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Jupyter Notebook

Jupyter Notebook

Overview

What is Jupyter Notebook?

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports…

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Recent Reviews

TrustRadius Insights

Jupyter Notebook has found a wide range of use cases across various industries and roles. Data analysts, scientists, and engineers have …
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A Notebook for All

10 out of 10
October 24, 2021
Incentivized
Jupyter Notebook is a coffee to me, a daily dose of it makes my day. I have been using Jupyter Notebook for various purposes from …
Continue reading
Read all reviews

Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 16 features
  • Interactive Data Analysis (21)
    9.6
    96%
  • Visualization (21)
    9.6
    96%
  • Connect to Multiple Data Sources (21)
    9.0
    90%
  • Data Transformations (21)
    8.9
    89%
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Pricing

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N/A
Unavailable

What is Jupyter Notebook?

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine…

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://www.trustradius.com/buyer…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

H2O TensorFlow Deep Learning Demo

YouTube

EXPORT JUPYTER to EXCEL | nb2xls | Demo & My Thoughts | Jupyter Notebook to Excel Spreadsheet

YouTube

Jupyter Notebook using Docker for Data Science (Demo)

YouTube

Lecture 11, Python Demo for Distribution

YouTube
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

8.5
Avg 8.5

Data Exploration

Ability to explore data and develop insights

9.6
Avg 8.4

Data Preparation

Ability to prepare data for analysis

9
Avg 8.2

Platform Data Modeling

Building predictive data models

8.9
Avg 8.5

Model Deployment

Tools for deploying models into production

8.8
Avg 8.6
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Product Details

What is Jupyter Notebook?

Jupyter Notebook Video

How to install and use Jupyter Notebooks, a step by step tutorial. Learn to when to use Jupyter Notebooks, and how to write and run code and markdown.

Jupyter Notebook Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for Jupyter notebooks, code, and data, with a configurable user interface that supports a wide range of workflows in data science, scientific computing, and machine learning.

Reviewers rate Visualization and Interactive Data Analysis highest, with a score of 9.6.

The most common users of Jupyter Notebook are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(123)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Jupyter Notebook has found a wide range of use cases across various industries and roles. Data analysts, scientists, and engineers have utilized Jupyter Notebook to handle different types of data, including csv, excel, and json files for efficient data analysis in banking and e-learning industries. The flexibility of the notebook allows users to organize workflows into manageable chunks, making them easier to replicate for future projects. Additionally, it has been widely adopted by Science and Analytics departments for tasks such as data wrangling, graph visualization, machine learning model creation, and suite of analyses to understand business landscapes. Jupyter Notebook has also been instrumental in training and fine-tuning machine learning models efficiently, as well as automating tasks like scraping data from PDFs. With its ability to integrate with cloud desktops and production systems, Jupyter Notebook is widely used by data scientists in organizations. Furthermore, it serves as a platform for collaboration and code sharing among teams, making it valuable for project management. Overall, Jupyter Notebook's interactive environment and flexibility have made it a versatile tool recommended for data analysts, managers, engineers, and data scientists seeking to explore, analyze, visualize, and deploy Python code efficiently.

Intuitive Organization: Users have found Jupyter Notebook's visually intuitive organization of code to be beneficial for understanding and navigating through different sections. This seamless workflow has increased productivity for many reviewers.

Static but Changeable Display: The static but changeable display of function outputs in Jupyter Notebook has been highly valued by users, allowing them to easily replicate notebooks or convert them into PDFs for documentation and sharing purposes. This feature provides a convenient way to showcase analysis results and collaborate with others.

Step-by-Step Output Viewing: Many users appreciate the ability to see the output after each step in Jupyter Notebook, enabling a step-by-step approach to data analysis and visualization. This functionality helps users identify and rectify errors or inconsistencies in their code efficiently.

Limited Features: Some users have expressed that Jupyter Notebook lacks certain features and functionality, such as limited markdown styling and the inability to handle multiple kernels. They feel that these limitations restrict their ability to fully utilize the software for their needs.

Difficult Code Styling and Navigation: A common concern among users is the difficulty in styling Python code within Jupyter Notebook and navigating through long notebooks. Several reviewers have suggested the addition of bookmarks or easier ways to navigate, as they find it time-consuming and cumbersome to work with large amounts of code.

Absence of Dark Mode Option: The absence of a dark mode option has been mentioned by several users as a helpful feature that could improve the user experience. They believe that having a dark mode would reduce eye strain during prolonged usage sessions and enhance overall readability.

Attribute Ratings

Reviews

(1-22 of 22)
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Score 7 out of 10
Vetted Review
Verified User
Platform Connectivity (2)
N/A
N/A
Extend Existing Data Sources
N/A
N/A
MDM Integration
N/A
N/A
Data Exploration (2)
N/A
N/A
Visualization
N/A
N/A
Interactive Data Analysis
N/A
N/A
Data Preparation (2)
N/A
N/A
Interactive Data Cleaning and Enrichment
N/A
N/A
Built-in Processors
N/A
N/A
Platform Data Modeling (3)
N/A
N/A
Multiple Model Development Languages and Tools
N/A
N/A
Single platform for multiple model development
N/A
N/A
Self-Service Model Delivery
N/A
N/A
Model Deployment (2)
N/A
N/A
Flexible Model Publishing Options
N/A
N/A
Security, Governance, and Cost Controls
N/A
N/A
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (3)
83.33333333333334%
8.3
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
70%
7.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
95%
9.5
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
100%
10.0
Data Encryption
80%
8.0
Built-in Processors
100%
10.0
Platform Data Modeling (4)
82.5%
8.3
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
50%
5.0
Model Deployment (2)
75%
7.5
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
70%
7.0
Shailesh Pandey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Platform Connectivity (4)
90%
9.0
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
100%
10.0
Automatic Data Format Detection
90%
9.0
MDM Integration
80%
8.0
Data Exploration (2)
95%
9.5
Visualization
90%
9.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
90%
9.0
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
80%
8.0
Data Encryption
90%
9.0
Built-in Processors
100%
10.0
Platform Data Modeling (4)
95%
9.5
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
95%
9.5
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
100%
10.0
October 24, 2021

A Notebook for All

Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (3)
73.33333333333333%
7.3
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
MDM Integration
40%
4.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (3)
83.33333333333334%
8.3
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
80%
8.0
Data Encryption
80%
8.0
Platform Data Modeling (4)
80%
8.0
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
70%
7.0
Model Deployment (2)
90%
9.0
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
90%
9.0
Shivam Rai | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Platform Connectivity (4)
97.5%
9.8
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
100%
10.0
Automatic Data Format Detection
100%
10.0
MDM Integration
90%
9.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
95%
9.5
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
90%
9.0
Data Encryption
100%
10.0
Built-in Processors
90%
9.0
Platform Data Modeling (4)
95%
9.5
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
100%
10.0
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
100%
10.0
September 27, 2021

Jupyter Pros and Cons

Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (3)
90%
9.0
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
90%
9.0
Data Exploration (2)
95%
9.5
Visualization
100%
10.0
Interactive Data Analysis
90%
9.0
Data Preparation (2)
90%
9.0
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Platform Data Modeling (4)
95%
9.5
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
90%
9.0
Model Deployment
N/A
N/A
Lindsay Veazey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
100%
10.0
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
100%
10.0
Automatic Data Format Detection
100%
10.0
MDM Integration
100%
10.0
Data Exploration (2)
80%
8.0
Visualization
80%
8.0
Interactive Data Analysis
80%
8.0
Data Preparation (4)
65%
6.5
Interactive Data Cleaning and Enrichment
80%
8.0
Data Transformations
100%
10.0
Data Encryption
N/A
N/A
Built-in Processors
80%
8.0
Platform Data Modeling (4)
100%
10.0
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
100%
10.0
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
100%
10.0
Ejaz Hussain | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
87.5%
8.8
Connect to Multiple Data Sources
80%
8.0
Extend Existing Data Sources
100%
10.0
Automatic Data Format Detection
100%
10.0
MDM Integration
70%
7.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
100%
10.0
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
100%
10.0
Data Encryption
100%
10.0
Built-in Processors
100%
10.0
Platform Data Modeling (4)
85%
8.5
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
70%
7.0
Model Deployment (2)
95%
9.5
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
90%
9.0
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (3)
63.33333333333333%
6.3
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
100%
10.0
MDM Integration
N/A
N/A
Data Exploration (2)
95%
9.5
Visualization
90%
9.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
45%
4.5
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Data Encryption
N/A
N/A
Built-in Processors
N/A
N/A
Platform Data Modeling (4)
100%
10.0
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
95%
9.5
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
90%
9.0
Hemant Jajoo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
57.5%
5.8
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
50%
5.0
Automatic Data Format Detection
N/A
N/A
MDM Integration
80%
8.0
Data Exploration (2)
85%
8.5
Visualization
80%
8.0
Interactive Data Analysis
90%
9.0
Data Preparation (3)
53.33333333333333%
5.3
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
70%
7.0
Built-in Processors
N/A
N/A
Platform Data Modeling (4)
55%
5.5
Multiple Model Development Languages and Tools
70%
7.0
Automated Machine Learning
N/A
N/A
Single platform for multiple model development
70%
7.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
70%
7.0
Flexible Model Publishing Options
60%
6.0
Security, Governance, and Cost Controls
80%
8.0
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
70%
7.0
Connect to Multiple Data Sources
70%
7.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
70%
7.0
MDM Integration
60%
6.0
Data Exploration (2)
85%
8.5
Visualization
90%
9.0
Interactive Data Analysis
80%
8.0
Data Preparation (4)
82.5%
8.3
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
80%
8.0
Data Encryption
80%
8.0
Built-in Processors
80%
8.0
Platform Data Modeling (4)
80%
8.0
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
85%
8.5
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
80%
8.0
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
82.5%
8.3
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
80%
8.0
MDM Integration
70%
7.0
Data Exploration (2)
90%
9.0
Visualization
90%
9.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
85%
8.5
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Data Encryption
80%
8.0
Built-in Processors
80%
8.0
Platform Data Modeling (4)
90%
9.0
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
95%
9.5
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
90%
9.0
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
85%
8.5
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
80%
8.0
MDM Integration
90%
9.0
Data Exploration (2)
95%
9.5
Visualization
100%
10.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
75%
7.5
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Data Encryption
60%
6.0
Built-in Processors
60%
6.0
Platform Data Modeling (4)
85%
8.5
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
80%
8.0
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
80%
8.0
Rodrigo Pérez Romero | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
100%
10.0
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
100%
10.0
Automatic Data Format Detection
100%
10.0
MDM Integration
100%
10.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
100%
10.0
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
100%
10.0
Data Encryption
100%
10.0
Built-in Processors
100%
10.0
Platform Data Modeling (4)
100%
10.0
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
100%
10.0
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
100%
10.0
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (1)
100%
10.0
Connect to Multiple Data Sources
100%
10.0
Data Exploration (2)
80%
8.0
Visualization
100%
10.0
Interactive Data Analysis
60%
6.0
Data Preparation (1)
100%
10.0
Data Transformations
100%
10.0
Platform Data Modeling (3)
100%
10.0
Multiple Model Development Languages and Tools
100%
10.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (1)
100%
10.0
Flexible Model Publishing Options
100%
10.0
Arpit Ranka | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
82.5%
8.3
Connect to Multiple Data Sources
80%
8.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
80%
8.0
MDM Integration
80%
8.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
87.5%
8.8
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Data Encryption
80%
8.0
Built-in Processors
90%
9.0
Platform Data Modeling (4)
82.5%
8.3
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
70%
7.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
90%
9.0
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
90%
9.0
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (2)
90%
9.0
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (2)
90%
9.0
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Platform Data Modeling (4)
87.5%
8.8
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
55%
5.5
Flexible Model Publishing Options
60%
6.0
Security, Governance, and Cost Controls
50%
5.0
Irene Rodríguez Alegre | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
32.5%
3.3
Connect to Multiple Data Sources
50%
5.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
N/A
N/A
MDM Integration
N/A
N/A
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
45%
4.5
Interactive Data Cleaning and Enrichment
100%
10.0
Data Transformations
80%
8.0
Data Encryption
N/A
N/A
Built-in Processors
N/A
N/A
Platform Data Modeling (4)
40%
4.0
Multiple Model Development Languages and Tools
N/A
N/A
Automated Machine Learning
N/A
N/A
Single platform for multiple model development
70%
7.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
N/A
N/A
Flexible Model Publishing Options
N/A
N/A
Security, Governance, and Cost Controls
N/A
N/A
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
77.5%
7.8
Connect to Multiple Data Sources
80%
8.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
80%
8.0
MDM Integration
70%
7.0
Data Exploration (2)
90%
9.0
Visualization
90%
9.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
77.5%
7.8
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
80%
8.0
Data Encryption
70%
7.0
Built-in Processors
70%
7.0
Platform Data Modeling (4)
80%
8.0
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
80%
8.0
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
80%
8.0
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (3)
83.33333333333334%
8.3
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
MDM Integration
70%
7.0
Data Exploration (2)
90%
9.0
Visualization
90%
9.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
72.5%
7.3
Interactive Data Cleaning and Enrichment
80%
8.0
Data Transformations
80%
8.0
Data Encryption
80%
8.0
Built-in Processors
50%
5.0
Platform Data Modeling (4)
70%
7.0
Multiple Model Development Languages and Tools
70%
7.0
Automated Machine Learning
70%
7.0
Single platform for multiple model development
70%
7.0
Self-Service Model Delivery
70%
7.0
Model Deployment (2)
75%
7.5
Flexible Model Publishing Options
60%
6.0
Security, Governance, and Cost Controls
90%
9.0
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
27.5%
2.8
Connect to Multiple Data Sources
20%
2.0
Extend Existing Data Sources
20%
2.0
Automatic Data Format Detection
60%
6.0
MDM Integration
10%
1.0
Data Exploration (2)
40%
4.0
Visualization
40%
4.0
Interactive Data Analysis
40%
4.0
Data Preparation (4)
60%
6.0
Interactive Data Cleaning and Enrichment
60%
6.0
Data Transformations
60%
6.0
Data Encryption
60%
6.0
Built-in Processors
60%
6.0
Platform Data Modeling (4)
75%
7.5
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
60%
6.0
Model Deployment (2)
70%
7.0
Flexible Model Publishing Options
70%
7.0
Security, Governance, and Cost Controls
70%
7.0
October 22, 2020

Jupyter or nothing

Score 10 out of 10
Vetted Review
Verified User
Incentivized
Platform Connectivity (4)
85%
8.5
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
90%
9.0
MDM Integration
70%
7.0
Data Exploration (2)
100%
10.0
Visualization
100%
10.0
Interactive Data Analysis
100%
10.0
Data Preparation (4)
55%
5.5
Interactive Data Cleaning and Enrichment
40%
4.0
Data Transformations
50%
5.0
Data Encryption
40%
4.0
Built-in Processors
90%
9.0
Platform Data Modeling (3)
86.66666666666666%
8.7
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
80%
8.0
Model Deployment (2)
45%
4.5
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
10%
1.0
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