Knowledge of statistics To be a data scientist
Very good question!

Statistics is the main core. Not only theoretical understanding but practical use of statistical concepts are very important (Applied Statistics). Moreover working with statistical Softwares to have some ideas how they work and what are usual input-outputs and visualizations.

More important is the main fields of Data Science namely Data Mining, Machine Learning and Pattern Recognition. They are all built on statistics (try to get a course on Statistical Learning Theory to get some ideas).

After statistics, computer knowledge in general is important. Get to know the concepts of programming (what is a good program and why, which language is proper for which task, what is algorithm and how you should evaluate, produce or read them, etc.). In Data Science, reporting the results are important. Get to know some visualization techniques/libraries which are handy and commonly used.

Then you come to the concept of Database and even more general "What is data in real-world?" Try to get your hands dirty with some SQL-like query languages on some toy examples to learn what the data is and how you load, transform or extract them.

The last but not least are Machine Learning/Data Mining/Pattern Recognition skills. I recommend a MSc. in this field (however it's not necessary but my personal experience as an interviewer shows that usually people with this background stand higher).

These are the main things came to my mind. Please note that 


This is just my opinion and based on my experience.
There are more details that I skipped to keep the answer as generally valid as possible.


Good Luck!