Resources: Programming & Data Science in R

This post is an annotated bibliography of helpful resources for programming and doing Data Science in R. I will try to expand this list and add some short recommendations in the future.

The last update of this post was done: 2017-07-21.

Books (on-line & printed)

  1. Hadley Wickham, Advanced R
    The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side. [See more…]
  2. Hadley Wickham, R packages
    […] You’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t perfect as long as the next version is better. [See more…]
  3. R Core Team, Writing R Extensions
    Official R Package documentation. Mandatory reading for R Package developers but not very user-friendly. [See more…]
    [R package development]
  4. Marek Gągolewski, Programowanie w języku R. Analiza danych. Obliczenia. Symulacje
    Formalne, ustrukturyzowane podejście do języka. Raczej dla osób powyżej początkujących. [See more…]
  5. Andrea Spanò, Ramarro. R for Developers
    For more advanced R programmers. Included topics about OOP, debugging and profiling, parallel computation, and C++ integration via Rcpp. [See more…]
  6. Dan Navarro, Learning Statistics with R
    Over 560 pages of very good introductory materials into statistics and R. Strongly recommended for beginners in statistics. [See more…]

Video Courses

  1. Jared P. Lander, R Programming LiveLessons. Fundamentals to Advanced
    16+ Hours of Video Instruction. R Programming: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization. data munging, regression, classification, clustering, modern machine learning and more. [See more…]

Massive Open Online Courses (MOOCs)

  1. DataCamp.com
    Interactive programming and Data Science courses in R and Python. Some of them are free of charge, some of them are premium courses. High quality materials, lots of interactive excercises, well-known data scientists as authors of the courses. Highly recommended. [See more…]

Formal Education

  1. Master of Computer Science in Data Science (MCS-DS)
    With tuition under $20K, the MCS-DS is one of the most affordable gateways to one of the most lucrative and fastest growing careers of the new millennium. The MCS-DS builds expertise in four core areas of computer science: data visualization, machine learning, data mining and cloud computing, in addition to building valuable skill sets in statistics and information science with courses taught in collaboration with the University’s Statistics Department and iSchool (ranked #1 among Library and Information Studies Schools.) [See more…]

  2. Studia podyplomowe: Metody statystyczne w biznesie. Warsztaty z oprogramowaniem SAS
    Adresatami studiów podyplomowych „Metody statystyczne w biznesie” są wszystkie osoby pracujące na co dzień z danymi, zajmujący się ich analizą, przetwarzaniem, jak również będące odbiorcami takich analiz. Oferta skierowana jest do analityków, specjalistów ds. badań, jak również kadry menedżerskiej. Wszystkie zajęcia mają charakter warsztatów z wykorzystaniem oprogramowania SAS [niestety nie R, ale również warte polecenia] i odbywają się w pracowni komputerowej. [See more…]
  3. Studia podyplomowe: Inżynieria danych – Big Data
    Adresatami studiów są zatem osoby pragnące pogłębić i poszerzyć swoją wiedzę teoretyczną oraz praktyczne umiejętności jej wykorzystania w obszarze nowoczesnych informatycznych metod i narzędzi analityki Big Data, ze szczególnym uwzględnieniem zastosowań biznesowych. [See more…]