Top 5 open source notebook for Data Science

Top 5 open source notebook for Data Science: The data science uses the Jupyter notebook for python and Rmarkdown for R. The output is presented by excel, PowerPoint and customer segmentation. They will run offline as often brittle as a one-off process. The notebook will provide a way of working that is repeated. They are used to create final client deliverability presented as web pages and automatically created. The notebook can scale a huge volume data complex when needed.

The source notebook for data science is described as below.

1) RStudio:-

The Rstudio is called the IDE for integrated development R programming language.
They are building for easy automation workflow of R with a user-friendly interface.
Some people are not comfortable with the terminals and built to make R easier.
The built-in packager manager is present there and data viewer of code.
The Rstudio is used for a long time on Linux and macOSx, analysis and data exploration.

2) Julia language IDE:-

It is the open-source general-purpose language and built to serve as the general-purpose language.
It will manage to attract data scientists and data engineers for data science.
The Julia language has many data-science packages which include data manipulation, mathematics, and Big Data packages.
They will support packages from Python, R, C/Fortran, C++, and Java.

3) Rodeo:-

They are similar to the python language and provide user interface like the Rstudio and features that include console pane, graphs manager.
They are available for Linux and windows.
The rodeo is an open-source and lightweight that is built for data science.
The rodeo editor is auto-complete and I python that includes tutorials to learn python.

4) Pros:-

1. The file navigator will allow a user to find what they are looking for.
2. Also, it helps the user to get an idea about data structure without writing code.

Cons:-

1. They are called slow development tools.
2. Also have many memory issues.

JASP:-

They are easy to use analysis software and perfect for beginners and students.
The JASP is installable for Windows, Linux, and macosx .
JASP will read multiple data files that will include txt,.csv and .ods.
They are open-source projects and have JASP functions.

5) SOFA statistics:-

They will connect database engines like SQLite, MS Access and SQL server.
Also separated by TSV and CSV as they are open source.

6) Gretl:-

They are open-source user-friendly software macosx and Linux.
They will support multiple file-formats excel files and values, comma-separated values.

7) GNU PSPP:-

It is an open-source for SPSS provides functionalities that support an open-source extension and open office.

8) Jupyter:-

The data science programming notebook and allows users to create a notebook with live code.
They will support programming languages Python, R, Julia which support apache spark and tensor flow.
The notebook for data scientists will support kernels with easy learning and cloud support.
The notebook is open source that allows creating and sharing the documents which support python.
The IDE will support and enable you to add HTML from images to videos.

Pros:-

1. We produce rich and interactive outputs.
2. They will edit snippets before running.

Cons:-

1. There are complex running  asynchronous tasks
2. here installation process is complex

BeakerX notebook:-

There is a collection of the kernel to jupyter and support Kotlin.
They will generate powerful data tablet-like scatter plots and treemaps.
There is a need to install the use of anaconda for a package of data science.

Pycharm:-

They are IDE of fully-fledged python scripting language which includes a feature like a debugger support web programming as well as code inspection.
They will not support python but support code written in SQL and database language.
They have easy completion of packages which support shortcuts for easy refactoring process.
Pros:-

1. The Version will control the system with external plug-in support.
2. Code is easy to run, edit or debug without any external requirement

Cons:-

1. They will have memory Intensive
2. Initial set-up can be time-consuming
3.   Spyder:-

They have a scientific environment in python that supports data science.
They will offer a combination of editing, analysis, and profiling functionality development tool.
The IDE will work on a multi-language editor with function and code analysis is done.
Pros:-

1. They are easy to use/install and intuitive interface.
2. Here they have excellent variable explorer.

Cons:-

1. More memory consumption.
2. Also fall in short non-data science projects.

Visual studio code:-

This editor is developed by Microsoft by using an electron and doesn’t use atom.
They have features as visual studio and code completion for debugging.
It has low weight and faster processing than any other notebooks IDE.

Thonny:-
The IDE is developed by Tartu for python and then created for beginners.
Also is teaching some features of Thonny without breakpoints and live variables using debugging.