Data science is at the intersection of programming, mathematics and business or domain knowledge. With the recent explosion of tools and data, many people have been coming to data science from a programming or data management background, quickly learning to apply some libraries, and publishing results.
Every day you can read news articles about recent discoveries enabled by data science.
Some examples of the common pitfalls of using data.
Choosing the easiest model. Many platforms are making it extremely easy to import some data, run a model over the data, and report some results. With AI-enhanced data analytics platforms, anyone with a little IT knowledge and access to data can pick from a library of models and get some results. But how do you know whether a boosted tree or a neural network is the best model for your data?
p-hacking
Results before theory
Multiple hypothesis testing