Data Science

by John D. Kelleher

A number of factors have contributed to the recent growth of data science. As we have already touched upon, the emergence of big data has been driven by the relative ease with which organizations can gather data. Be it through point-of-sales transaction records, clicks on online platforms, social media posts, apps on smart phones, or myriad other channels, companies can now build much richer profiles of individual customers. Another factor is the commoditization of data storage with economies of scale, making it less expensive than ever before to store data. There has also been tremendous growth in computer power. Graphics cards and graphical processing units (GPUs) were originally developed to do fast graphics rendering for computer games. The distinctive feature of GPUs is that they can carry out fast matrix multiplications. However, matrix multiplications are useful not only for graphics rendering but also for ML. In recent years, GPUs have been adapted and optimized for ML use, which has contributed to large speedups in data processing and model training. User-friendly data science tools have also become available and lowered the barriers to entry into data science. Taken together, these developments mean that it has never been easier to collect, store, and process data.