Data Science

This book powers our MicroMasters program on edX and specialization on Coursera, one of the ten most popular computer science courses on Coursera. Over half a million students have tried to solve many programming challenges and algorithmic puzzles described in this book. We invite you to join them! See the webpage of the book for more details.

Everything you really need to know in Machine Learning in a hundred pages.

The book provides a modern look at introductory Biostatistical concepts and the associated computational tools using the latest developments in computation and visualization in the R language environment. The book includes practical data analysis based on datasets that can be downloaded here: https://github.com/muschellij2/biostatmethods.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

Become a better data scientist by understanding different modeling mindsets.

What You Will LearnBy the end of this book, you will be able to:1.     Understand the core concepts of unsupervised learning and how it differs from supervised learning.2.     Preprocess and prepare datasets for clustering, including scaling, normalization, and handling outliers.3.     Implement popular clustering algorithms in Python, tuning parameters for optimal results.4.     Evaluate clustering performance using both internal and external metrics.5.     Apply clustering techniques to real-world problems such as customer segmentation, anomaly detection, and image grouping.6.     Work with high-dimensional data and understand techniques to reduce dimensionality while preserving patterns.7.     Use advanced clustering techniques to solve complex data grouping problems in large datasets.8.     Develop ethical awareness of privacy, bias, and fairness in AI applications. Benefits After Studying This Book For Students ·        Gain strong theoretical foundations in machine learning without supervision.·        Prepare for academic exams, assignments, and competitive exams like UGC NET, GATE, and data science interviews.·        Build portfolio-worthy projects to showcase in internships or job applications. For Job Seekers and Professionals ·        Learn industry-relevant clustering algorithms used in AI, marketing, healthcare, and cybersecurity.·        Enhance data analysis and problem-solving skills to stand out in interviews for roles such as Data Scientist, Machine Learning Engineer, or Business Analyst.·        Understand how to integrate clustering techniques into business solutions for better decision-making. For Researchers and Innovators ·        Explore cutting-edge clustering methods and hybrid models for high-dimensional and big data scenarios.·        Gain insights into current trends and future research opportunities in unsupervised learning.·        Leverage clustering techniques for research publications, AI prototypes, and academic projects.

By diving into this book, students will:1.     Master Supervised Learning Fundamentals Gain strong conceptual understanding and practical competence.2.     Evate & Deploy Models Effectively Understand how to build, validate, interpret, and deploy high-quality models.3.     Think Critically about AI Systems Develop ethical awareness and critical reasoning regarding data bias and model behavior.

Every spreadsheet tells many stories - this book tells you how to ask the right questions.

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Printed copies of this book are available through Lulu.

Your company's answers are already written down. This book builds the machine that finds them.

Includes Editions 1, 2, 3, and 4. Editions 1-3 are listed under "Extras" after checking out, including for $0 purchases. AHSS provides a thorough intro to statistics and supports students preparing for the AP Statistics exam. B&W paperbacks (color) are sold for $25 ($40). Paperback royalties are shared between OpenIntro and the authors. Leanpub contributions go to authors to fund their time.

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. This book covers the design, acquisition, and analysis of fMRI data.

Data engineers are the unsung heroes who make analytics and data-driven applications possible.  This handbook introduces the fundamentals of what it's like to work with data, and the skills and concepts needed to develop "data products" in the real world.

Master the art of data engineering with Enterprise Big Data Engineer: Fundamental Approaches for Data Engineering and the Creation of Data Pipelines. This comprehensive guide dives deep into the essential skills and strategies needed to design, build, and manage scalable data systems in today's complex enterprise environments. Explore advanced techniques for batch and stream processing, develop secure and reliable data pipelines, and ensure your data's quality, security, and governance. As the official guide for the Enterprise Big Data Engineer (EBDE) certification by APMG International, this book is your ultimate resource for achieving professional excellence and propelling your career forward in the dynamic field of data engineering.