IBM’s Data Science Experience IBM’s Data Science Experience
Despite the backlash against Data Science’s hype, there is no doubt that the field is vital in our data filled world. It is no... IBM’s Data Science Experience

Despite the backlash against Data Science’s hype, there is no doubt that the field is vital in our data filled world. It is no surprise that some leading companies are dashing to enhance their Data Science offerings. Within the past few weeks, IBM made the latest surge in this race.

The company’s analytics content revolves around IBM Watson powered Natural Language Processing APIs. The Juypter notebook, Apache Spark, and the PyData ecosystem rounds out its current offerings. IBM’s new platform promises to provide a much more holistic experience.

The IBM Data Science Experience incorporates a wider breadth of Data Science tools. Users can code in Python, Scala, or R in Apache Spark enabled Jupyter notebooks. Support is also provided for RStudio and Shiny, R’s package for data-driven web apps. IBM has also enhanced its own internal offerings. Users have access to the Sparkling.Data and Prescriptive Analytics libraries. (The former is for data cleaning and the latter for modeling.)

Collaboration and learning are central to the Data Science Experience’s ethos. A user-generated library of notebooks, tutorials, and data sets round out its structure.  The IBM Data Science experience is still under wraps with only a limited number of current users. Its full release will show if the company’s surge will be enough to separate it from the pack.


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Gordon Fleetwood

Gordon Fleetwood

Gordon studied Math before immersing himself in Data Science. Originally a die-hard Python user, R's tidyverse ecosystem gradually subsumed his workflow until only scikit-learn remained untouched. He is fascinated by the elegance of robust data-driven decision making in all areas of life, and is currently involved in applying these techniques to the EdTech space.