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Data Analysis for Business, Economics, and Policy
This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
R 7,541.00
R 274.00
Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
R 958.00
Computational Metrics for Soccer Analysis
This book provides an account of the use of computational tactical metrics in improving sports analysis, in particular the use of Global Positioning System (GPS) data in soccer. As well as offering a practical perspective on collective behavioural analysis, it introduces the computational metrics available in the literature that allow readers to identify collective behaviour and patterns of play in team sports. These metrics only require the bio-dimensional geo-referencing information from GPS or video-tracking systems to provide qualitative and quantitative information about the tactical behaviour of players and the inter-relationships between teammates and their opponents. Exercises, experimental cases and algorithms enable readers to fully comprehend how to compute these metrics, as well as introducing them to the ultimate performance analysis tool, which is the basis to run them on. The script to compute the metrics is presented in Python. The book is a valuable resource for professional analysts as well students and researchers in the field of sports analysis wanting to optimise the use of GPS trackers in soccer.
R 2,198.00
R 672.00
R 977.00
Python Machine Learning
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practicesBook DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnMaster the frameworks, models, and techniques that enable machines to 'learn' from dataUse scikit-learn for machine learning and TensorFlow for deep learningApply machine learning to image classification, sentiment analysis, intelligent web applications, and moreBuild and train neural networks, GANs, and other modelsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
R 1,791.00
Interpretable Machine Learning with Python 2e
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesInterpret real-world data, including cardiovascular disease data and the COMPAS recidivism scoresBuild your interpretability toolkit with global, local, model-agnostic, and model-specific methodsAnalyze and extract insights from complex models from CNNs to BERT to time series modelsBook DescriptionInterpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting and machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What you will learnProgress from basic to advanced techniques, such as causal inference and quantifying uncertaintyBuild your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformersUse monotonic and interaction constraints to make fairer and safer modelsUnderstand how to mitigate the influence of bias in datasetsLeverage sensitivity analysis factor prioritization and factor fixing for any modelDiscover how to make models more reliable with adversarial robustnessWho this book is forThis book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
R 1,693.00
Introduction to Computational Models with Python
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website.The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware toolsProgramming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithmsPython lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under LinuxImplementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.
R 6,704.27
R 489.00
Automate the Boring Stuff with Python, 3rd Edition
If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In this fully revised edition of the best-selling classic Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You'll learn the basics of coding in Python and explore the language's rich library of modules for performing specific tasks, like scraping data from websites, searching for text across multiple files, and merging, watermarking, or encrypting PDFs. The third edition includes: Expanded coverage of developer techniques, like creating command line programs; Updated examples and new projects; Additional chapters about working with SQLite databases, speech-recognition technology, video and audio editing, and text-to-speech capabilities; Simplified explanations (based on reader feedback) of beginner programming concepts, like loops and conditionals. Even if you've never written a line of code, Automate the Boring Stuff with Python, 3rd Edition will teach you how to make your computer take on tedious tasks and do all your grunt work - the way it should be!
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R 1,627.00
Introduction To Python For Quantitative Finance: From Scratch To Productivity
Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years.
R 3,590.00
Automate The Boring Stuff With Python, 2nd Edition
In this second edition of Automate the Boring Stuff with Python, you'll learn the basics of programming in Python, the fastest growing programming language today, before moving on to create Python programs that effortlessly perform useful and impressive feats of automation. This updated edition is full of step-by-step instructions that walk through each programme. Practice projects at the end of each chapter challenge you to improve those programmes and use your newfound skills to automate similar tasks.
R 1,391.00
R 458.00
Computational Analysis of Communication
Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and othersIntegrates theory, methods, and tools to provide unified approach to the subjectIncludes sample code in Python and links to actual research questions and cases from social science and communication studiesDiscusses ethical and normative issues relevant to privacy, data ownership, and reproducible social scienceDeveloped in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
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R 1,343.00
R 580.00
Complex Survey Data Analysis with SAS
Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT® procedures.The book offers comprehensive coverage of the most essential topics, including:Drawing random samplesDescriptive statistics for continuous and categorical variablesFitting and interpreting linear and logistic regression modelsSurvival analysisDomain estimationReplication variance estimation methodsWeight adjustment and imputation methods for handling missing dataThe easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author’s website: http://mason.gmu.edu/~tlewis18/.While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation.Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.
R 2,649.41
Web Scraping with Python
If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web.Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter.Parse complicated HTML pagesDevelop crawlers with the Scrapy frameworkLearn methods to store the data you scrapeRead and extract data from documentsClean and normalize badly formatted dataRead and write natural languagesCrawl through forms and loginsScrape JavaScript and crawl through APIsUse and write image-to-text softwareAvoid scraping traps and bot blockersUse scrapers to test your website
R 1,638.00
Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask
These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide.
R 1,835.75
R 764.00
R 611.00
Linear Models with Python
Praise for Linear Models with R:This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical JournalThroughout, it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical SocietyLike its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python.Features: Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners.Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.
R 5,646.42
Applied Mixed Model Analysis
This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.
R 2,595.00
R 764.00
R 397.00
R 808.06
Eye-Tracking with Python and Pylink
Several Python programming books feature tools designed for experimental psychologists. What sets this book apart is its focus on eye-tracking.Eye-tracking is a widely used research technique in psychology and neuroscience labs. Research grade eye-trackers are typically faster, more accurate, and of course, more expensive than the ones seen in consumer goods or usability labs. Not surprisingly, a successful eye-tracking study usually requires sophisticated computer programming. Easy syntax and flexibility make Python a perfect choice for this task, especially for psychology researchers with little or no computer programming experience.This book offers detailed coverage of the Pylink library, a Python interface for the gold standard EyeLink ® eye-trackers, with many step-by-step example scripts. This book is a useful reference for eye-tracking researchers, but you can also use it as a textbook for graduate-level programming courses.
R 3,284.34
Python for Chemists
This accessible and self-contained guide provides a comprehensive introduction to the popular programming language Python, with a focus on applications in chemistry and chemical physics. Ideally suited to students and researchers of chemistry learning to employ Python for problem-solving in their research, this fast-paced primer first builds a solid foundation in the programming language before progressing to advanced concepts and applications in chemistry. The required syntax and data structures are established, and then applied to solve problems computationally. Popular numerical packages are described in detail, including NumPy, SciPy, Matplotlib, SymPy, and pandas. End of chapter problems are included throughout, with worked solutions available within the book. Additional resources, datasets, and Jupyter Notebooks are provided on a companion website, allowing readers to reinforce their understanding and gain confidence applying their knowledge through a hands-on approach.
R 1,800.00
Uncoiling Python
The oral and written traditions of the Africans of South Africa have provided an understanding of their past and the way the past relates to the present. These traditions continue to shape the past by the present, and vice versa. From the time colonial forces first came to the region in 1487, oral and written traditions have been a bulwark against what became 350 years of colonial rule, characterized by the racist policies of apartheid. The Uncoiling Python: South African Storytellers and Resistance is the first in-depth study of how Africans used oral traditions as a means of survival against European domination.Africans resisted colonial rule from the beginning. They participated in open insurrections and other subversive activities in order to withstand the daily humiliations of colonial rule. Perhaps the most effective and least apparent expression of subversion was through indigenous storytelling and poetic traditions. Harold Scheub has collected the stories and poetry of the Xhosa, Zulu, Swati, and Ndebele peoples to present a fascinating analysis of how the apparently harmless tellers of tales and creators of poetry acted as front-line soldiers.
R 970.00
Test-Driven Development with Python
The third edition of this trusted guide demonstrates the practical advantages of test-driven development (TDD) with Python and describes how to develop a real web application. You'll learn how to write and run tests before building each part of your app and then develop the minimum amount of code required to pass those tests. The result? Clean code that works. In the process, author Harry Percival teaches software and web developers the basics of Django, Selenium, Git, JavaScript, and Mock libraries, along with current web development techniques. This book--updated for Python 3.14 and Django 4--clearly demonstrates how TDD encourages simple designs and inspires confidence. Fully updated, this third edition addresses: The TDD workflow, including the unit test/code cycle and refactoring Unit tests for classes and functions and functional tests for user interactions within the browser Mock objects and the pros and cons of isolated versus integrated tests Testing and automation of deployments with a staging server Tests applied to the third-party plug-ins you integrate into your site Automatic tests using a continuous integration environment Using TDD to build a REST API with a JavaScript frontend interface
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R 458.00
Introduction to GIS Programming and Fundamentals with Python and ArcGIS®
Combining GIS concepts and fundamental spatial thinking methodology with real programming examples, this book introduces popular Python-based tools and their application to solving real-world problems. It elucidates the programming constructs of Python with its high-level toolkits and demonstrates its integration with ArcGIS Theory. Filled with hands-on computer exercises in a logical learning workflow this book promotes increased interactivity between instructors and students while also benefiting professionals in the field with vital knowledge to sharpen their programming skills. Readers receive expert guidance on modules, package management, and handling shapefile formats needed to build their own mini-GIS. Comprehensive and engaging commentary, robust contents, accompanying datasets, and classroom-tested exercises are all housed here to permit users to become competitive in the GIS/IT job market and industry.
R 6,118.00
R 1,374.00
Sensor Data Analysis and Management
Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.
R 4,753.00
Survival Kit for an Equity Analyst
Discover a one-of-a-kind blueprint to equity analyst success from an industry leader In Survival Kit for an Equity Analyst: The Essentials You Must Know, veteran analyst Shin Horie delivers a must-read, practical guide on the analysis of companies and industries. The book offers a guide to conducting industry-level analysis from a global perspective, with a particular emphasis on adjustments necessary for covering companies in emerging markets. Readers will also get: A robust background on the basic process flow of company analysis and valuationGuidelines for examining earnings drivers in major industriesTips on the qualitative profiling of companies and how to dig deeper to find nuance that financial statements don't express Perfect for early-career equity analysts themselves, as well as corporate managers who seek to understand how their organisations will be scrutinized and examined by finance professionals, Survival Kit for an Equity Analyst offers readers an insightful narrative on the necessary ingredients for success in this demanding field.
R 1,139.00
Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
R 5,045.66
R 611.00
Learning Python with Raspberry Pi
The must-have companion guide to the Raspberry Pi User Guide! Raspberry Pi chose Python as its teaching language of choice to encourage a new generation of programmers to learn how to program. This approachable book serves as an ideal resource for anyone wanting to use Raspberry Pi to learn to program and helps you get started with the Python programming language. Aimed at first-time developers with no prior programming language assumed, this beginner book gets you up and running. Covers variables, loops, and functionsAddresses 3D graphics programmingWalks you through programming MinecraftZeroes in on Python for scripting Learning Python with Raspberry Pi proves itself to be a fantastic introduction to coding.
R 775.00
R 489.00
