This text provides a practical, hands-on introduction to data conceptualization, measurement, and association through active learning. Students get step-by-step instruction on data analysis using the latest version of SPSS and the most current General Social Survey data. The text starts with an introduction to computerized data analysis and the social research process, then walks users through univariate, bivariate, and multivariate analysis using SPSS. The book contains applications from across the social sciences-sociology, political science, social work, criminal justice, health-so it can be used in courses offered in any of these departments. The Eleventh Edition uses the latest general Social Survey (GSS) data, and the latest available version of SPSS. The GSS datasets now offer additional variables for more possibilities in the demonstrations and exercises within each chapter.

System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today's debugging team's point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources. The book presents the rapidly growing application areas of systems and software modeling, including intelligent synthetic characters, human-machine interface, menu generators, user acceptance analysis, picture archiving and software systems. Students, research scholars, academicians, scientists and industry practitioners will benefit from the book as it provides better insights into modern related global trends, issues and practices.

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

Das sachliche Hauptziel der Wahrscheinlichkeitsrechnung ist die mathematische Erforschung von Massenerscheinungen. In formaler Hin sicht bedeutet das einen erkenntnistheoretisch genugend scharf ab gegrenzten Problemkreis: diejenigen Gesetzmassigkeiten der Erscheinun gen und Vorgange theoretisch zu erfassen, die durch das Massenhafte an ihnen (d. h. durch das Auftreten einer grossen Anzahl von in gewissem Sinne gleichberechtigten Ereignissen, Grossen u. dgl. m. ) in ihren Haupt zugen bedingt sind, so dass daneben die individuelle Beschaffenheit der einzelnen Ingredienten gewissermassen in den Hintergrund tritt. Rein mathematisch fuhrt das endlich zu Infinitesimalbetrachtungen einer spezifischen Gattung, indem die fur eine unendlich grosse Ingredienten anzahl geltenden Grenzgesetze systematisch untersucht und begrundet werden. In diesem Zusammenhang erscheinen die unter dem Namen von "Grenzwertsatzen" bekannten asymptotischen Gesetze der Wahr scheinlichkeitsrechnung keinesfalls als ein isoliertes Nebenstuck dieser Wissenschaft, sondern sie bilden im Gegenteil den wesentlichsten Teil ihrer Problematik. Diese "asymptotische" Wahrscheinlichkeitsrechnung ist als mathe matische Wissenschaft noch ziemlich weit davon entfernt, ein einheit liches Ganzes zu bilden. Vor wenigen Jahren zahlte sie zu ihren Ergeb nissen nur ein paar ganz abgesondert stehender, durch keinen allgemeinen Standpunkt vereinigter Grenzwertsatze. Nur in der allerletzten Zeit konnte sie gewisse neue Aussichtspunkte erringen, die die Hoffnung erwecken, fur dieses theoretisch grundlegende und auch fur die Natur wissenschaften ausserst wichtige Forschungsgebiet in absehbarer Zeit eine einheitliche Theorie zu gewinnen. Es mussen hier einerseits die aus der physikalischen Statistik kommenden, mit der sog."

Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key Features Gain a comprehensive overview of different machine learning techniques Explore various methods for selecting a particular algorithm Implement a machine learning project from problem definition through to the final model Book DescriptionWith huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you'll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you'll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learn Define a problem that can be solved by training a machine learning model Obtain, verify and clean data before transforming it into the correct format for use Perform exploratory analysis and extract features from data Build models for neural net, linear and non-linear regression, classification, and clustering Evaluate the performance of a model with the right metrics Implement a classification problem using the neural net package Employ a decision tree using the random forest library Who this book is forIf you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

This innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB (R), and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.

Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications Key Features Explore advanced financial models used by the industry and ways of solving them using Python Build state-of-the-art infrastructure for modeling, visualization, trading, and more Empower your financial applications by applying machine learning and deep learning Book DescriptionThe second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis. What you will learn Solve linear and nonlinear models representing various financial problems Perform principal component analysis on the DOW index and its components Analyze, predict, and forecast stationary and non-stationary time series processes Create an event-driven backtesting tool and measure your strategies Build a high-frequency algorithmic trading platform with Python Replicate the CBOT VIX index with SPX options for studying VIX-based strategies Perform regression-based and classification-based machine learning tasks for prediction Use TensorFlow and Keras in deep learning neural network architecture Who this book is forIf you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key Features Build state-of-the-art algorithms that can solve your business' problems Learn how to find hidden patterns in your data Revise key concepts with hands-on exercises using real-world datasets Book DescriptionStarting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learn Implement clustering methods such as k-means, agglomerative, and divisive Write code in R to analyze market segmentation and consumer behavior Estimate distribution and probabilities of different outcomes Implement dimension reduction using principal component analysis Apply anomaly detection methods to identify fraud Design algorithms with R and learn how to edit or improve code Who this book is forApplied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

Discover how to build and backtest algorithmic trading strategies with Zipline Key Features Get to grips with market data and stock analysis and visualize data to gain quality insights Find out how to systematically approach quantitative research and strategy generation/backtesting in algorithmic trading Learn how to navigate the different features in Python's data analysis libraries Book DescriptionAlgorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses. The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. You'll also focus on time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization. What you will learn Discover how quantitative analysis works by covering financial statistics and ARIMA Use core Python libraries to perform quantitative research and strategy development using real datasets Understand how to access financial and economic data in Python Implement effective data visualization with Matplotlib Apply scientific computing and data visualization with popular Python libraries Build and deploy backtesting algorithmic trading strategies Who this book is forThis book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Python's core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful.