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Category: "Data Science"

Books

  1. Apache Spark & Geodata
    Learn to use Apache Spark (with PySpark) for Big Data GeoAnalytics of OpenStreetMap and other Geospatial Data
    Shoaib Burq, Dr. Kashif Rasul, and Zaeem Burq

    Learn to use Apache Spark for Analyzing OpenStreetMap and other Geospatial Data

  2. Correlation Is Not Causation
    Learn How to Avoid the 5 Traps That Even Pros Fall Into
    Lee Baker

    Correlation Is Not Causation explains how to test for the five most common correlation-causation pitfalls that even the pros fall into.It is packed with visually intuitive examples and is perfect for beginners! Discover the world of correlation and causation. Get this book, TODAY! Spoiler Alert: There's also a FREE book for you to claim inside!

  3. Conversations On Data Science
    Roger D. Peng and Hilary Parker

    Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn't work) in the real world.

  4. A visual, hands-on guide to Microsoft's Power BI Desktop. In no time you will be up and running, creating powerful visualisations from data gathered everywhere.

  5. Ya lo dijo Viviane Reding, comisaria europea de justicia, en julio de 2011: Europa no puede permitir que tres empresas privadas estadounidenses la destrocen. Desde 2010, términos como prima de riesgo, rating, solvencia, bonos a 10 años, etc. se han convertido en un miembro más de nuestra cotidianidad. Sin embargo, para el público general estos términos, aunque usados de forma habitual, son profanos

  6. Get a hands-on introduction to machine learning with genetic algorithms using Python. Step-by-step tutorials build your skills from Hello World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to problems in your own field of expertise.

  7. Want a quick start way of generating those reports you've been asked for about the performance of your FutureLearn course? Or maybe fancy the idea of pulling together a simple dashboard to review course progress? This recipe book should get you started...

  8. Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data science tasks on medium- to large-scale data sets, using Jupyter as the master controller.

  9. The textbook for the data science intensive specialization at UCLA extension. **FREE** until completed.

  10. Use numpy to explore the quantum realm. 

  11. Bioconductor
    An Introduction to Core Technologies
    Kasper D. Hansen

    The Bioconductor project is a widely used open source and open development platform for software for computational biology. It is a leading platform for doing data science in Genomics.  This book covers the core functionality needed to deploy Bioconductor on modern datasets, and will lay the foundation for you to learn and explore parts of the project devoted to specific application domains.

  12. Beginner’s Guide to Correlation Analysis
    Learn The One Reason Your Correlation Results Are Probably Wrong
    Lee Baker

    Your correlation results are probably wrong. Sorry, but they are.You see, there is one really important thing to know about your correlations that mean that whatever results you get you can’t be sure they are correct. Beginner’s Guide to Correlation Analysis fixes that. Discover the world of correlation analysis. Get this book, TODAY!

  13. A book about how to be a scientist the modern, open-source way.

  14. A Data Engineer's Manual
    Joseph W. Clark, Ph.D.

    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.

  15. The Data Science Salon
    A Collaborative Learning Experience
    Roger D. Peng, Elizabeth Matsui, and Corinne Keet

    This book, along with the materials it provides access to, offers a guided path to learning the art of data science. Engage in weekly activities and learn how to ask a good question, explore datasets, and to use models to develop solid statistical evidence. In addition to the materials in the book, you will get access to lecture videos, receive emails with bonus material, and have access to videos of the authors discussing their views on each session’s activities.