By raising awareness about the different pathways into AI and making it more accessible, we want to inspire participation from historically underrepresented groups so that together we can build a more equitable and ethical tech future. AI Ethics and Policy Researcher, Favour Borokini takes us through her career journey and shares what inspired her to join this space and how she landed her current role at Pollicy. She also talks about some of the most common barriers and challenges she tackles on a daily basis and how she deals with them as someone who comes from a non-technical background. She also shares her thoughts on diversity and the most practical tips to get started in this space especially if you're someone who comes from a non-technical background. You can listen to the podcast or read through their conversation below.

Google's AI fundamentals video- covers what AI is, use cases and the impact it's having on our world. Watch here Azure AI Fundamentals course- teaches the basics of machine learning services. Really useful for those with non-technical backgrounds to understand the power of AI, what it can do out of the box and the problems it can solve. Find out more here, scroll down to the learning path Python data science handbook- Python is the go to programming language for machine learning engineers. I recommend checking out chapters 2,3 and 4 to get familiar with Python from a data science perspective.

The global market revenues from data science activities are set to grow in leaps and bounds in the future. And hence, it is no wonder that the demand for data scientists in various industrial roles will rise in proportion to market growth. But the main question is how to get started for a career in data science? While there are specialized technical courses that can be pursued if one has a technical background, things may not be the same for someone with a non-technical (non-engineering) background. At the same time, given the gap between existing skills and required skills, it will be sometime before a non-techie finds a perfect fit in the data science market. Nevertheless, interested individuals can still succeed professionally with or without a technical background.