Lorgan believes Machine learning and statistics are part of data science with its origin being Mathematics. The word learning in machine learning means that the scientific algorithms depend on some data, also referred as a training set, to fine-tune a model or algorithm parameters.

This includes but not limited to many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this “learning” category. For instance, an unsupervised clustering – a statistical and data science technique, aims at detecting clusters and cluster structures without any prior knowledge or training to help the algorithm’s classification. Techniques can also be referred as hybrid, such as semi-supervised classification.

Lorgan believes that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In our view, data science covers:

  • machine learning
  • automating machine learning
  • data visualisation
  • dashboards
  • deployment of models in production mode
  • automated data-driven decisions