Tag: data-science-big-data-and-scientific-computing

Seven pioneers helped establish numerical computing through electronic scientific machines, optimization, error analysis, matrix algorithms, data analysis, and MATLAB.

Seven numerical-computing pioneers helped establish stable matrix algorithms, portable libraries, high-performance implementations, and modern linear-algebra software.

Seven architects and software pioneers helped push scientific computing from the CDC 6600 and Cray systems to massive parallelism, Beowulf clusters, and multithreaded machines.

Seven pioneers helped define the limits, scaling laws, compilers, multithreading, and interconnection architectures behind modern parallel computing.

Seven statisticians and software creators helped establish exploratory data analysis, S, R, generalized models, statistical learning, and programmable statistical ecosystems.

Seven open-source developers helped turn Python into a scientific and data-computing platform through NumPy, SciPy, IPython, pandas, and ecosystem education.

Seven distributed-systems engineers helped move MapReduce from Google’s internal programming model into Hadoop and production-scale open-source big-data infrastructure.

Seven researchers and engineers helped grow Spark from Berkeley research into a general engine for batch, SQL, streaming, and machine-learning workloads.

Seven statisticians, software builders, and industry practitioners helped turn exploratory analysis, open-source tools, and Internet-scale analytics into the modern data-science discipline.

Seven visualization pioneers helped establish visual encodings, exploratory graphics, information design, treemaps, design methodology, D3, and interactive visualization systems.