An Overview Of Popular R Libraries for Data Science. When we talk about the top programming language for Data Science, we often find Python to be the best fit for the topic. Sure, Python is undoubtedly an excellent choice for a vast majority of Data Science-centric tasks, but there’s another programming language that was built specifically to provide superior number-crunching capabilities for Data Science, and that is R.
When we talk about the top programming language for Data Science, we often find Python to be the best fit for the topic. Sure, Python is undoubtedly an excellent choice for a vast majority of Data Science-centric tasks, but there’s another programming language that was built specifically to provide superior number-crunching capabilities for Data Science, and that is R.
In addition to providing robust statistical computing, R offers a huge collection, over 16 thousand *to be exact, of *highly resourceful libraries, catering to the needs of Data Scientists, Data Miners, and Statisticians alike. Further, in this article, we will shed some light on a handful of top R libraries for Data Science.
R is extremely popular among Data Miners and Statisticians, and part of the reason is the extensive range of libraries that comes with R. These tools and functions can simplify statistical tasks to a great extent, making tasks such as** data manipulation, visualization, web crawling, Machine Learning** and more, a breeze. Some of the libraries have been briefly explained below:
The dplyr package, also known as the grammar of data manipulation, essentially provides frequently used tools and functions for data manipulation, that includes the following functions:
tidyr is one of the core packages in the Tidyverse** ecosystem, and as the name suggests, it is **used to tidy up messy data. Now, if you’re wondering what tidy data is, let me clear it for you. A tidy data indicates that every column is variable, each row is an observation, and each cell is a singular value.
According to tidyr, tidy data is a way of storing the data that is to be used throughout the tidyverse and can help you save time and be more productive with your analysis. You can get the package from tidyverse or by the following command “install.packages(“tidyr”)”.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
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