Your data science team can help multiple departments, using a diverse set of tools and techniques available on the IBM Data Science Platform. In this demo, learn how data science helps scale predictive models, predicting adjuster deployment and fraudulent claims.

Learn from Gartner how data science and machine learning can deliver business results

You'll get:

  • Tips on driving successful data science projects
  • Proving ROI from machine learning
  • Ways to empower data analysts with little expertise in math or statistics
  • Powerful predictive analytics use cases


Predictive analytics

Predict with confidence what will happen next, so you can make smarter decisions for your organization. IBM is a leader in data science platforms.

Machine learning

IBM has one of the leading data science platforms, enabling you to easily collaborate across teams, use the open-source tools and scale at the speed your business requires.

Prescriptive analytics

Help organizations make better decisions by solving complex optimization problems involving trade-offs between business goals and constraints.

Leading modern data science teams

By Carlo Appugliese

While data science and AI are relatively new in the market, the concept of extracting value from data has been around for a while. But time has changed things —  and leaders are dealing with a variety of tools (open source options with Rand Python and proprietary packages, like SAS and SPSS), skills and techniques (machine learning, optimization, stats and more) and a variety of workload options dealing with large data (EDW, Hadoop and more). Leading  data science teams is no easy feat — and many have been asking questions about how they can maximize their business value today. Carlo Appugliese shares key insights from his team's experiences helping hundreds of companies enhance their data science practice, ranging from how to shorten time to impact to how to extract the best work from your team.

Carlo shares six steps to leading an effective DS team:

  1. First and most important, focus on your business objectives and problem; don’t get caught up with technology. Don’t do technology for the sake of technology.
  2. Don’t overanalyze what you’re trying to do. Some of the most successful projects are delivered by just doing it. Identify your business problem/use case, put a working team together and go.
  3. Don’t limit the data your team has available to them. Data scientists need to explore, and sometimes their approach will change based on findings. Give your scientist access to all the data.
  4. Think about how you will operationalize your project at the start. Don’t code an entire solution and then rewrite the model in a digital application. Build the solution in a technology you can operationalize.
  5. Don’t be afraid to use the latest Python frameworks. Open frameworks are community-developed and have outperformed traditional approaches. Just scan some the Kaggle winners - Python is the clear leader, so don’t be afraid of open source.
    → See the Kaggle winners (link resides outside
  6. Empower your team, so that even the most junior scientists can use open frameworks to solve your business problems. Some of my most productive team members are knocking it out of the park right out of graduate programs.
Carlo Appugliese profile photo

Get your free copy of Machine Learning For Dummies

Chapters include:

  • Putting machine learning in context
  • Implementing machine learning aproaches
  • Getting started with a strategy
  • Understanding machine learning techniques
  • Tying machine learning methods to outcomes
  • Applying machine learning to business needs
Dallas Crawford

Is data science driving value for the business?

Hear from data science, analytics and business leaders at Revelwood, QueBit and Liberty Seguros in this trailer video.

Client success

Enghouse Interactive

A large provider of call center technology uses the IBM Data Science and AI Elite team to reimagine the call center experience and gain valuable insights from customers.

Geisinger Health System

A Pennsylvania urgent care clinic turned to IBM data scientists to develop a predictive model to identify sepsis mortality biomarkers and gather the latest clinical research to help providers actively monitor and take preventive actions to improve patients’ survival.


Today’s banks use financial models to predict performance and make market judgments based on historic and financial data – but what happens if the modal is inaccurate? JPMC teamed up with IBM data scientists to make key enhancements to their risk mitigation platform with machine learning technology to help manage model risk.