Typically, chemistry students learn to work with spreadsheets, which are a powerful tool for processing data. However, they are limited in comparison to data science tools. For example, spreadsheets are limited in storage capacity and memory and cannot accommodate big data sets that are routinely being generated. They are also limited in capacity for image analysis and automated data processing and cannot be used for other complex analyses such as dimensionality reduction, simulations and machine learning. All of these attributes are currently needed in most modern chemistry labs. Big data will continue to impact a wide array of STEM and non-STEM professions, including clinical science, medicine, dentistry, environmental science, biology, chemistry, physics, math and social sciences. Exposing undergraduates to computer programming provides invaluable skills for whatever career path they ultimately choose to follow. The data that students of this generation will most likely be facing will not fit well into spreadsheets, will be complicated (unstructured) to extract information from, and will require additional programming skills to deal with other complexities. Therefore, undergraduates with exposure to computing will gain a competitive edge over peers who have not had exposure because they will be more efficient and effective scientists.