Data science is one of the hottest technology in the 21st century. This technology can be practiced with some modern and complex tools. The below tools are the best and open source tools available to practice data science. Data Science involves different languages like Python, R, Scala, Hadoop, etc. So it has lots of tools and software's. Some of the important ones are mentioned below.
It is a great platform of IBM which consists of all the inbuilt tools used in data science needed for the data scientist to analyze the data. It consists of all the open source data sets in which is very useful for the user to practice his skills on the data. The inbuilt data sets include the United Nations data, Industry data, Search engine data, etc. You can upload your dataset here and can use it for the future. You can manage your own data in the mydata tab.
It is an interactive tool used in data analysis. This tool allows you to execute small chunks of python code in cells. It allows you to embed data and other important information. It allows the data scientist to tell a story along with the analysis.you can also see your visualization in the same tool.You can also include markdown that is HTML in the jupyter notebooks. It is extensively used by beginners. You can also use HTML to embed an image in the notebook.
This is one of the open source tools used by the data scientist who use R language to analyze and visualize data. It has a lot of statistical packages but inside it. I like the visualization libraries of R compared to Python. It can be used to create beautiful visualizations. It consists of the code editor and also a tab in the window that displays all the variables and packages used and the visualizations created by you. You can also use this IDE to develop web applications with R using the R Shiny library. It also consists of the R console. It is like an all in one platform.
It is a Zeplin Notebook web-based open source tool which supports the data ingestion, data discovery, data analytics, data visualization, data collaboration and so on. It supports multiple languages. For backend, it supports python, JDBC.
With this Tool, it becomes very easy to visualize Big data and Machine learning problems and solve them. We can develop a workflow for developing and deploying an application.