Most of the organizations are using data science project and platforms to handle huge data volumes.
A platform helps to create a logical workflow, gives version controls, and facilitates integrations. It scales and creates better models in lesser time. In this DASCA infographic, a compiled list of commonly used data science platforms by data scientists across the globe are briefed with its main functionalities.
Have a quick rundown of some of the best data science platforms that are extensively used to turn their data into a valuable resource and create business value.
Apache Spark
Apache Spark is preferred because of its speed, ease of use and unified engine. Spark is fast for processing data as compared to other known platforms. It has easy-to-use APIs for operating on a large dataset. Moreover, the higher-level libraries can get combined seamlessly and increase the productivity of a developer.
JavaScript
JavaScript reduces the compatibility issues as it runs on almost all platforms. It enables data scientists to collect data, run algorithms in a streamlined manner. It is aggressively used for asynchronous tasks, visualizations, and handling real-time data.
Keras
Kera is used to develop and evaluate deep learning models. It is easy for any data scientist to get started with neural networks more easily while using Kera.
Chainer
Chainer framework, written in Python is flexible and intuitive. It is a high-performance medium to implement a full range of deep learning models that are needed by data scientists.
Ggplot 2
Gg in the ‘ggplot’ refers to ‘grammar of graphics.’ It is the most versatile platform used to create a meaningful data visualization that helps for strong decision-making.
Anaconda
It is extensively used for machine learning applications, large-scale data processing, predictive analytics, and many more applications.
Matplotlib
Visualization plays a fundamental role to communicate results. Data scientists find it comfortable and often use Matplotlib to create, customize, and share advanced data visualizations.
SciKit learn
It is one of the simple and efficient tools used to carry predictive data analysis.
Tableau
Tableau is being extensively used by data scientists and business intelligence professionals everywhere. It creates insightful and impactful visualizations colorfully and interactively.
PyTorch
The uniqueness of PyTorch is that it can be used with Python and C++ as well. It is widely used to develop and train neural network-based deep learning models.
BigML
It is used to import data and derive predictions. It enables users with a minimal machine learning experience to improve decision-making.
Theano
It facilitates the user to optimize and evaluate mathematical expressions that involve multi-dimensional arrays.
Shogun
Shogun is used to solve classification problems. Moreover, it facilitates data scientists to exchange and analyze their workflows.
DL4J
Deep learning 4J bridges the gap between data scientists who use Python and developers who use Java. It makes the deep learning deployment easier in enterprise big data applications.
Jupyter
Jupyter facilitates data scientists to leverage big data. It enables them to create and share documents including codes and reports.
Rapid miner
It supports the machine learning process and can be readily used for research, business and commercial applications
Python
Python is used for applications like natural language processing, sentiment analysis, and more for solving complex business problems, build systems and data applications.
TensorFlow
It is used to create large-scale neural networks having multilayers. It makes the data scientists’ tasks easier.
PC
PyCharm is used by data scientists as it enables them to show all the created variables.
IBM Infosphere
It bridges the gap between business and IT. It helps to integrate data across systems, govern information, improve productivity, and align business.