Anyone can become a data scientist, no matter their background. The key is having a strong curiosity and a real willingness to learn new things. This is reflected in the fact that 25% of a data science curriculum typically focuses on multidisciplinary analysis.
Next, you’ll need to build a solid understanding of theory—both mathematical and non-mathematical—as well as models and statistical methods (including AI), each of which usually makes up around 20%. Then, set aside about 15% to learn algorithm design and analysis, plus programming languages like R or Python. Once you’re comfortable with those (through lots of practice), level up by learning more advanced languages like C or C++.
At this point, you’ve already covered about 80% of what’s needed in a typical data science curriculum. To complete the rest, just stay up to date with relevant technologies (about 5%) and improve your communication skills (15%) so you can effectively present your findings to stakeholders.
This article is inspired by and adapted from Cleveland (2001), which outlines a foundational roadmap for data science education.
- Cleveland, W.S. (2001), Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics, International Statistical Review, 69(1), pp. 21–26.
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