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Many “How to Data Science” courses and articles, including my own, tend to highlight fundamental skills like Statistics, Math, and Programming. Recently, however, I noticed through my own experiences that these fundamental skills can be hard to translate into practical skills that will make you employable.
Therefore, I wanted to create a unique list of practical skills that will make you employable.
The first four skills that I talk about are absolutely pivotal for any data scientist, regardless of what you specialize in. The following skills (5–10) are all important skills but will vary in usage depending on what you specialize in.
For example, if you’re most statistically grounded, you might spend more time on inferential statistics. Conversely, if you’re more interested in text analytics, you might spend more time learning NLP, or if you’re interested in decision science, you might focus on explanatory modeling. You get the point.
With that said, let’s dive into what I believe are the 10 most practical data science skills:
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