![]() ![]() A number of efforts from diverse computational and data science communities are working to formulate the core methodological principles for conceptualizing, representing, modeling and understanding of complex processes that are observed as large, heterogeneous and multisource information streams. The history of data science commenced at least half a century ago ( 6). This has nascent and direct implications on public-private partnerships, research funding priorities, and team-based quantitative-qualitative education. Blending human-machine interfaces and data-driven inference is required to improve the long-term odds of survival and increase the impact of future decision support systems that rely on large amounts of complex empirical evidence. The distinct, yet equally important, contributions of fundamental scientific principles and artistic vision scale opposite slopes of the majestic Data Science mountain scape. A less recognized characteristic of Data Science is its intrinsic dual reliance on quantitative basic sciences techniques as well as qualitative Artistry ( 5). It is commonly acknowledged that Data Science is an extremely transdisciplinary field. ![]() Applying innovative data science techniques to tackle challenging computational problems requires substantial investment in both – hard and soft skills. There is a clear evidence of the growing synergies between the four fundamental discovery paradigms – experimental, theoretical, computational, and data sciences ( 4). ![]() As proxies of complex natural phenomena, Big Data provide a unique view into the intrinsic process organization, enabling information extraction, reinforced learning, model-free inference, and deep understanding of the underlying systems. A very similar polarity governs the Big Data Science theory and practice – making sense of enormous amounts of heterogeneous, multi-source, multi-scale, incomplete and incongruent data requires rigorous foundational training paired with dexterous artistic skills. Human expression of physical experiences and our reflection on abstract ideas cover the continuum – from uttering musical tones, painting canvases, and art sculpting, to conceiving fundamental geometric structures, interpreting non-Euclidian topological objects, identifying mathematical patterns, and inventing data analytics that explain complex environments or elucidate effective messaging and communication. “Fourier-series expansion” of continuous functions ( 2), or “Banach spaces” vs. “ a word may be worth a thousand pictures” ( 1), “Taylor-series expansion” vs. Common natural laws explain well-known dichotomies like “ a picture is worth a thousand words” vs. ![]()
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