DataSciGuide Content To Date

I think I’m going to start posting weekly what content has been added to the site. That way, you can subscribe to the RSS feed for blog posts (instead of the RSS feed for the custom post type of ‘Content’) to just get these updates and summaries instead of being bombarded every time I sit down to enter content (or, other people enter content once that’s enabled).

Here’s the RSS feed for this update blog:

And if you really want it, here’s the RSS feed with every piece of content I add:

To date, this is what I’ve added to DataSciGuide:

  • Advanced R No ratings yet. The book is designed primarily for R users who want to improve their programming skills and understanding of the language.
  • An Introduction to Statistical Learning: with Applications in R Provides an accessible overview of the field of statistical learning… This book presents some of the most important modeling and prediction techniques, along with relevant applications.
  • Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Applied Longitudinal Data Analysis is a professional book for empirical researchers and graduate students, and offers the first accessible in-depth presentation multilevel models for individual change and hazard/survival models for event occurrence.
  • Applied Predictive Modeling Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning…plus regression and classification techniques.
  • Becoming a Data Scientist A blog documenting Renee’s path from “SQL Data Analyst pursuing an Engineering Master’s Degree” to “Data Scientist”
  • Codecademy – Python Learn to program in Python via interactive guided coding tasks in your browser.
  • Data Science from Scratch: First Principles with Python In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
  • Data Science Glossary Terms included in this glossary are the kind that typically come up in data science discussions and job postings. Most are from the worlds of statistics, machine learning, and software development.
  • Data Skeptic Data Skeptic is a podcast explaining concepts from data science with interviews featuring practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism.
  • Data Stories A podcast on data visualization with Enrico Bertini and Moritz Stefaner
  • Data Visualization for Data Analysts This course challenges you to go beyond the data, beyond the software, and start thinking more clearly and strategically about the foundations of great communication design.
  • DataKind NYC Calling all data do-gooders: come rub elbows and throw high fives with DataKind’s community of top data scientists and social sector leaders working to tackle the world’s toughest problems with data science.
  • DataQuest – Learning Python Python is an easy-to-use language that is commonly used in data science work. Learn beginner and intermediate python, including syntax, loops, functions, classes, and more.
  • Doing Data Science: Straight Talk from the Frontline How can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.
  • Flowing Data FlowingData explores how statisticians, designers, data scientists, and others use analysis, visualization, and exploration to understand data and ourselves.
  • Hadoop: The Definitive Guide With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop.
  • Harvard CS109 Data Science This course introduces data wrangling, cleaning, and sampling; data management; exploratory data analysis; prediction based on statistical methods; and communication of results through visualization, stories, and interpretable summaries.
  • Learn Python the Hard Way Learn Python The Hard Way is the most successful beginner programming book on the market.
  • Linear Algebra – Foundations to Frontiers In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you’ll also get more!
  • Linear Digressions We’re producing this podcast because machine learning is exciting!
  • Machine Learning (Coursera) In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
  • Machine Learning: The Art and Science of Algorithms that Make Sense of Data With hundreds of worked examples and explanatory figures, the book explains the principles behind state-of-the-art methods for making sense of data in an intuitive yet precise manner.
  • Mathematics for the Nonmathematician In this erudite, entertaining text, Morris Kline, Professor Emeritus of Mathematics at New York University, provides the student with a detailed treatment of mathematics in a cultural and historical context.
  • Mining the Social Web With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
  • Now You See It: Simple Visualization Techniques for Quantitative Analysis Now You See It teaches simple, practical means to explore and analyze quantitative data–techniques that rely primarily on using your eyes.
  • O’Reilly Data Show The O’Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.
  • Partially Derivative Partially Derivative is a podcast about the data of everything.
  • Perceptual Edge Stephen Few’s thoughts about Visual Business Intelligence
  • Predictive Analytics 2 – Neural Nets and Regression In this online course, you will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
  • Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules In this online course, you will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering.
  • Predictive Analytics I – Machine Learning Tools In this online course, you will be introduced to the basic concepts in predictive analytics…this course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction.
  • Programming Collective Intelligence: Building Smart Web 2.0 Applications This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet.
  • Python for Data Science for Dummies Cheat Sheet Python is an incredible programming language that you can use to perform data science tasks with minimal effort. This cheat sheet helps you access the most commonly needed reminders for making your programming experience fast and easy.
  • R Programming for Data Science 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.
  • R Talk We discuss R packages, techniques, code, useR conferences, and interview folks in the #rstats community.
  • Show Me the Numbers: Designing Tables and Graphs to Enlighten Show Me the Numbers: Designing Tables and Graphs to Enlighten is the most accessible, practical, and comprehensive guide to table and graph design available.
  • Statistics (The Easier Way) with R An informal text on applied statistics. Want to learn R, statistics, or data analysis? Become successful fast with this unique book, which takes the approach of solving all problems analytically (using equations) before solving them using the R statistical software.
  • Strata Data Conference Strata + Hadoop World is where cutting-edge science and new business fundamentals intersect—and merge. It’s a deep-immersion event where data scientists, analysts, and executives get up to speed…
  • Talking Machines Human Conversation About Machine Learning
  • The Art of Data Science This book describes, simply and in general terms, the process of analyzing data.
  • The Data Incubator Bootcamp/Fellowship The Data Incubator is an intensive 7 week fellowship that prepares the best scientists and engineers with advanced degrees to work as data scientists and quants.
  • The Data Science Handbook The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice.
  • The Data Scientist’s Toolbox Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialization.
  • The Functional Art: An introduction to information graphics and visualization In this practical introduction to understanding and using information graphics, you’ll learn how to use data visualizations as tools to see beyond lists of numbers and variables and achieve new insights into the complex world around us.
  • Udacity Data Analyst Nanodegree The Data Analyst Nanodegree is specifically designed to prepare you for a career in data science.
  • University of Virginia – Master of Science in Data Science The Master of Science in Data Science (MSDS) is an 11‐month professional masters program, designed to meet the increasingly data‐intensive needs of industry and government.
  • Up and Running with Public Data Sets Up and Running with Public Data Sets shows how to find free, public sources of data on a variety of business, education, and health issues and download the data for your own analysis.
  • Women Data Scientists DC Meetup Group Made for women in data science, women working toward a data science career, and supporters of women in data science. We will have data science speakers, networking events, mentoring opportunities, and workshops to learn new data science tools.

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