This article explores 10 underrated soft skills that are crucial for data scientists to thrive in their careers. These skills range from communication and empathy to adaptability and time management, with examples and evidence to support their importance in the data science field.
Bias in data science is like a silent operator. It exists subtly in many forms, from data collection to interpretation, and if not checked, it can derail the objectivity and reliability of your analyses.
Here, we delve into five statistical paradoxes that every data scientist should be aware of, complete with specific examples and in-depth explanations of their significance.
Despite the importance of reproducibility, there are several challenges in achieving it.
In Thinking Clearly: A Data Scientist’s Guide to Understanding Cognitive Biases, each chapter provides an in-depth exploration of one of a wide ranging number of cognitive biases, including its definition, examples, and the consequences it can have on decision-making and problem-solving.
