Complex algorithms and sophisticated tools make up a large part of a data scientist's day. In addition to data analysis tools, keeping up with the latest tools in data acquisition, data cleansing, data warehousing and data visualization is becoming increasingly important as the historically separate roles of data scientist and analyst become merged for increased efficiency. Python is the lingua franca of data science but knowledge of R, SAS, SQL, and sometimes Java, Scala, Julia among others must also be acquired at the foundational level itself. Technical soundness is a must for moving forward towards solutions while avoiding roadblocks.