Ten expert tips for visual data science

Creative and Editorial Lead, Data and AI, IBM

Data science and machine learning provide the basis for business growth, cost and risk reduction, and even new business model creation, but implementing predictive analytics does present some challenges.

Not only is the process complex, but it can also be difficult to find data scientists and analysts with the right mix of skillsets.

IT Central Station members have shared tips that help organizations overcome the challenges in effective data preparation, model development and training.

IBM visual data scienceWith a visual data science approach based on their use of the IBM SPSS Modeler, they recommend taking advantage of tools and techniques that speed up the data science lifecycle.

Many of their tips deal with empowering people who aren’t data scientists to accomplish sophisticated analytic tasks through solutions such as IBM SPSS Modeler, which is designed for the business or IT generalist.

This new peer paper report titled “Modern Data Science: Best Practices for Predictive Analytics” lists issues that can create an obstacle to success with data science and machine learning projects, the top one being the inability to hire and retain data scientists.

But enough about problems. How about solutions?

Here are the 10 tips for visual data science.

  1. Deploy quickly by using GUI-based Machine Learning algorithms.
  2. Take advantage of open source-based innovation including R or Python.
  3. Seek ROI by speeding up the end to end data lifecycle.
  4. Empower people with varying levels of skill with an intuitive user interface.
  5. Exploit a multicloud approach.
  6. Prototype and iterate quickly.
  7. Integrate into environments to deploy real time and near real time.
  8. Start small and scale the solution up and out.
  9. Make use of online documentation.
  10. Look for proven experience and expertise in a vendor.

A drag-and-drop visual data science tool, exemplified by IBM SPSS Modeler, enables rapid creation of machine learning models while making it easy to collaborate with data science and analytics teams as a whole. In particular, IBM SPSS Modeler extends to the open source environment for data scientists who code in R and Python, where new innovation and custom algorithms can be built.

In this paper, members of IT Central Station who use IBM SPSS Modeler share their experiences and offer insights and recommended best practices for data science and machine learning.

Get IT Central’s peer paper here.