running the org as the CEO, I got interested in data science and started spending time researching on how one can become a data scientist. (4/6)
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 sites like freeCodeCamp and working on projects. (5/6)
Everything one needs to get started in data science can be found online. So I've decided to do my part by using my social media platforms to share useful resources and tips on how anyone can get started in data science. I'm hoping to learn from you too. Now, shall we? 😎 (6/6)
• • •
Missing some Tweet in this thread? You can try to
force a refresh
How can one become a data scientist? Is it even worth it to pursue a career in data science? Today, I share 10 tips that can help you get started in data science. Thread 🧵👇🏾
1️⃣ 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.
2️⃣ 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.