Explore Career in Data Science (with the best online data science training institute in kenya)

  This article predominantly talks about testing as a distinct career option in data science and machine learning  (ML). It gives a brief on testing workflows and process. It also depicts the expertise and top-level skills a tester needs to possess in order to test a ML application. Testing in Data Science: Opportunity for Expansion There is a significant opportunity to explore and expand the possibilities of testing and quality assurance into the field of data science and machine learning (ML). Playing around with training data, algorithms and modeling in data science may be a complex yet interesting activity—but testing these applications is no less. A considerable amount of time goes into testing and quality assurance activities. Experts and researchers believe 20 to 30% of the overall development time is spent testing the application; and 40 to 50% of a project's total cost is spent on testing. Moreover, data science experts and practitioners often complain about havi...