Being able to count on digitally competent students unlocks opportunities for teaching chemistry . Naturally, programming allows students to surpass the "blackbox" approach to computational chemistry by giving them hands-on access to source code . It also allows students to directly interact with modern instrumentation and automated data acquisition as can be done in LabVIEW or Python interfacing with an Arduino or Raspberry Pi . The Jupyter Book Scientific Computing for Chemists with Python contains many examples of chemical applications to programming, such as producing calibration curves, NMR peak finding, and microscopic image analysis. However, digital skills go beyond just programming, and it is important that, as educators, we do not shy away from details of statistics in data analysis or the problems of non-standard file formats in working with experimental equipment . A data-trained student could use Jupyter Notebooks or Electronic Laboratory Notebooks in analytical or inorganic chemistry laboratory exercises for data analysis and visualisation . Traditional lab techniques may be complemented by exercises in automation .