DataSciGuide Content Added since Sept 22

This is what I’ve added to DataSciGuide since 9/22/15:

  • Coursera Data Science Certification

    (2) This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results.

  • Data Analysis for the Life Sciences Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst.
  • Data Science at the Command Line This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.
  • DataCamp Learn R and Python for data analysis in the comfort of your browser with video lessons and coding challenges. Discover our R tutorials and data science courses, and start analyzing your own data.
  • How to Lie with Statistics Darrell Huff probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to fool rather than to inform.
  • Introduction to R Programming This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R.
  • Introduction to SQL for Data Scientists PDF document explaining basic SQL commands like joins, aggregate functions, and subqueries.
  • KDNuggets Data Mining Course This course contains modules for a complete 1-semester course in data mining and individual lectures on data mining in the context of courses on Algorithms, Artificial Intelligence, and Introduction to Computer Science.
  • Multithreaded (stitchfix technology blog) We are reinventing the retail industry through innovative technology.
  • Naked Statistics: Stripping the Dread from the Data Wheelan strips away the arcane and technical details, focuses on the underlying intuition that drives statistical analysis, and clarifies key concepts such as inference, correlation, and regression analysis.
  • Not So Standard Deviations Not So Standard Deviations: The Data Science Podcast Co-hosts: Roger Peng of the Johns Hopkins Bloomberg School of Public Health and Hilary Parker of Etsy.
  • Open Data Science Conference (West) ODSC is THE conference for you to learn all about the latest insights, trends, and discoveries in the most important data science languages, tools, and topics.
  • Probability Cheat Sheet This is an 10-page probability cheatsheet that summarizes important probability probability concepts, formulas, and distributions, with figures, examples, and stories.
  • Python Machine Learning This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python.
  • scikit-learn algorithm cheat sheet This flowchart is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
  • Statistics in a Nutshell Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject.
  • The Information James Gleick now brings us a revelatory chronicle and meditation that shows how information has become the modern era’s defining quality—the blood, the fuel, the vital principle of our world.
  • The Signal and the Noise Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty.
  • What Every Data Scientist Needs to Know about SQL In this series of posts, I will provide a broad overview of the key SQL topics required to successfully work with databases to do effective data science.
  • ŷhat blog machine learning, data science, engineering

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