Dataiku

Data Science Studio (DSS)

4.5 / 5 2 reviews

Who Uses This Software?

DSS is for all companies, whatever their expertise, industry or size, that want to create their own data-driven strategic advantages by transforming their raw data into business impacting predictions.


Average Ratings

2 Reviews
  • 4.5 / 5
    Overall
  • 5 / 5
    Ease of Use
  • 4.5 / 5
    Customer Service

Product Details

  • Starting Price
    $0.01/year/user
  • Free Version
    Yes
  • Free Trial
    Yes
  • Deployment
    Cloud, SaaS, Web
    Installed - Mac
  • Training
    Documentation
    Webinars
    Live Online
    In Person
  • Support
    Online
    Business Hours

Vendor Details

  • Dataiku
  • www.dataiku.com/
  • France

About Data Science Studio (DSS)

Dataiku develops DSS, the most productive development platform for data professionals. With its highly comprehensive visual interface, DSS encourages collaborative work between both business & tech profiles when working on a same project. Because DSS integrates standard data and machine learning tools and technologies that users can transparently appropriate, the platform enables them to efficiently achieve their business goals all the while keeping complete control of their work.


Data Science Studio (DSS) Features

  • Access Control
  • Code Assistance
  • Collaboration
  • Debugging
  • Graphical User Interface
  • Mobile Development
  • Software Development
  • Testing Management
  • Version Control
  • Web Development

Data Science Studio (DSS) Reviews

Excellent software for data and business teams collaboration in building data science applications

Nov 21, 2015
5/5
Overall
5 / 5
Ease of Use
5 / 5
Customer Support

Comments: Easier way for data and business teams collaboration aiming to build data science applications

Making Kaggle Submissions with DSS

Apr 27, 2015
4/5
Overall
5 / 5
Ease of Use
4 / 5
Customer Support

Comments: As a non - data scientist, i was curious to see how DSS could help me with the data preparation (cleaning and combining data), feature engineering and predictive modelling phases of a data analysis project

My goal was to make 2 submissions on Kaggle challenges in under 1 hour and without 1 line of code using the Data Science Studio (Titanic and Otto Product Classification datasets).

First, I was really impressed with the overall ease of use and ergonomy of the studio. Building "recipes" for data preparation mostly uses visual processors and the operations are visible directly on a sample of the data, facilitating validation of preparation steps.
In a train / test scenario, i especially enjoyed being able to replicate my recipes on both datasets very easily.
I used the Data Visualization tool to build a few exploratory charts, which can be done quite easily, though it is not as powerful as specialized tools (namely Tableau or Qlik).

For the machine learning part, I restricted myself to visual machine learning in the studio, which already packs the most common algorithms (random forest, logistic, svm, gradient-boosting...). I found the ability to benchmark and compare algorithms performance quickly a great time saver, allowing me to reach a first score in under half an hour on each dataset.

Once I chose the best model, I only needed a few clicks to use the model to prepare and score the Test Dataset and make my submissions. Both times I was in the lower half of the rankings but above Kaggle algorithmic benchmarks.

For "real" Data Scientists and engineers, the Studio allows them to go much further by building recipes and models in R, Python, SQL, Hive, Pig etc...but even as a business analyst, I felt empowered by the software that enabled me to prepare, analyse and build simple predictive models with my data.