Search Results - mathematical statistics data processing.

Design and Analysis of Experiments . Volume 3, Special Designs and Applications

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Practical data analysis by Cuesta, Hector

Table of Contents: “…Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1:Getting Started; Computer science; Artificial intelligence (AI); Machine Learning (ML); Statistics; Mathematics; Knowledge domain; Data, information, and knowledge; The nature of data; The data analysis process; The problem; Data preparation; Data exploration; Predictive modeling; Visualization of results; Quantitative versus qualitative data analysis; Importance of data visualization; What about big data?…”
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Data Science for Dummies. by Pierson, Lillian

Table of Contents: “…Title Page; Copyright Page; Table of Contents; Foreword; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go from Here; Part 1 Getting Started with Data Science; Chapter 1 Wrapping Your Head around Data Science; Seeing Who Can Make Use of Data Science; Analyzing the Pieces of the Data Science Puzzle; Collecting, querying, and consuming data; Applying mathematical modeling to data science tasks; Deriving insights from statistical methods; Coding, coding, coding -- it's just part of the game; Applying data science to a subject area.…”
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Learning pandas by Heydt, Michael

Table of Contents: “…-- The process of data analysis -- The process -- Ideation -- Retrieval -- Preparation -- Exploration -- Modeling -- Presentation -- Reproduction -- A note on being iterative and agile -- Relating the book to the process -- Concepts of data and analysis in our tour of pandas -- Types of data -- Structured -- Unstructured -- Semi-structured -- Variables -- Categorical -- Continuous -- Discrete -- Time series data -- General concepts of analysis and statistics -- Quantitative versus qualitative data/analysis -- Single and multivariate analysis -- Descriptive statistics -- Inferential statistics -- Stochastic models -- Probability and Bayesian statistics -- Correlation -- Regression -- Other Python libraries of value with pandas -- Numeric and scientific computing -- NumPy and SciPy -- Statistical analysis -- StatsModels -- Machine learning -- scikit-learn -- PyMC -- stochastic Bayesian modeling -- Data visualization -- matplotlib and seaborn -- Matplotlib -- Seaborn -- Summary -- Chapter 2: Up and Running with pandas -- Installation of Anaconda -- IPython and Jupyter Notebook -- IPython -- Jupyter Notebook -- Introducing the pandas Series and DataFrame -- Importing pandas -- The pandas Series -- The pandas DataFrame -- Loading data from files into a DataFrame -- Visualization -- Summary -- Chapter 3: Representing Univariate Data with the Series -- Configuring pandas -- Creating a Series -- Creating a Series using Python lists and dictionaries -- Creation using NumPy functions -- Creation using a scalar value.…”
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Computational molecular evolution by Yang, Ziheng

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Modelling Under Risk and Uncertainty : an Introduction to Statistical, Phenomenological and Computational Methods. by De Rocquigny, Etienne

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Machine learning and artificial intelligence in marketing and sales : essential reference for practitioners and data scientists by Syam, Niladri (Niladri B.), 1964-, Kaul, Rajeeve

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Uncertainty management in remote sensing of climate data : summary of a workshop by McConnell, Martha Clarke

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Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification

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Structural equation modeling : applications in ecological and evolutionary biology

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Stochastic and global optimization

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Algorithmic trading methods : applications using advanced statistics, optimization, and machine learning techniques by Kissell, Robert, 1967-

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Modern Interdisciplinary University Statistics Education : Proceedings of a Symposium. by Committee on Applied and Theoretical Statistics, Nat

Table of Contents: “…""THE PROGRAM AT UCSB""""CONCLUDING REMARKS""; ""References""; ""Discussion""; ""A University Statistics Program Based on Quality Principles""; ""INTRODUCTION""; ""VOICE OF THE CUSTOMER""; ""VOICE OF THE PROCESS""; ""CURRICULUM PRINCIPLES""; ""SOME SUGGESTIONS FOR THE CURRICULUM""; ""Measurement""; ""Variation""; ""Mathematical Statistics and Probability""; ""Analogy and Metaphor""; ""Model Description and Presentation""; ""Bias""; ""Sense Deception""; ""Design of Observational and Randomized Experiments""; ""PEDAGOGY""; ""Grades""; ""Mastery""; ""Journal""; ""Feedback to Instructors""…”
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GIS and geocomputation for water resource science and engineering by Dixon, Barnali, Uddameri, Venkatesh

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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

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Modelling Extremal Events : for Insurance and Finance by Embrechts, Paul

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Event History Analysis : a Process Point of View. by Aalen, Odd O.

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The Oxford handbook of quantitative methods. Vol. 2, Statistical analysis

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How to be a quantitative ecologist : the 'A to R' of green mathematics and statistics by Matthiopoulos, Jason

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Statistical Physics, High Energy, Condensed Matter and Mathematical Physics : Proceedings of the Conference in Honor of C.N. Yang's 85th birthday, Singapore, 31 October - 3 Novembe...

Table of Contents: “…Thouless -- Professor C.N. Yang and statistical mechanics / F.Y. Wu -- A few pieces of mathematics inspired by real biological data / B.L. …”
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