Analytics delivers the value of data for the enterprise.


Related code patterns

Analyze historical shopping data with Spark and PixieDust in a Jupyter notebook

Create bar charts, line charts, scatter plots, pie charts, histograms, and maps without any coding.

Retrieve client insights for wealth management companies

Explore the Client Insight for Wealth Management service through a Jupyter Notebook and create a web application with the service.

Understand customer interests with clickstream analysis

Use Scala in a Jupyter Notebook to ingest and analyze clickstream data. The data is fed into IBM Db2 Event Store which is optimized for event-driven data processing and analytics.

Take corrective actions at the edge based on predictive analytics of IoT sensor data

Once an anomaly is detected in an IoT system or sub-system using change point detection, a failure prediction based on predictive analytics models can identify an upcoming failure condition in advance. Then, based on this detection, a proactive prescriptive action can be taken.

Build a dynamic dashboard application

This code pattern provides an Angular 5 and Node.js demo app that demonstrates IBM Cognos Dashboard Embedded, an IBM Cloud service for visualizations.

Ingest and analyze event data streams for timely insights

Visualize statistics about taxi rides while the event data is streamed from an external program.

Generate restaurant reviews using deep learning

Train a deep learning language model in a notebook using Keras and Tensorflow.

Analyze open medical datasets to gain insights

Use this code pattern as a beginning guide to run through various machine learning classifiers and compare the outputs with evaluating measures.

Predict equipment failure using IoT sensor data

Walk through a prediction methodology that utilizes multivariate IoT sensor data to predict equipment failure.

Mine insights from software development artifacts

Learn how to build an interactive text analytics solution with customization using IBM Watson Studio, Python NLTK, IBM Cloud services, Watson services, and Orient DB.

Fingerprinting personal data from unstructured text

IBM Watson Natural Language Understanding together with Watson Knowledge Studio provides an effective way to identify required information from unstructured documents. The result can be augmented with regular expressions, and personal data identified is provided a score based on which further processing or consuming can be done.

Build a robotic calculations and inference agent

This pattern takes you through end to end flow of steps in building an interactive interface between NAO Robot, Watson Assistant API and Watson Studio.

Correlate documents from different sources

Correlate content across documents using Python NLTK, Watson Natural Language Understanding (NLU) and IBM Data Science Experience (DSX)

Orchestrate data science workflows using Node-RED

Build a web interface using Node-RED to trigger an analytics workflow on IBM Watson Studio.

Extend Watson text classification

Augment classification of text from Watson Natural Language Understanding with IBM Watson Studio.

Detect change points in IoT sensor data

Use time series from IoT sensor data, IBM Watson Studio, and the R statistical computing project to analyze the data and detect change points.

Transform the retail customer experience with APIs on a mainframe

Create retail applications that leverage data from enterprise IT infrastructure using APIs in a hybrid cloud environment -- no mainframe knowledge required.

Apply machine learning to financial risk management

Use machine learning to perform secure, real-time risk assessment and management to help financial institutions more accurately determine credit worthiness.

Use Swift to interpret unstructured data from Hacker News

Learn how to pull data points -- concepts, entities, categories, keywords, sentiment, emotion, etc. -- from Hacker News articles using natural-language service calls from a Swift-based application.

Discover hidden Facebook usage insights

Enrich unstructured data from Facebook using a Jupyter Notebook with Watson Visual Recognition, Natural Language Understanding, and Tone Analyzer, then use PixieDust to explore the results and uncover hidden insights.

Implement voice controls for a serverless home automation hub

Discover how simple it is to build a home automation hub using natural-language services and OpenWhisk serverless technology.

Create a stress-test app for investment portfolios

Make the markets more predictable by building a portfolio stress-testing app using a set of financial web services.

Analyze traffic data from the city of San Francisco

Look at traffic data from the city of San Francisco, create robust data visualizations that allow users to encapsulate business logic, create charts and graphs, and quickly iterate through changes in the notebook.

Create an investment management chatbot

Create a Watson Conversation-based financial chatbot that enables you to query your investments, analyze securities, and use multiple interfaces.

Analyze Starcraft II replays with Jupyter Notebooks

Combine gaming and the power of data analysis to become an unstoppable player. Read how you can actually analyze your game to become a top-notch competitor.

Accelerate training of machine learning algorithms

Efficiently build powerful deep learning applications and improve your machine learning speeds quickly.

Correlate flight and weather data in augmented reality

Develop a modern, cloud-based air traffic control application.

Analyze tweets with Jupyter Notebooks

Analyze and create data visualizations with Jupyter Notebooks.

Related open projects

Jupyter Enterprise Gateway

A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across an Apache Spark cluster.

Application Metrics for Swift

Application Metrics for Swift™ collects and visualizes resource and performance monitoring data for Swift-based applications. Application Metrics for Swift builds on the open source polyglot data collection capabilities of omr-agentcore, which is also used in both Node Application Metrics and the Java monitoring in the Eclipse-based IBM Monitoring and Diagnostics Tools.


QISKit lets developers conduct explorations on IBM’s Quantum Experience using a Python interface. This interface enables you to work with quantum circuits and executing multiple circuits in an efficient batch of experiments. To get you started, we have provided example Jupyter Notebooks that demonstrate several standard experiments.


QISKit OPENQASM contains specifications, examples, documentation, and tools for the OPENQASM intermediate representation.


The QISKit SDK provides support for the Quantum Experience circuit generation phase and uses the QISKit API to access the Quantum Experience hardware and simulators. It includes example scripts written for Jupyter Notebooks.


The lightweight QISKit API is a thin Python wrapper around the Quantum Experience HTTP API that enables you to connect and execute OPENQASM code.

Watson Developer Cloud: Java SDK

The IBM Watson Developer Cloud (WDC) provides multiple services for developing cognitive applications, including text and language processing, image evaluation, personality insights, relationships, and tradeoff analysis.


Tosca is a lightweight preprocessor that increases a developer’s productivity when dealing with syntax-driven, source-to-source transformation.

Watson Developer Cloud: Unity SDK

The Watson Unity SDK enables developers to integrate Watson services into their Unity applications. The services in the initial release include speech to text, text to speech, dialog, translation, and natural language classification.

Apache SystemML

Apache SystemML advances machine learning through the DML language for ML algorithms and automatic optimization for efficiency and scalability.

IBM Performance Monitor

The IBM Performance Monitor makes it easy to instrument Java applications for performance tuning and maintenance.


The iostash project is our effort to speed applications by caching data that is read from host-attached magnetic disks (HDDs) or network-attached storage (SAN volumes) on solid state drives (SSDs) that are directly attached to the server.


PerfHarness is a flexible and modular Java package for performance testing of HTTP, SOAP, JMS, MQ and TCP/IP transports.

Simple Data Pipe

CRM dashboards are mission critical for every business, and with this Simple Data Pipe tool, they’ve never been easier to feed with data.

Anomaly Detection Engine for Linux Logs

Anomaly Detection Engine for Linux Logs analyzes Linux logs to help system admins and software developers understand Linux system behaviour.

Simple Metrics Collector

Simple Metrics Collector is a microservice that quickly and easily tracks click events of a single-page JavaScript app.

Watson Developer Cloud: Node SDK

The Node SDK delivers high-level access to all Watson Developer Cloud services without requiring REST expertise or new methods to authenticate to Bluemix.


The goal of the Merlin project is to develop an open, easy-to-use, extensible framework to facilitate exact and approximate algorithms for inference over probabilistic graphical models.


epanetReader takes U.S. E.P.A. EPANET hydraulic and water quality data and formats it for use in the R statistical analysis environment.

Direct Storage and Networking Interface (DiSNI)

The Direct Storage and Networking Interface (DiSNI) project is a Java framework and API for direct storage and network access from a user space.

Spark Multiuser Benchmark

The Spark Multiuser Benchmark evaluates resource manager performance for applications that are running multiuser and multitenant workloads.

Simple Search Service

The Simple Search Service is an app that can turn structured data into a faceted search engine API in a few clicks.

Node Application Metrics

Node Application Metrics provides a foundational infrastructure for collecting resource and performance monitoring data for Node.js-based applications. Node Application Metrics builds on the data collection capability that is used for the Health Center developer tool, which is part of the Eclipse-based IBM Monitoring and Diagnostics Tools.


DaRPC is an RPC framework and API for Java which uses RDMA to implement a tight integration between RPC message processing and network processing in a user space.

Brunel Visualization

Brunel Visualization is a domain specific language that defines a set of composable atomic “actions” that, when stitched together, produce an extraordinarily large variety of data visualizations.

Watson on Node-RED

Watson on Node-RED exposes IBM Watson services as Node-RED nodes, enabling developers and designers to add Watson services to their Node-RED Internet of Things models.


RBFOpt is a tool for derivative-free optimization, a mathematical technique that is used for simulation-based optimization.

Agentless System Crawler

Agentless System Crawler provides a unified cloud monitoring and analytics framework that enables deep visibility into all types of cloud platforms and runtimes, with close to zero effort or pain from the end user.

Activity Streams

Activity Streams provides developers with a standard model and JSON-based encoding format for describing how users engage with both the application and with one another. This standard format can be used at every layer within an application, from back-end data storage to driving the user experience, and frees developers from the need to invent new adhoc application-specific data formats and models for describing the kinds of actions that users can perform within the system.

IBM open source graduated to Apache Edgent

Apache Edgent is an open source development tool that makes it easier for developers to create Internet of Things (IoT) applications to analyze data on the edge of their networks.

IBM open source graduated to Apache Toree

Apache Toree acts as a gateway between an application and a Spark cluster.

Related tech talks

Stream real-time data with MQTT

July 31, 2018

Sean Dague uses MQTT to stream real-time data for the code pattern

Simplifying data architecture for real time Architecture

May 29, 2018

A discussion with the Director, Data Science & Analytics of a leading financial services corporation

Analyze industrial equipment on sight for defects


With the Watson Visual Recognition service, we can determine whether the equipment in the image meets normal conditions or by training the service to identify particular defects and damage.

Open Source Week: ODPi: Simplifying and standardizing the big data ecosystem

February 28, 2018

The Open Data Platform initiative (ODPi) aims to provide common reference specifications and test suites to simplify and standardize the big data ecosystem.

Correlate text content with Watson NLU, NLTK, and DSX to gain insights

February 20, 2018

Learn how to correlate and analyze text content across various data sources using Watson Natural Language Understanding (NLU), Python Natural Language Processing Toolkit (NLTK), and IBM Data Science Experience (DSX).

Data Science model deployment & management

February 08, 2018

Simplifying deploying and managing data science models

Building an analytics platform with Jupyter Notebooks and Apache Spark

January 24, 2018

Learn more about the design choices that make the Jupyter Notebook stack work with scalable resource managers in a secure way.

Make your NAO robot smarter

January 16, 2018

Learn how you can add intelligence to a NAO robot using IBM Data Science Experience and IBM Conversation service.

Discover Facebook hidden usage insights

January 10, 2018

Discover how to combine the power of a Jupyter Notebook, PixieDust, and IBM Watson™ cognitive services to glean useful marketing insight from a vast body of unstructured Facebook data. Learn how to improve brand perception, product performance, customer satisfaction, and audience engagement by taking data from a Facebook Analytics export.

Bridge the gap between data scientists and business users

December 13, 2017

The data scientist needs to provide a full solution view, complete with visualization at every stage, to solicit meaningful feedback from business people. This presentation discusses ways to bridge the gap between data scientists and the business community by using IBM Data Science Experience and Node-RED.

Brunel Visualization Tech Talk

May 16, 2016

Project innovators Dan Rope and Graham Wills provide an in-depth overview and demo of Brunel Visualization, a domain specific language that defines a set of composable atomic “actions” that, when stitched together, produce an extraordinarily large variety of data visualizations.

Agentless System Crawler Tech Talk

April 13, 2016

Agentless System Crawler provides a unified cloud monitoring and analytics framework to give you deep visibility into all types of cloud platforms and runtimes. In this tech talk, recorded April 13, 2016, the Agentless System Crawler project team provides an in-depth exploration of concepts and implementation details, and shows you how to crawl the cloud just like you crawl the web.

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