Data science is an umbrella term that can cover visualization, statistical inference, predictive modeling, machine learning, deep learning, artificial intelligence, data engineering, insert buzzword of the day here… (the list goes on). Regardless of what specific niche you’re trying to fit into in the data science universe, it’s likely you’ll rely on a few key languages. These popular languages include R and Python (and SQL when you have to). There are other languages out there that can be used for data analysis and machine learning, but R and Python are the most popular from what I’ve seen. I will discuss both of these primary languages below and weigh their strengths and weaknesses.
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There are various ways to turn data into actionable insights with the end goal of increasing your bottom-line. The thread that binds all of these methods together is their dependence on input data.