Search results for "Data Science for Beginners"
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Data Science For Dummies
Understand the foundations of data science and modern AI tools with this fully updated beginner's guide Whether you're a business analyst who's never coded, a manager who’s making data-driven decisions, or a career changer eyeing one of the fastest-growing professions, data science skills are vital to success. In the fourth edition of Data Science For Dummies, Lillian Pierson, a fractional CMO and GTM engineer who has trained over two million learners, delivers a hands-on introduction to the key concepts and tools such as AI-enabled shortcuts that make advanced analytics accessible to everyone. This updated edition covers generative AI, prompt engineering, and predictive analytics right alongside evergreen fundamentals like Python programming, statistics, and data wrangling. Brand-new chapters explain practical applications using ChatGPT and Claude, while expanded no-code workflows make AI-powered analysis accessible to readers who have never written a single line of code. Data Science For Dummies walks you through: Foundational skills in Python, statistics, and machine learning for beginners and career changersHands-on generative AI coverage with practical demos using ChatGPT and ClaudeNo-code workflows that show non-programmers how to perform AI-powered data analysis tasksUpdated guidance on ethical AI practices, data regulations, and responsible complianceCareer-focused advice on emerging roles, portfolio building, and industry preparation Perfect for professionals, students, career changers, and business leaders looking for an accessible launching pad into an exciting and rewarding discipline, Data Science For Dummies gives you the foundational skills and modern AI tools you need to extract insights, create visualizations, and make smarter, data-driven decisions.
R 800.00
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R 336.00
R 1,289.24
R 611.00
R 742.47
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
Dive Into Data Science
This beginner's book will teach you how to apply the principles of data science to improve your business strategies - no math proficiency required! Easy-to-follow chapters take the reader through concepts like A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples. The tone is conversational, and the author avoids the dense mathematical theories associated with data science in favour of simple explanations and practical applications. By the end of the book, readers should be comfortable working with data, applying data to business problems, and using best practices to analyse data using Python.
R 1,175.00
R 635.00
Beginning Data Science with R
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library.The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
R 3,577.12
Data Science
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
R 6,960.00
R 1,146.00
Data Science Programming All-in-One For Dummies
Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionalsWhat lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data storySee clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!
R 1,026.00
R 1,173.29
Beginning Data Science with R
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library.The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
R 4,727.83
R 591.00
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 Fundamentals for Python and MongoDB
Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained.Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll LearnPrepare for a career in data scienceWork with complex data structures in PythonSimulate with Monte Carlo and Stochastic algorithmsApply linear algebra using vectors and matricesUtilize complex algorithms such as gradient descent and principal component analysisWrangle, cleanse, visualize, and problem solve with dataUse MongoDB and JSON to work with dataWho This Book Is ForThe novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.
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Essential Statistics for Data Science
Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks. Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that intoaccount when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.
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R 616.00
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R 947.00
R 480.59
R 342.00
Data Science and Machine Learning for Non-Programmers
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
R 2,820.55
R 616.00
R 397.00
Data Science and Machine Learning for Non-Programmers
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
R 5,754.93
R 415.00
R 782.31
R 579.00
R 684.00
Data Science for Sensory and Consumer Scientists
Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences.Key Features:• Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science.• Examples based in industrial practice used throughout the book
R 5,165.00
R 306.00
