I have accumulated almost three years of experience as a self-taught Data Scientist coming from a non-technical background. Over the years, I have worked on a wide variety of projects, including forecasting models, recommender systems, computer vision models, and more recently, LLM-powered chatbots.
And despite having proven my value to myself and others time and time again, frankly speaking, I still struggle to believe it when very smart and accomplished colleagues and acquaintances come to me and ask for my advice to solve a data science-related problem.

If you're interested in AI and considering a career as a Data Scientist, but you're hesitating because you don't have a technical background in computer science, math, or physics, let me tell you that, although it's not an easy task, my journey is living proof that this is absolutely possible.

Here are the three aspects I suggest focusing on:

• Learn the fundamentals of Python, statistics, and machine learning.

• Practice, practice, and practice again.

• Share your work online.

Learn the fundamentals of Python, statistics, and machine learning

The role of a Data Scientist is a technical role that requires a good understanding of Python, statistics, and machine learning.

With Python you should be comfortable working with basic data structures, variables, lists, dictionaries, functions, and classes.

For statistics, you should at least cover sampling distributions, exploratory data analysis (EDA), and A/B testing.

For machine learning, you should be familiar with basic supervised and unsupervised algorithms.

Practice, practice, and practice again

Learning the theory is important, but if there is one mistake beginners make over and over again, it's spending way too much time learning the concepts and too little time putting those concepts into practice to solve real problems.

If you want to get good at this, you should definitely spend a good chunk of your time writing code.

Share your work online

As an introvert, this is the aspect that I struggle the most with, but whether we like it or not, we live in a society where social media platforms play an important role and having an online presence is not an option. Progressively building a portfolio of projects and share it online will definitely put you ahead of the competition.

At the end of the day, you'll be paid to solve problems and a portfolio of well-curated projects is one of the effective ways to signal that.

It's going to be a bumpy road with lots of ups and downs, but you're truly passionate about this field and you're willing to put in the time and the effort to learn the fundamentals, put them into practice, and share your work online, nothing will stop you from becoming a Data Scientist, not even your non-technical academic background.