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Data Wrangling with Python
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that's initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you'll learn how to acquire, clean, analyze, and present data efficiently. You'll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process
R 1,730.00
Python Data Science Handbook
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you'll learn how:IPython and Jupyter provide computational environments for scientists using PythonNumPy includes the ndarray for efficient storage and manipulation of dense data arraysPandas contains the DataFrame for efficient storage and manipulation of labeled/columnar dataMatplotlib includes capabilities for a flexible range of data visualizationsScikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
R 1,978.00
Data Management and Analytics for Medicine and Healthcare
This book constitutes the thoroughly refereed conference proceedings of the Third International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017, in Munich, Germany, in September 2017, held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017. The 9 revised full papers presented together with 2 keynote abstracts were carefully reviewed and selected from 16 initial submissions. The papers are organized in topical sections on data privacy and trustability for electronic health records; biomedical data management and Integration; online mining of Health related data; and clinical data analytics.
R 2,152.44
Health Informatics Data Analysis
This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection.With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.
R 6,548.86
R 1,099.00
Exploratory Data Analytics for Healthcare
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.
R 2,960.36
Python for Data Science For Dummies
Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.
R 835.00
Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
R 1,335.00
Python for R Users
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
R 2,441.00
Statistics and Calculus with Python Workshop
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easyKey FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook DescriptionAre you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.
R 1,335.00
R 1,099.00
Interactive Dashboards and Data Apps with Plotly and Dash
Build web-based, mobile-friendly analytic apps and interactive dashboards with PythonKey FeaturesDevelop data apps and dashboards without any knowledge of JavaScriptMap different types of data such as integers, floats, and dates to bar charts, scatter plots, and moreCreate controls and visual elements with multiple inputs and outputs and add functionality to the app as per your requirementsBook DescriptionPlotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you're getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways.Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it.Next, you'll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them.By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.What you will learnFind out how to run a fully interactive and easy-to-use appConvert your charts to various formats including images and HTML filesUse Plotly Express and the grammar of graphics for easily mapping data to various visual attributesCreate different chart types, such as bar charts, scatter plots, histograms, maps, and moreExpand your app by creating dynamic pages that generate content based on URLsImplement new callbacks to manage charts based on URLs and vice versaWho this book is forThis Plotly Dash book is for data professionals and data analysts who want to gain a better understanding of their data with the help of different visualizations and dashboards – and without having to use JS. Basic knowledge of the Python programming language and HTML will help you to grasp the concepts covered in this book more effectively, but it's not a prerequisite.
R 1,791.00
Text Analytics
Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.Features: Easy-to-follow step-by-step concepts and methods Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves Practical programming exercises in Python for each chapter Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797
R 8,125.41
Essentials of Statistics with SPSS
This workbook is designed to provide a good grounding of the most commonly used analytic approaches in the health sciences, using the Statistical Package for the Social Sciences (SPSS). Topics covered include creation of a database, aggregation of data, data transformations, scale development, t-tests, cross tabulations, ANOVA, logistic regression, correlation and regression. Notes and slides are included in each Chapter that describe the essentials of each analytic approach. As well, data from the Canadian Community Health Survey are analyzed to illustrate the application of each approach. SPSS output is included, with the most important elements for a research paper highlighted and interpreted.
R 992.89
Data Analytics and Big Data
The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.
R 5,795.00
Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 3,079.26
AI-Assisted Statistics for Data Scientists
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows.This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects.Conduct exploratory analysis of data to improve quality and model outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data
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R 891.43
Hands-on Introduction to Big Data Analytics
This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional.It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark.Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning.Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton.Dr Ofer Engel is a Data Scientist at the University of Groningen.
R 5,797.00
Mastering Predictive Analytics with scikit-learn and TensorFlow
Learn advanced techniques to improve the performance and quality of your predictive modelsKey FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
R 1,140.00
R 662.27
R 736.36
Statistics for Exercise Science and Health with Microsoft Office Excel
This book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis. Statistics for Exercise Science and Health with Microsoft Office Excel: Aids readers in analyzing their own data using the presented statistical techniques combined with ExcelFeatures comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports scienceUtilizes Excel to enhance reader competency in data analysis and experimental designsIncludes coverage of both binomial and poison distributions with applications in exercise science and healthProvides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniquesContains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems
R 4,854.00
Delivering Superior Health and Wellness Management with IoT and Analytics
This in-depth book addresses a key void in the literature surrounding the Internet of Things (IoT) and health. By systematically evaluating the benefits of mobile, wireless, and sensor-based IoT technologies when used in health and wellness contexts, the book sheds light on the next frontier for healthcare delivery. These technologies generate data with significant potential to enable superior care delivery, self-empowerment, and wellness management. Collecting valuable insights and recommendations in one accessible volume, chapter authors identify key areas in health and wellness where IoT can be used, highlighting the benefits, barriers, and facilitators of these technologies as well as suggesting areas for improvement in current policy and regulations.Four overarching themes provide a suitable setting to examine the critical insights presented in the 31 chapters:Mobile- and sensor-based solutionsOpportunities to incorporate critical aspects of analytics to provide superior insights and thus support better decision-makingCritical issues around aspects of IoT in healthcare contextsApplications of portals in healthcare contextsA comprehensive overview that introduces the critical issues regarding the role of IoT technologies for health, Delivering Superior Health and Wellness Management with IoT and Analytics paves the way for scholars, practitioners, students, and other stakeholders to understand how to substantially improve health and wellness management on a global scale.
R 6,228.91
Predictive and Simulation Analytics
This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
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R 519.00
R 1,160.00
AI and Data Engineering for Healthcare
This book examines the transformative role of artificial intelligence (AI) and data engineering in revolutionizing the healthcare landscape. It presents cutting-edge developments ranging from predictive algorithms for disease diagnosis to large-scale data systems that enhance patient outcomes. By emphasizing the synergy between AI and data engineering, the book showcases practical applications in medical imaging, clinical diagnostics, and personalized treatment strategies.It also thoughtfully examines ethical considerations, data privacy, and healthcare equity, particularly in underserved and rural populations.Key Features:Explores state-of-the-art technologies in healthcare, including image segmentation, feature extraction, feature selection, and classification.Provides real-world case studies, practical examples, and hands-on exercises for effective implementation of AI-driven solutions.Bridges disciplines across computer science, data engineering, and biomedical sciences to foster cross-domain collaboration.Highlights innovative research methodologies and their applications in AI-powered healthcare systems.Discusses the role of AI in improving healthcare access, delivery, and outcomes across diverse populations.This book is ideal for professionals, researchers, and policymakers seeking to understand and shape the future of healthcare through the lens of AI and data-driven innovation.
R 9,339.65
Healthcare Big Data Analytics
THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSISBy focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
R 9,281.00
Decision Analytics
Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problemsMonte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulationsPython code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presentedPractice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teachingA concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.
R 4,559.00
Financial Data Science with Python
In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.
R 1,043.69
Big Data Analytics
A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clustersAbout This Book• This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools.• Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR.• Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall.Who This Book Is ForThough this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory.What You Will Learn• Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop• Understand all the Hadoop and Spark ecosystem components• Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx• See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming• Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall.In DetailBig Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters.It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark.Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.Style and approachThis step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-l
R 1,889.00
R 855.35
Practical Python Data Visualization
Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations.You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets.The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface.What You'll LearnReview practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with PandasWho This Book Is ForData Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.
R 1,482.00
Learn Data Science Using Python
Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization.You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding.Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression.What You’ll LearnUnderstand installation procedures and valuable insights into Python, data types, typecastingExamine the fundamental statistical analysis required in most data science and analytics reportsClean the most common data set problemsUse linear progression for data predictionWho This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science.
R 2,156.71
R 4,396.00
R 519.00
Beginning Python Visualization
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts, Second Edition discusses turning many types of data sources, big and small, into useful visual data. And, you will learn Python as part of the bargain.In this second edition you’ll learn about Spyder, which is a Python IDE with MATLAB® -like features. Here and throughout the book, you’ll get detailed exposure to the growing IPython project for interactive visualization. In addition, you'll learn about the changes in NumPy and Scipy that have occurred since the first edition. Along the way, you'll get many pointers and a few visual examples. As part of this update, you’ll learn about matplotlib in detail; this includes creating 3D graphs and using the basemap package that allows you to render geographical maps. Finally, you'll learn about image processing, annotating, and filtering, as well as how to make movies using Python. This includes learning how to edit/open video files and how to create your own movie, all with Python scripts. Today's big data and computational scientists, financial analysts/engineers and web developers – like you - will find this updated book very relevant.
R 1,596.00
Healthcare Analytics and Advanced Computational Intelligence
This book aims to apply state-of-the-art advanced computational intelligence frameworks in healthcare. It presents recent and real-life applications of computationally intelligent healthcare. It also discusses problems and solutions to remote healthcare and emergency healthcare services. Healthcare Analytics and Advanced Computational Intelligence highlights modern ambient intelligence-enabled healthcare models along with advanced topics like quantum computing in healthcare and cryptomedical systems.Healthcare Analytics and Advanced Computational Intelligence examines designing the latest medical systems and models that will allow the societal acceptance of ambiance computing in healthcare, medical imaging, health analytics, machine intelligence, sensory computing, medical data analytics, disease detection, telemedicine, and their applications. It includes diverse case studies dealing with various clinical-based applications. These intelligent models are primarily structured to deal with complex real-world issues in clinical data analytics, by means of state-of-the-art techniques with general implementation, domain-specific solutions, or hybrid methods which integrate computational intelligence with conventional statistical methods.The book is written for researchers and academicians in diverse areas. Engineers from technical disciplines such as computer engineering are likely to purchase the book. Various sub-streams such as machine learning, big data analytics, healthcare analytics, and computational intelligence will find the book significant for their curriculum.
R 2,711.00
Data Analytics for Pandemics
Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.
R 3,595.01
Data Analysis in the AI Era
Data Analysis in the AI Era covers the process of exploring and analyzing data from various sources for the purpose of extracting information, building models, and making decisions and inferences. The AI and data analysis field is a scientific area that has grown enormously in recent decades, following a rapidly growing computer industry and the wide applicability of computational techniques in conjunction with new advances in analytic tools. Modeling enables data analysts to apply various statistical methods to the data they are investigating, identify relationships between variables, make predictions about future sets of data, and understand, interpret and visualize the extracted information more strategically. New research results have been developed and published, with many more in progress at the present time. This book includes applications to health, demography, markets and climate, along with new mathematical and statistical techniques.
R 5,536.00
Basic Python for Data Management, Finance, and Marketing
Learn how to gather, manipulate, and analyze data with Python. This book is a practical guide to help you get started with Python from ground zero and to the point where you can use coding for everyday tasks. Python, the most in-demand skill by employers, can be learned in a matter of months and a working knowledge will help you to advance your career. This book will teach you to crunch numbers, analyze big-data, and switch from spreadsheets to a faster and more efficient programming language. You'll benefit from the numerous real-life examples designed to meet current world challenges and from step-by-step guidance to become a confident Python user. Python is used in all aspects of financial industry, from algo trading, reporting and risk management to building valuations models and predictive machine learning programs. Basic Python for Data Management, Finance, and Marketing highlights how this language has become a useful skill with digital marketers, allowing them to analyze data more precisely and run more successful campaigns.What You'll LearnGet started with Python from square oneExtend what's possible on excel with PythonAutomate tasks with PythonAnalyze data more preciselyWho This Book Is ForProfessionals who want to find a job in the modern world or advance their careers within field of Python programming language.
R 1,482.00
Python for Bioinformatics
In today's data driven biology, programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience, Python for Bioinformatics, Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language.This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics, but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases, the Anaconda Python distribution, graphical libraries like Bokeh, and the use of Github for collaborative development.
R 4,524.65
R 579.00
R 967.00
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Biostatistics and Computer-based Analysis of Health Data using Stata
This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research. The use of Stata for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epideomological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential Stata packages and commands.
R 3,825.00
