Dataiku vs. JMP Pro

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Incentivized

Anything marketing research and statistical analysis. It does the job well and provides a wide statistical toolset.

Incentivized

• Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
• Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
• Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
• Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
• Support from research till final MaaS solution deployment.

Incentivized

• Several types of segmentation models
• Conjoint design
• VERY user-friendly

Incentivized

• Its community support is very limited at the moment
• Complex to integrate with automation tools such as Blue Prism

Incentivized

• JMP Pro is a really powerful tool for doing statistical analysis. Although the click environment does not require coding experience, new learners will still need to take a long time to know the parameters in the function before performing any analysis.
• The output from JMP Pro analysis (regression analysis) is not always easy to understand, especially when the parameters are programmed differently with the other similar software.

Incentivized

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

Incentivized

The intuitive interface JMP Pro offers is more interactive than R studio, but since coding is not required in JMP Pro, programming analysis in R studio is more convenient in some aspects. JMP Pro is able to quickly analyze multiple variables and produce reasonable outputs and models, which is valuable for users doing data analysis.

Incentivized

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• It helped me put together meteorological data from different locations to show which area is the optimal location for wind energy.

Incentivized