#
Science -- Data processing
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The concept ** Science -- Data processing** represents the subject, aboutness, idea or notion of resources found in **University of Missouri Libraries**.

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Science -- Data processing
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The concept

**Science -- Data processing**represents the subject, aboutness, idea or notion of resources found in**University of Missouri Libraries**.- Label
- Science -- Data processing

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- 68 scientific & engineering programs for the IBM-PC and PC XT
- A guide to Microsoft Excel 2007 for scientists and engineers
- A primer on scientific programming with Python
- A survey of computational physics : introductory computational science
- Advanced computational methods in science and engineering
- Advances in software tools for scientific computing
- Agenda for developing e-science in research libraries : final report and recommendations to the Scholarly Communication Steering Committee, the Public Policies Affecting Research Libraries Steering Committee, and the Research, Teaching, and Learning Steering Committee
- Applications and algorithms in science and engineering : module A
- Applications of mathematics and informatics in science and engineering
- BASIC programming for scientists and engineers
- Become a Python data analyst : perform exploratory data analysis and gain insight into scientific computing using Python
- Building Data Science Teams
- Classical FORTRAN : programming for engineering and scientific applications
- Client-side data storage : keeping it local
- Cloud computing with e-science applications
- Computer systems and scientific data analysis support devices for the Earth Sciences Directorate and Space Sciences Directorate : final progress report : triannual period June through September 1993, contract NAS5-30440, Volume I
- Computer-aided experimentation : interfacing to minicomputers
- Computers and instrumentation : a practical handbook of measurement, interfacing and control circuits /Alan Carrick
- Computers and their role in the physical sciences
- Computing for engineers and scientists with Fortran 77
- Computing for scientists and engineers : a workbook of analysis, numerics, and applications
- Computing in applied science
- Computing report in science and engineering
- Cálculo cientifico con MATLAB y Octave
- Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning
- Data-Intensive Science
- Design of adaptive finite element software : the finite element toolbox ALBERTA
- Design patterns for e-science
- Designing scientific applications on GPUs
- Digital Da Vinci : computers in the arts and sciences
- Disrupted networks : from physics to climate change
- Elements of scientific computing : with 88 figures and 18 tables
- Engineering and scientific computations using MATLAB
- Excel for scientists and engineers : numerical methods
- FORTRAN 77 for engineers and scientists
- FORTRAN in the physical sciences : student text
- Fundamentals of scientific computing
- Future application and middleware technology on e-Science
- F♯ for scientists
- Guide to scientific computing in C++
- High performance computing & networking for science
- High performance computing in science and engineering '08 : transactions of the High Performance Computing Center, Stuttgart (HLRS) 2008
- High performance computing in science and engineering '13 : transactions of the High Performance Computing Center, Stuttgart (HLRS) 2013
- High speed computing : scientific applications and algorithm design
- High-performance scientific computing : algorithms and applications
- High-performance scientific computing : algorithms and applications
- Introduzione al Calcolo Scientifico : esercizi e problemi risolti con MATLAB
- Jupyter Notebook for data science
- Jupyter for data science : exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter
- Learning Jupyter 5 : explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab
- Learning Jupyter : learn how to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using Project Jupyter
- Mathematical aspects of scientific software
- Microcomputers in engineering and science
- Microprocessors and small digital computer systems for engineers and scientists
- Modeling and simulation in Scilab/Scicos
- Modeling and simulation in Scilab/Scicos with ScicosLab 4.4
- Modern software tools for scientific computing
- Networking for Science
- Open scientific data : why choosing and reusing the right data matters
- Parallel science and engineering applications : the Charm++ approach
- Practical scientific computing
- Process design for natural scientists : an agile model-driven approach
- Python and Matplotlib essentials for scientists and engineers
- Python for scientists
- Python scripting for computational science
- Python scripting for computational science
- Quantitative data in science and technology
- Reliability models for engineers and scientists
- Rice boy
- SciDAC 2007 : 24-28 June 2007, Boston Massachusetts, USA
- Science and computing with Raspberry Pi
- Science with pocket calculators
- Scientific computation
- Scientific computing : an introduction using Maple and MATLAB
- Scientific computing with MATLAB and Octave
- Scientific computing with MATLAB and Octave
- Seeking solutions : high-performance computing for science
- Steps toward large-scale data integration in the sciences : summary of a workshop
- Supercomputing and the transformation of science
- The UNISIST guidelines : a special supplement to "Information reports and bibliographies"
- The fourth paradigm : data-intensive scientific discovery
- The handbook of software for engineers and scientists
- Topics in advanced scientific computation
- Transactions on Computational Science II
- Transactions on computational science I
- Transactions on computational science III
- Transactions on computational science V : special issue on cognitive knowledge representation
- Transactions on computational science VI
- Transactions on computational science VII
- Transactions on computational science VIII
- Using R for numerical analysis in science and engineering
- Using R for numerical analysis in science and engineering
- What can we learn from PISA? : investigating PISA's approach to scientific literacy
- What is data science?
- XML in scientific computing

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`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/resource/E3BCKwexhsw/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/resource/E3BCKwexhsw/">Science -- Data processing</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>`