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The vertical dimension of the figure reflects the degree to which domain context plays a role in the process. Domain context can play a key role in these exploratory steps, even in relatively well-defined processes such as predictive modeling (for example, as characterized by CRISP-DM 5) where human expertise in defining relevant predictor variables can be critical.įigure 1 provides a conceptual framework to guide our discussion of automation in data science, including aspects that are already being automated as well as aspects that are potentially ready for automation. This desire and potential for automation is the focus of this article.Īs illustrated in these examples, data science is a complex process, driven by the character of the data being analyzed and by the questions being asked and is often highly exploratory and iterative in nature. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise.
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The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels.
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50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions.Īs another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers.
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Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems.ĭata science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.