Programmingr Alternativen & Konkurrenten
ProgrammingR is your one-stop source for learning the R programming language, finding R jobs, hiring R programmers or R consultants, and getting R related help.
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Statistics Globe is an education platform providing free programming tutorials - R programming & Python - Statistics & data science.
R is a language used for statistical computations, data analysis and graphical representation of data. R is a very popular language in academia.
Python tutorials: Pandas, Statsmodels, Seaborn, Matplotlib. R tutorials: descriptive statistics, importing data, and many more.
Quick-R: Home Page.
Welcome to R CODER This website provides a wide variety of R PROGRAMMING TUTORIALS and a FREE R COURSE developed by experts
Home - Datanovia.
Easily search the documentation for every version of every R package on CRAN and Bioconductor.
Tidyverse.
Datamentor: Learn to Code in R Programming and Python |.
Review our step-by-step Data Science tutorials using a variety of tools, such as Python, SQL, MS Access, MS Excel, and more!
Home - DataScience Made Simple.
R news and tutorials contributed by hundreds of R bloggers
An R introduction to statistics that explains basic R concepts and illustrates with statistics textbook homework exercises.
STHDA est dédié à l'analyse statistique des données en utilisant le langage R.
A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge.
R: The R Project for Statistical Computing.
The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.
Over 1400 graphs with reproducible code divided in 8 big categories and over 50 chart types, in addition of tools to choose and create colors and color palettes
Spark By Examples | Learn Spark Tutorial with Examples In this Apache Spark Tutorial, you will learn Spark with Scala code examples and every sample example explained here is available at Spark Examples Github Project for reference. All Spark examples provided in this Apache Spark Tutorials are basic, simple, easy to practice for beginners who
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PyData |.
Statology is a website that makes learning statistics easy by explaining concepts in simple, straightforward ways.
This cookbook contains more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly—without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project and includes a discussion of how and why the recipe works.
RStudio provides open source and enterprise-ready professional software for data science.
Bot Verification.
Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source.
Definitive Python, Pandas, Machine learning and AWS tutorials with working examples and step by step guides.
ThisPointer – Programming Tutorials.
Do you want to learn Python from scratch to advanced, check out best way to learn python and machine learning from experts.
Learn Python online: Python tutorials for developers of all skill levels, Python books and courses, Python news, code examples, articles, and more.
Learn Data Science & AI from the comfort of your browser, at your own pace with DataCamp's video tutorials & coding challenges on R, Python, Statistics & more.
Learn to Make Plots in Python and R
Matplotlib — Visualization with Python.
Ggplot2: Elegant Graphics for Data Analysis (3e).
Piles of precious data
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Greg Kamradt.
Datagy.io • datagy.
Home | Pythonic Perambulations.
Hundreds of statistics how to articles and step by step videos for elementary statistics and probability, plus AP and advanced statistics topics.
Learn Data Science with Python and R
Python Examples – Learn Python Programming - Python Examples.
Data Imaginist.
Cloud, Data and AI community. About Big Data, Data Engineering, Data Analytics, AI, Business Intelligence and Cloud platforms.
Home | Delta Lake.
SQL & Hadoop – SQL on Hadoop with Hive, Spark & PySpark on EMR & AWS Glue.
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.