the Nest of Students

MATLAB R2024a v24.1.0.2537033 x64 [Size: 12.375 GB] ... The company MathWorks is the most complete software computing computer production in … program main the company that, in fact, come, it is software MATLAB (stands for Matrix Laboratory and able laboratories, matrix), which is one of the most advanced software numerical calculations, and mathematical and a programming language, advanced, and fourth-generation and it is possible to visualize and draw the functions and data it provides. Icon and sign MATLAB that with the manufacturer’s logo is also identical from the wave equation, etc. of the membrane in L-shape and the Special Functions is extracted. Competitors of MATLAB can be used to Mathematica, etc. Maple and Mathcad noted.

Facilities and features of the software MathWorks MATLAB: – Perform a variety of calculations, heavy and complicated math – Development environment for managing code, files and data – Discover the methods to faster reach a solution – A variety of mathematical functions for linear algebra, etc. statistics. analysis, February,, optimization, etc. filtering, etc. old style numeric and… – Draw functions of the graphics were two-dimensional and three-dimensional visualization information – Design and build user interfaces under the programming languages C++, C or Java – Ability to test and measure the exact functions and charts – Possibility of signal processing, communications, image and video – Is there a box of tools from various companies, engineering, navigation, applications monitoring, such as telecommunications, control, fuzzy, etc. estimate the … statistics. gathering data, simulation system, neural network, etc. probability and… – Ability to biological computing ...

Rakesh K. Jain, Prashant Shantaram Dhotre, Deepak Tatyasaheb Mane, Parikshit Narendra Mahalle - 251 pages - Language: ‎English - Publisher: ‎CRC Press; (May, 2023).

This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation.

Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. + Discusses broad background of data science and its fundamentals. + Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. + Includes python programming libraries to solve complex problems. + Addresses various real-world applications of data science based civil engineering use cases.

This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.

Language: English - Level: Beginner - Number of Lessons: 78 - Duration: 5 hours and 26 minutes.

Statistics for Business Analytics: Data Analysis with Excel is a training course on the importance of statistics in business and business data analysis in Excel software, published by Udemy Academy. Statistical modeling is a very important skill for data analysts, and in this training course you will practice this skill with Excel software. Today, data has included all parts of our lives, and the success of business and various decisions in various industries depends on access to appropriate data and their correct analysis. The world is moving day by day towards a purely data-centric direction, and in this direction, many job positions have been created for data engineers and analysts.

This course is completely comprehensive and includes all the details of statistical modeling and business analysis. What you will learn?: Data Analysis with Excel:Basic principles and basics of statistics and their application in the world of business and various industries + Carrying out data analysis and analysis projects in the powerful Excel software environment + Various statistical methods and their exploitation to solve business problems and finalize data-driven decisions + Statistical assumption test with Excel software + Data-driven decision making and its principles + Business data analysis with descriptive statistics and statistical inference in Excel software + Construction and interpretation of various statistical models based on business data + Implementation of statistical analysis or regression analysis in Excel software to predict the future + Different techniques for analyzing huge and large data sets + Evaluation of different scenarios using available data ...

Language: English - Education Time: 7 hours and 28 minutes - Level: Elementary, Secondary - Size: 2.72 GB.

Data analysis is one of the leading jobs in the current technology market. As per the forecasts of Glassdoor and World Economic Forum, the demand for data scientists will also increase in the next few years. We are generating huge data every day from different domains like Social Media, Healthcare, Sensor data… we have a great tool to analyze them and the tool is R. R programming is a powerful language used widely for data analysis and statistical computing. It is completely free and has rich repositories for packages.

In this course first, you will learn how to install R and start programming on it. It will also help you to know the programming structures and functions. This R programming in Data Science and Data Analytics covers all the steps of Exploratory data analysis, Data pre-processing, and Modelling process. In EDA sections you will learn how to import data sets and create data frames from it. Then it will help you to visualize the variables using different plots. It will give you an initial structure of your data points. In Data pre-processing sections you will get the full idea of Missing value & outliers treatment and data split methods. Finally, you will be able to generate machine learning models using Linear and Logistic Regression.

This R programming for data science and data analytics is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

A. C. Faul ... 334 pages - Language: English - Publisher: Chapman and Hall/CRC; (August, 2019) - AmazonSIN: ‎B07VYV4NLM.

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

Jonathan Schwabish ... 384 pages - Language: English - Publisher: A.K. Peters/CRC Press; (May, 2023) - ISBN-10: ‎1032343265 - ISBN-13: ‎978-1032343266.

This book closes the gap between what people think Excel can do and what they can achieve in the tool. Over the past few years, recognition of the importance of effectively visualizing data has led to an explosion of data analysis and visualization software tools. But for many people, Microsoft Excel continues to be the workhorse for their data visualization needs, not to mention the only tool that many data workers have access to. Although Excel is not a specialist data visualization platform, it does have strong capabilities. The default chart types do not need to be the limit of the tool’s data visualization capabilities, and users can extend its features by understanding some key elements and strategies.

Data Visualization in Excel provides a step-by-step guide to creating more advanced and often more effective data visualizations in Excel and is the perfect guide for anyone who wants to create better, more effective, and more engaging data visualizations.

Lars Grüne,‎ Jürgen Pannek ... 456 pages - Publisher: Springer; 2nd edition (November 11, 2016) - Language: English - ISBN-10: 3319460234 - ISBN-13: 978-3319460239.

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine―the core of any nonlinear model predictive controller―works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.

Mircea Ancau ... 295 pages - Publisher: Cambridge Scholars Publishing; (November, 2019) ... Language: English - ISBN-10: 1527538494 - ISBN-13: 978-1527538498.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Wenyu Sun, Ya-Xiang Yuan ... 688 pages - Publisher: Springer; (May, 2006) ... Language: English - ISBN-10: 0387249753 - ISBN-13: 978-0387249759.

This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea’s motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference.

Subal C. Kumbhakar, Hung-Jen Wang, Alan P. Horncastle ... 374 pages - Language: ‎ English - Publisher: ‎ Cambridge Univ. Press; (January, 2015) - ISBN-10: ‎ 1107609461 - ISBN-13: 978-1107609464.

Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.

MathWorks MATLAB 2022a v9.12.0.1884302  [Size: 20.8 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. 

Tonny J. Oyana ... 354 pages - Language: ‎English - Publisher: CRC Press; 2nd edition (September, 2020) - ISBN-10: 0367860856 - ISBN-13:‎ 978-0367860851.

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.

New in the Second Edition: Includes new practical exercises and worked-out examples using R + Presents a wide range of hands-on spatial analysis worktables and lab exercises + All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences + Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods + Explains big data, data management, and data mining

Frederick J. Gravetter, Larry B. Wallnau, Lori-Ann B. Forzano, James E. Witnauer ... 608 pages - Language: English - Publisher:‎ Cengage Learning; 10th edition (January, 2020) - ISBN-10: 0357365291 - ISBN-13: ‎978-0357365298.

Updated with the latest research, Gravetter/Wallnau/Forzano/Witnauer's Essentials of Statistics for the Behavioral Sciences, 10th Edition delivers straightforward instruction, hands-on learning tools and real-world examples to help you go beyond memorizing formulas to truly understanding the hows and whys of statistics. Giving extra focus to the topics students typically struggle with most, the authors take time to fully explain statistical concepts. Integrated applications reinforce concepts, offering further support to ensure that even those with a weak background in mathematics can fully grasp statistics. The authors also illustrate how an understanding of statistical procedures will help you comprehend published findings -- ultimately leading you to become a savvy consumer of information. Also available, the MindTap digital learning solution helps you learn on your own terms.

John Sall, Mia L. Stephens, Ann Lehman, Sheila Loring ... 1017 pages - Language: English - Publisher: SAS Institute; 6th edition (February, 2017).

This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. + New and improved ways to create formulas in JMP directly from the data table or dialogs. + Interface updates, including improved menu layout. + Updates and enhancements in many analysis platforms. + New ways to get data into JMP and to save and share JMP results. + Many new features that make it easier to use JMP.

Wengang Zhang ... 240 pages - Publisher: Springer; (April, 2019) ... Language: English - AmazonSIN: B07R93QW6S.

This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach’s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis.

Statgraphics Centurion 19.1.1 for x64 [Size: 188 MB] ... Statgraphics Centurion 18 is a comprehensive Windows desktop product for statistical analysis, data visualization and predictive analytics. It contains over 260 procedures covering a wide range of data analysis techniques. Statgraphics 18 features an easy-to-use GUI that does not require learning a complicated command language. Especially helpful to practitioners is the StatAdvisor, which explains the results of statistical analyses in a manner suitable for presentation to non-statisticians. Statgraphics 18 is a major upgrade that contains many new features, including: A new file format for handling big data. The 64-bit version can now analyze datasets containing in excess of 100 million rows. * 9 new interactive Statlets for dynamic data visualization, including sunflower plots, population pyramids, wind roses, and violin plots. * Improved demographic maps, with support for SHP files. * Dialog-box driven procedures for accessing R libraries to use classification and regression trees (CART), text mining, multidimensional scaling, analysis of arbitrarily censored data, and seasonal adjustment with X-13ARIMA-SEATS. * Over a dozen other new statistical procedures, including equivalence analysis and noninferiority testing, orthogonal regression, capability control charts, multivariate tolerance intervals, tests for multivariate normality, and random number generation for multivariate normal distributions. * New definitive screening designs in the DOE Wizard.* A new network installation program which allows users to check out seats for use on individual computers. * Extensive enhancements to the process capability analysis procedures to implement the methods described in the new book by Dr. Neil W. Polhemus, Process Capability Analysis: Estimating Quality to be published by Chapman and Hall/CRC Press in December, 2017.

Kaushik Kumar, J. Paulo Davim ... 179 pages - AmazonSIN: B07V7SN988 ... Publisher: Wiley-ISTE; (July, 2019) - Language: English.

Optimization is central to any problem involving decision-making in engineering. Optimization theory and methods deal with selecting the best option regarding the given objective function or performance index. New algorithmic and theoretical techniques have been developed for this purpose, and have rapidly diffused into other disciplines. As a result, our knowledge of all aspects of the field has grown even more profound. In Optimization for Engineering Problems, eminent researchers in the field present the latest knowledge and techniques on the subject of optimization in engineering. Whereas the majority of work in this area focuses on other applications, this book applies advanced and algorithm-based optimization techniques specifically to problems in engineering.

Dirk P. Kroese; Zdravko I. Botev; Thomas Taimre; Radislav Vaisman .... 532 pages - Language: English - AmazonSIN: B081S6BQ2Y - Publisher: Chapman and Hall/CRC; (November, 2019).

The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. + Presentation is self-contained, accessible, and comprehensive. + Extensive list of exercises and worked-out examples. + Many concrete algorithms with Python code. + Full color throughout.