Most of the mathematics required for Data Science lie within the realms of statistics and algebra, which explains the disproportionate number of these courses listed below. A few other areas are included to round out the list, including calculus, finite mathematics, and a few more advanced offerings; however, the essence of the skills on parade here are statistical and algebraic in nature.
Statistics, in particular, is at the very foundation of Data Science,...
And for software engineers or data analysts as well, in random order:
Not being able to work well in a team
Using jargon that stakeholders don't understand
Being perfectionist: perfection is always associated with negative ROI, in the business world: 20% of your time spent on a project yields 80% of the value; the remaining 80% yields the remaining 20% (this is also known as the law of diminishing returns)