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In this listicle, youll find some of the bestdata science notebooksto supercharge your teams workflow.
These data science notebooks facilitate better collaboration and can be alternatives to the Jupyter notebook.
In this guide, well discuss the use of the classic Jupyter notebook for data science projects.
And then, well go over other data science notebooks.
Additionally, well also enumerate each of these notebooks features.
For all this and more, lets get started.
Jupyter Notebook for Data Science
Jupyter notebookis an interactive web-based platform used in data science projects.
Itll walk you through the Jupyter notebooks features and help you set up your working environment.
Lets now go over other data science notebooks you could consider.
If you are interested in learning Python and Jupyter, check out thisUdemy course.
Head over to the following sections to learn more.
Deepnote
Deepnoteis a cloud-based Jupyter notebook environment.
It is designed to allowdata scienceteams to collaborate effectively.
Or you could work as part of a team.
These notebooks are well-suited for performing big data analysis as a team.
In most data science projects, the data collection phase involves querying databases to fetch required data.
Mode Notebooks allow you to query data from connected data sources with SQL.
On the development front, Datalore includes features for coding assistancewith an intelligent code editor.
It also allows teams to work with multiple data sources.
In addition, there are enhanced features for collaboration and reporting.
If youre a data science enthusiast,Google Colabcan be a great way to start building projects.
Do you already use Colab for your data science projects?
If yes, check out this video tutorial outlining the cool features of Colab that you should be using.
Google Colab also has the following salient features:
Nextjournal
Nextjournalis another collaborative data science notebook.
With the taglineThe notebook for reproducible research, Nextjournal facilitates real-time collaboration with an emphasis on reproducibility.
Count
Countoffers a data science notebook with added flexibility for customization.
Counts design goal is to change the way data teams work together.
Their vision is to provide a collaborative data platform that connects analysts to stakeholders.
And allows teams to go from ideation to analysis in data science projects faster.
Kaggle
Kagglealso offers a web-based Jupyter notebook environment designed to ensure reproducible and collaborative analysis.
These notebooks can be a great way to showcase your data science projects.
Its also helpful in building a portfolio of data science projects, right from the web app.
Kaggle offers the following two flavors:
The notebook interface allows you to manage datasets and hardware accelerators.
you’re free to use all datasets hosted on Kaggle or datasets from competitions.
Participating inKaggle competitionswill help you level up your data science skills all the more rapidly.
Heres a video tutorial on getting started with Kaggle.
Databricks Notebooks
Databricks notebooksare collaborative data science notebooks as well.
Further, they also allow interactive data visualization and support multiple programming languages.
In addition, Databricks notebooks also support real-time co-authoring and version control.
Watch this video tutorial to get started with Databricks notebooks.
In addition to the features of the classic Jupyter notebook, CoCalc provides an integrated course management system.
Observable
Observable notebookis another collaborative platform for data science teams.
With the taglineExplore, analyze, and explain data.
As a team, Observable aims to bring together data analysts, developers, and decision-makers.
It also facilitates seamless collaboration between teams.
In addition, having the proper tooling helps teams to collaborate effectively!
From big data analysis to academia and reproducible researchyou have data science notebooks tailor-made for many use cases.
Happy teamwork and collaborative data science!
