IBM Data Science 


Your data science team can help multiple departments, using a diverse set of tools and techniques available on the IBM Data Science Platform.

The New Data Science Experience with AI – Infusing Data Science & AI into your business

IBM Data Science, and why does it matter?


Data science is the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making.

Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights backed by tools for ModelOps and cloud data science.

The data science lifecycle starts with gathering data from relevant sources, cleaning it and putting it in formats that machines can understand. In the next phase, statistical methods and other algorithms are used to find patterns and trends. Then models are programmed and built to predict and forecast; finally, results are interpreted.

Advances in AI, machine learning and automation have raised the standards of data science tools for business. The result is the formation of data science teams — expert data scientists, citizen data scientists, programmers, engineers and business analysts — that extend across business units.

The opportunity here is massive. The automation of tedious data science tasks such as data preparation, and the empowerment of analysts without coding experience (00:21) to build models, keeps business agile and innovative. Automating the data science lifecycle frees expert data scientists to address the more interesting and innovative aspects of the field. Human intelligence — combined with data science technology and automation — helps a business extract greater value from data.

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Why IBM Data Science Matters today

With the volume and variety of social, mobile and device data, along with new technologies and tools, data science (03:43)  today plays a broader role than ever before. Business considers data science and AI (06:13) to be a technology-enabled strategy. In order for data science to be effective, its full lifecycle not only must support traditional analytics, but it must also work in concert with modern applications. This means that the data science practice must evolve beyond routine, tedious tasks — as much 85% of a data scientist’s time is spent cleaning, shaping and moving data from place to place, often to feed machine learning. That leaves only a small percentage of time to find patterns and trends, to build models, to predict and forecast, and to interpret results.

Fortunately, there is relief. The latest development in modern data science is an AutoAI capability that automates the data preparation and modeling stages of the data science lifecycle. Now, not only can more data scientists use their specialized skills the way they were intended; but more businesses can benefit from data science, from prediction to optimization.


Some of the Key Questions Data Science Will help you to Solve 


  • Which next thousand customers will we lose and why?
  • Where should we set up another kiosk or a new store?
  • Which high-performing employees are we at greatest risk of losing?
  • If we price products differently, will we save costs?
  • Is my team offering the right things to the right people?

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