What is data science?

Marketplace disruptors have one thing in common: data science. Data science is the process of discovering hidden insights from massive amounts of structured and unstructured data, using methods such as statistics, machine learning, data mining, and predictive analytics. This multi-discipline area is changing the way organizations solve problems and gain competitive advantage.

Businesses that can capture higher value from new data sources—weather, healthcare, traffic, retail, social sentiment etc. —and use it to differentiate themselves will define what it means to be cognitive businesses. For areas as diverse as predicting where to deploy your police force, to finding new markets for your product, to fighting infectious diseases before they are an outbreak, and turning call center agents into change agents with real-time information—data science is being applied by today’s new data artists: data scientists.

Who are data scientists?

To be successful, data scientists need an environment that is open, engaging, and fosters collaboration. They need:

  • Ability to use open source tools they know and love
  • Enterprise-grade functionality they’ll need for critical data science projects
  • Community that supports them throughout the whole process

In this seedbed of innovation, data scientists can break down data barriers and develop ideas that change the world.

The data science toolkit

Just as an artist requires different tools for different media, a data scientist needs a palette of capabilities to tackle the different problems they need to solve. IBM's data science toolkit offers the most advanced analytics, open source technology, and integrated development community, all built to encourage creativity and collaboration.

Self-service data science

Self-service data science

Best of open source combined with IBM innovation - work with Jupyter notebooks in a language of your choice: R, Python or Scala.

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Predictive analytics

Predictive analytics

Infuse predictive intelligence into every business decision.

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Machine learning

Machine learning

Create, train and deploy self-learning models

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Cognitive computing

Cognitive computing

Enhance and scale human expertise using cognitive computing.

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data science tools

Data scientists

Get ready access to the latest data science and data preparation capabilities via IBM’s reliable, managed services. And employ the proven DataFirst Method to map out a game plan for data science success.

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data science tools

IBM and Apache Spark

It’s not just about data access anymore. It’s about building algorithms that put analytics into action. It's about changing data science and driving intelligent apps fueled by data. Combining data, design and speed, IBM and Apache Spark are creating a new blueprint of innovation. IBM and Spark. Power of data. Simplicity of design. Speed of innovation.

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Watson Data Platform partner ecosystem

Watson Data Platform partner ecosystem offers a first of its kind open partnership program to build relationships with in the open analytics community directly with the business leaders, applications makers, and technology experts to decrease the time it takes for them to achieve success.

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Data science resources

 

A glimpse inside the mind of a data scientist

It's all well enough for an organization to collect every slice of data it can reach, but having more data doesn't mean you'll automatically get better insights. First, you have to figure out what you want from your data you have to find its value.

 

Achieve more successful outcomes with a data science methodology

Data scientists typically construct models to predict outcomes or discover underlying patterns with the goal of gaining actionable insights. IBM's data science methodology ensures that as data scientists learn more about their data and models, they can quickly make adjustments and provide continuous value to the organization.

 

Spark: The Next Wave of Intelligent Applications

Life revolves around prediction. For example, the route you take to get to work, whether to go on a second date, or whether to keep reading this sentence are all forms of prediction. Predicating our future is very much tied to progress. We use it to help us plan our lives so we can increase our likelihood of success. The problem is that the human mind cannot possibly process all of the insight flowing from big data.

Get started with the IBM Data Science Experience