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R 1,221.00
Data Science for the Geosciences
Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
R 5,809.77
Modern Time Series Forecasting with Python
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesApply ML and global models to improve forecasting accuracy through practical examplesEnhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATSLearn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressionsBook DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What you will learnBuild machine learning models for regression-based time series forecastingApply powerful feature engineering techniques to enhance prediction accuracyTackle common challenges like non-stationarity and seasonalityCombine multiple forecasts using ensembling and stacking for superior resultsExplore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time seriesEvaluate and validate your forecasts using best practices and statistical metricsWho this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.
R 1,889.00
Data Science for Marketing Analytics
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming languageKey FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook DescriptionUnleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is forThis marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path.Basic prior knowledge of Python and experience working with data will help you access this book more easily.
R 1,335.00
Think Stats
If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis-from wrangling data and generating statistics to identifying patterns and testing hypotheses.Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.Analyze data distributions and visualize patterns using Python librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks
R 1,824.00
R 580.00
Mathematical Methods in Data Science
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
R 5,801.00
Data Science with Java
Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications.Examine methods for obtaining, cleaning, and arranging data into its purest formUnderstand the matrix structure that your data should takeLearn basic concepts for testing the origin and validity of dataTransform your data into stable and usable numerical valuesUnderstand supervised and unsupervised learning algorithms, and methods for evaluating their successGet up and running with MapReduce, using customized components suitable for data science algorithms
R 2,076.00
R 1,527.00
Machine Learning on Geographical Data Using Python
Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application.What You Will LearnUnderstand the fundamental concepts of working with geodataWork with multiple geographical data types and file formats in PythonCreate maps in PythonApply machine learning on geographical data Who This Book Is ForReaders with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment
R 1,254.00
R 305.00
Just Enough Data Science and Machine Learning
An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.
R 1,628.00
R 544.90
Data Science for Marketing Analytics
Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise resultsKey FeaturesStudy new techniques for marketing analyticsExplore uses of machine learning to power your marketing analysesWork through each stage of data analytics with the help of multiple examples and exercisesBook DescriptionData Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.What you will learnAnalyze and visualize data in Python using pandas and MatplotlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated data Predict customer lifetime value using linear regressionUse classification algorithms to understand customer choiceOptimize classification algorithms to extract maximal informationWho this book is forData Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
R 1,498.00
R 1,534.76
Python Polars: The Definitive Guide
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition. With this definitive guide at your side, you'll be able to: Process larger-than-memory datasets at record speed Apply the eager, lazy, and streaming APIs of Polars and decide when to use them Transition smoothly from pandas or Spark to Polars Integrate Polars into your existing code base Work with Arrow and Parquet to efficiently read and write data Translate complex ETL tasks into efficient and elegant queries
R 1,824.00
Multivariate Statistics and Machine Learning
Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.
R 9,434.30
R 1,340.19
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
R 2,711.00
Practical Web Scraping for Data Science
This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set.Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. What You'll LearnLeverage well-established best practices and commonly-used Python packagesHandle today's web, including JavaScript, cookies, and common web scraping mitigation techniquesUnderstand the managerial and legal concerns regarding web scrapingWho This Book is ForA data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well.
R 1,986.30
Python for AI, Machine Learning, and Deep Learning
Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.Key FeaturesBuild Python, data science, mathematics, and statistics foundations for machine learningTrain, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost modelsCreate CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorchBook DescriptionLearning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.What you will learnUse Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysisApply linear algebra, calculus, probability, and statistics to MLBuild regression, classification, and k-NN modelsEngineer features and evaluate models with cross-validation and hyperparameter tuningTrain ensembles with random forests, XGBoost, LightGBM, and CatBoostBuild neural networks and CNNs with TensorFlow, Keras, and PyTorchDevelop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLPApply transfer learning and fine-tuning to computer vision and NLPWho this book is forThis book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help.
R 1,628.00
Data Science
Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...
R 2,059.00
R 786.41
R 808.00
Learning Scientific Programming with Python
Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming.
R 4,780.90
Data Science with SQL Server Quick Start Guide
Get unique insights from your data by combining the power of SQL Server, R and PythonKey FeaturesUse the features of SQL Server 2017 to implement the data science project life cycleLeverage the power of R and Python to design and develop efficient data modelsfind unique insights from your data with powerful techniques for data preprocessing and analysisBook DescriptionSQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you.This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment.You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm.What you will learnUse the popular programming languages,T-SQL, R, and Python, for data scienceUnderstand your data with queries and introductory statisticsCreate and enhance the datasets for MLVisualize and analyze data using basic and advanced graphsExplore ML using unsupervised and supervised modelsDeploy models in SQL Server and perform predictionsWho this book is forSQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.
R 1,270.00
Data Science
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
R 9,361.53
Beginner's Guide to Data Science
This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered.Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification.This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics:Data gathering and acquisition techniques including data creationManaging, transforming, and organizing data to ultimately package the information into an accessible format ready for analysisFundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurementsInferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recordedMetrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observationsRecommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some mannerMachine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analyticsNatural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate mannerTime series analysis, techniques to examine and generate forecasts about the progress and evolution of data over timeData science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.
R 2,381.00
R 678.82
Python for the Life Sciences
Treat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you.What You'll Learn Write Python scripts to automate your lab calculationsSearch for important motifs in genome sequencesUse object-oriented programming with PythonStudy mining interaction network data for patternsReview dynamic modeling of biochemical switchesWho This Book Is ForLife scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers.“A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow“Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School“Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley
R 1,254.00
R 1,527.00
R 1,173.29
Learn Python Programming
Get up and running with Python 3.9 through concise tutorials and practical projects in this fully updated third edition.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesExtensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python codeDiscover how to think like a Python programmerLearn the fundamentals of Python through real-world projects in API development, GUI programming, and data scienceBook DescriptionLearn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.What you will learnGet Python up and running on Windows, Mac, and LinuxWrite elegant, reusable, and efficient code in any situationAvoid common pitfalls like duplication, complicated design, and over-engineeringUnderstand when to use the functional or object-oriented approach to programmingBuild a simple API with FastAPI and program GUI applications with TkinterGet an initial overview of more complex topics such as data persistence and cryptographyFetch, clean, and manipulate data, making efficient use of Python’s built-in data structuresWho this book is forThis book is for everyone who wants to learn Python from scratch, as well as experienced programmers looking for a reference book. Prior knowledge of basic programming concepts will help you follow along, but it’s not a prerequisite.
R 1,596.00
R 684.00
Essential Data Analytics, Data Science, and AI
In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.What you will learn:What are Synthetic data and Telemetry dataHow to analyze data using programming languages like Python and Tableau.What is feature engineeringWhat are the practical Implications of Artificial IntelligenceWho this book is for:Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.
R 1,254.00
Python Data Analysis Cookbook
Over 140 practical recipes to help you make sense of your data with ease and build production-ready data appsAbout This Book• Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types• Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning• Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed booksWho This Book Is ForThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.What You Will Learn• Set up reproducible data analysis• Clean and transform data• Apply advanced statistical analysis• Create attractive data visualizations• Web scrape and work with databases, Hadoop, and Spark• Analyze images and time series data• Mine text and analyze social networks• Use machine learning and evaluate the results• Take advantage of parallelism and concurrencyIn DetailData analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.Style and ApproachThe book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.
R 1,889.00
Machine Learning With Python: Theory And Applications
Machine Learning (ML) has become a very important area of research widely used in various industries.This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks.
R 2,858.00
Practical Linear Algebra for Data Science
If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications.This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:The interpretations and applications of vectors and matricesMatrix arithmetic (various multiplications and transformations)Independence, rank, and inversesImportant decompositions used in applied linear algebra (including LU and QR)Eigendecomposition and singular value decompositionApplications including least-squares model fitting and principal components analysis
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Machine Learning and Data Science Blueprints for Finance
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.This book covers:Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio managementSupervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategiesDimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve constructionAlgorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio managementReinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio managementNLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
R 1,824.00
