English | 简体中文 | 繁體中文 | Русский язык | Français | Español | Português | Deutsch | 日本語 | 한국어 | Italiano | بالعربية
When we handle datasets, we apply different statistical functions to the dataset. These functions can be used for a wide range of explorations, including descriptive statistics, statistical tests, and plotting features. Data science is actually the development of algorithms, data inference, and multidisciplinary exploration of technologies, specifically designed to solve complex analytical problems. The core of data science is data.
In Python, Pandas is one of the data analysis libraries used to import data from Excel spreadsheets, CSV, and other data sources.
R is an open-source language. The language is very popular because it helps to develop more user-friendly environments and provides better ways to perform data analysis, statistics, and graphical models. When it was developed, the language was only used in academic and research fields. However, now it is also used in the business world. Now, R is one of the fastest-growing statistical languages in the business community.
R comes from a vast community. The community provides support through mailing lists, user-provided documents, and a very active Stack Overflow group. CRAN is a vast repository of curated R packages, where users can easily contribute. It is a collection of R functions and data. It makes it easy to develop the latest technologies and features without having to start everything from scratch.
R has many built-in data analysis functions. The R language is mainly used for statistical and data analysis purposes. By default, R has many tools that are very important in research and development related to data analysis.
For data analysis, data visualization is an important part, because R provides many packages like ggplot.2ggvis, lattice, etc., which are very helpful in simplifying these implementations.
R has many packages for implementing applications related to data science. The availability of a large number of packages makes R the most resource-rich and versatile package.
When data analysis tasks need to be independently calculated or analyzed on a single server, R will be used in this case. The language is very useful for exploratory work and can handle any type of data analysis, and can implement larger solutions for this problem.
R language is mainly suitable for data science environments.
Python is a very flexible language, and it's great to do something new, focusing mainly on readability and simplicity. Python has many packages that can work in different areas of applications related to data science.
Both Python and R are good at finding outliers in datasets, but Python is better when it comes to uploading datasets to web services and finding outliers.
Python is a general-purpose programming language, which is why most data analysis functions are available.
Python also provides packages such as Lasagne, Caffe, Keras, Mxnet, OpenNN, TensorFlow, etc. These packages allow the development of deep neural networks, which are much simpler in Python.
Python, like Pandas and Scikit, is a rare data analysis software package. But it is very easy to achieve the goal.
When our data analysis tasks need to be integrated with web applications or need to merge statistical code into production databases, Python is used in this case. It is a very popular tool for implementing production use algorithms.
Python is widely used in many fields, such as-
Performing Computer Vision (Facial Detection and Color Detection, etc.)
Developing Games
Doing Machine Learning (Making Computers Learn)
Building a Website
Enabling Robots
Executing Scripts
Automating Web Browsers
Performing Scientific Calculations
Performing Data Analysis
Performing Web Scraping (Collecting Data from Websites)
Establishing Artificial Intelligence