Statistics is a crucial part of data science. If you think about the 3 phases of a typical data science project, Data Collection, Data Analysis and Results Communications, statistics is critical in the first two. You need to apply appropriate sampling techniques so data collected are not biased. In Phase II, you need modeling skills to derive deep insights about the data if the business question has to be answered through modeling. Visualization comes before modeling as part of Exploratory Data Analysis. It sheds light on trend, patterns and often implies about findings. Visualization is always needed in a data science project. The weight of Statistics increases proportionally to the degree of project complexity.
Programming and communication are must-have skills for any data science project.