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 Call number: 9781783553365:ONLINE Show nearby items on shelf Title: Python Data Analysis learn how to apply powerful data analysis techniques with popular open source Python modules Author(s): Ivan Idris Date: 2014 Publisher: Birmingham, UK: Packt Publishing Size: 348 p Note: online access: non linear lending Contents: Getting started with Python libraries -- NumPy arrays -- Statistics and linear algebra -- pandas primer -- Retrieving, processing, and storing data -- Data visualization -- Signal processing and time series -- Working with databases -- Analyzing tex tual data and social media -- Predictive analytics and machine learning -- Environments outside the Python ecosystem and cloud computing -- Performance tuning, profiling, and concurrency -- Appendix A : key concepts -- Appendix B : useful functions -- Ap p endix C : online resources ISBN: 9781783553365 Series: eBooks Series: EBL eBook Keywords: Python (Computer program language) Availability: Click here to see Library holdings or inquire at Circ Desk (x3401) Click to reserve this book Be sure to include your ID please. More info: Amazon.com More info: Barnes and Noble Full Text: Click here Location: ONLINE

 Call number: 9781482203547:ONLINE Show nearby items on shelf Title: Dynamic documents with R and knitr Author(s): Yihui Xie Date: 2014 Edition: 2nd ed. Publisher: Boca Raton: CRC Press Size: 1 online resource (190 p) Contents: Preface -- Author -- List of Figures -- List of Tables -- Introduction -- Reproducible Research -- A First Look -- Editors -- Document Formats -- Text Output -- Graphics -- Cache -- Cross Reference -- Hooks -- Language Engines -- Tricks and Solutions -- Publishing Reports -- R Markdown -- Applications -- Other Tools -- Appendix -- Internals -- Bibliography -- Index. ISBN: 9781482203547 Series: EBL eBook Series: eBooks Keywords: Statistics, Data processing Availability: Click here to see Library holdings or inquire at Circ Desk (x3401) Click to reserve this book Be sure to include your ID please. More info: Amazon.com More info: Barnes and Noble Full Text: Click here Location: ONLINE

 Call number: 10310727:ONLINE Show nearby items on shelf Title: A Guide to Microsoft Excel 2007 for scientists and engineers [electronic resource] Author(s): Bernard V. Liengme David J. Ellert Date: 2009 Size: 1 online resource (336 p.) Contents: 1. The Microsoft Excel Window 2. Basic Operations 3. Printing a Worksheet 4. Using Functions 5. Decision Functions 6. Charts 7. Curve Fitting 8. User-defined Functions 9. Modelling I 10. Solving Equations 11. Numerical Integration 12. Differential Equations 13. Modelling II 14. Statistics for Experimenters 15. Report Writing ISBN: 9780123746238 Series: ebrary eBook Series: eBooks Keywords: Engineering - Data processing , Microsoft Excel (Computer file) , Science - Data processing Availability: Click here to see Library holdings or inquire at Circ Desk (x3401) Click to reserve this book Be sure to include your ID please. More info: Amazon.com More info: Barnes and Noble Full Text: Click here Location: ONLINE

 Call number: SPRINGER-2016-9783319285887:ONLINE Show nearby items on shelf Title: Computational Diffusion MRI MICCAI Workshop, Munich, Germany, October 9th, 2015 Author(s): Date: 2016 Size: 1 online resource (63 p.) Note: 10.1007/978-3-319-28588-7 Contents: An Efficient Finite Element Solution of the Generalised Bloch-Torrey Equation for Arbitrary Domains: L. Beltrachini et al -- Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation: Feng Shi et al -- Holist ic Image Reconstruction for Diffusion MRI: V. Golkov et al -- Alzheimer’s Disease Classification with Novel Microstructural Metrics from Diffusion-Weighted MRI: T. M. Nir et al -- Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squar es Variable Separation: H. Farooq et al -- Multi-Tensor MAPMRI: How to Estimate Microstructural Information from Crossing Fibers: M. Zucchelli et al -- On the Use of Antipodal Optimal Dimensionality Sampling Scheme on the Sphere for Recovering Intra-Voxel Fibre Structure in Diffusion MRI: A.P. Bates et al -- Estimation of Fiber Orientations Using Neighborhood Information: C. Ye et al -- A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images: V. Gupta et al -- Alignment of Tractograms as Linear Assignment Problem: N. Sharmin -- Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo: H. Wu et al -- Adaptive Enhancement in Diffusion MRI Through Propagator Sharpeni ng: T. Dela Haije et al -- Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Image Information Transfer: Geng Chen et al -- Crossing versus Fanning: Model Comparison Using HCP Data: A. Ghosh et al -- White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss: G. Zimmerman Moreno et al -- A Temperature Phantom to Probe the Ensemble Average Propagator Asymmetry: an In-Silico Study: M. Pizzolato et al -- Registration Strategies for Whole-Body Diffusi on-Weighted MRI Stitching: J. Ceranka et al -- HARDI Feature Selection, Registration and Atlas Building for A$\beta$ Pathology Classification: E. Schwab et al -- Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions: J. E. Villalon-Reina et al ISBN: 9783319285887 Series: eBooks Series: SpringerLink (Online service) Series: Springer eBooks Keywords: Mathematics , Computer simulation , Image processing , Bioinformatics , Computer mathematics , Visualization , Statistics , Mathematics , Visualization , Computational Biology/Bioinformatics , Computational Science and Engineering , Simulation and Modeling , Image Processing and Computer Vision , Statistics for Life Sciences, Medicine, Health Sciences Availability: Click here to see Library holdings or inquire at Circ Desk (x3401) Click to reserve this book Be sure to include your ID please. More info: Amazon.com More info: Barnes and Noble Full Text: Click here Location: ONLINE

 Call number: SPRINGER-2009-9780387848587:ONLINE Show nearby items on shelf Title: The Elements of Statistical Learning [electronic resource] Data Mining, Inference, and Prediction Author(s): Trevor Hastie Robert Tibshirani Jerome Friedman Date: 2009 Edition: Second Edition Publisher: New York, NY : Springer New York : Imprint: Springer Size: 1 online resource Note: Springer e-book platform Note: Springer 2013 e-book collections Note: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge ofunderstanding these data ha s led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are oftenexpressed with different terminology. This book d escribes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples aregiven, with a liberal use of color graphics. It is a valuable resource f or statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervisedlearning. The many topics include neural networks, support vector machines, classification tre es and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in theoriginal, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods forwide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in thisarea: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling softw are and environment in R/S-PLUS and invented principal curves andsurfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inve Note: Springer eBooks Contents: Overview of Supervised Learning Linear Methods for Regression Linear Methods for Classification Basis Expansions and Regularization Kernel Smoothing Methods Model Assessment and Selection Model Inference and Averaging Additive Models, Trees, and Related Methods Boosting and Additive Trees Neural Networks Support Vector Machines and Flexible Discriminants Prototype Methods and Nearest Neighbors Unsupervised Learning Random Forests Ensemble Learning Undirected Graphical Models High Dimensional Problems: p ? N ISBN: 9780387848587 Series: e-books Series: SpringerLink (Online service) Series: Springer Series in Statistics, 0172-7397 Series: Mathematics and Statistics (Springer-11649) Keywords: Statistics , Data mining , Artificial intelligence , Bioinformatics , Biology Data processing , Mathematical statistics Availability: Click here to see Library holdings or inquire at Circ Desk (x3401) Click to reserve this book Be sure to include your ID please. More info: Amazon.com More info: Barnes and Noble Full Text: Click here Location: ONLINE