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Data Science and Advanced Analytics

Machine learning as competitive advantage for your business

Predictive analytics helps the worlds largest perfumery and cosmetics franchiser understand what customers want, before they even know they want itenabling smarter sales, marketing and production planning.

Combine machine learning models with advanced prescriptive modeling to optimize complex business decisions. Use fast visual modeling capabilities without coding, advanced data preparation capabilities, and automatically handle common data quality issues. Bring your analytics to the data behind your firewall, and easily incorporate cloud application data and sources.

Your favorite open source tools, with multi-cloud scalability

In downtown San Francisco, engineers, Apache Spark Committers, and designers contribute to Apache Spark and design optimal UX experiences for people using Spark-based applications.

Succeeding with data science takes a holistic approach

Infuse continuous intelligence into your enterprise applications with an end-to-end platform for developing and deploying data science projects quickly. IBM has the leading data science platform that allows you to easily collaborate across teams, use the top open source tools and scale at the speed your business requires.

Transform your data into tangible business value with the latest, most flexible and open technology. Enable your data scientists, data engineers, machine learning engineers, and analysts to collaborate with the best-in-class open source tools and visual tools, along with the most flexible and scalable deployment options.

Analyzing big data in seconds unlocks never-before-seen capabilities, helping to win new viewers and advertisers.

Find out what data scientists really think about their critical role in data science.

Take control of customer satisfaction by using predictive analytics to embed a deeper understanding of customers into operations.

Understanding machine learning techniques

Research from Gartner and insights from IBM

Working together, learn how the data science team can outthink todays challenges and problems to create new opportunities and possibilities for tomorrow.

Putting machine learning in context

Predictive analytics brings together advanced analytics capabilities, spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning. These tools help organizations discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next.

Dinesh Nirmal explains how your data can help you build the right cognitive systems to learn about, reason with, and engage with your customers.

Applying machine learning to business needs

Tying machine learning methods to outcomes

Prescriptive analytics helps organizations make better decisions and recommend the best course of action, whether you want to decide on a configuration, a design, a plan, or a schedule. This is done by optimizing trade-offs between business goals while considering business constraints on available resources.

Empowering business with visual approaches to data science

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