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GraphPad Prism VS Dataiku

Compare GraphPad Prism VS Dataiku and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

GraphPad Prism logo GraphPad Prism

Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

Dataiku logo Dataiku

Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.
  • GraphPad Prism Landing page
    Landing page //
    2023-03-24
  • Dataiku Landing page
    Landing page //
    2023-08-17

GraphPad Prism

Website
graphpad.com
Pricing URL
-
Release Date
-
Edit details

Dataiku

Website
dataiku.com
Pricing URL
Official Dataiku Pricing
Release Date
2013 January
Startup details
Country
United States
State
New York
City
New York
Founder(s)
Clément Stenac
Employees
500 - 999
Edit details

GraphPad Prism videos

+ Add

GraphPad Prism Tutorial 1 - Introducing Table Types

More videos:

  • Tutorial - ELISA Tutorial 6: How to Analyze ELISA Data with GraphPad Prism

Dataiku videos

+ Add

AutoML with Dataiku: And End-to-End Demo

More videos:

  • Review - Dataiku: For Everyone in the Data-Powered Organization
  • Tutorial - Dataiku DSS Tutorial 101: Your very first steps

Category Popularity

0-100% (relative to GraphPad Prism and Dataiku)

GraphPad Prism

Dataiku

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare GraphPad Prism and Dataiku

GraphPad Prism Reviews

25 Best Statistical Analysis Software
GraphPad Prism is a powerful statistical software package specifically tailored for scientific research purposes. This is an excellent choice for those seeking to perform statistical analysis, nonlinear regression, graphing, and data visualization with ease.
Source: www.scijournal.org

Dataiku Reviews

15 data science tools to consider using in 2021
Some platforms are also available in free open source or community editions -- examples include Dataiku and H2O. Knime combines an open source analytics platform with a commercial Knime Server software package that supports team-based collaboration and workflow automation, deployment and management.
Source: searchbusinessanalytics.techtarget.com
The 16 Best Data Science and Machine Learning Platforms for 2021
Description: Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch....
Source: solutionsreview.com

What are some alternatives?

When comparing GraphPad Prism and Dataiku, you can also consider the following products

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Stata - Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

NumPy - NumPy is the fundamental package for scientific computing with Python

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