Analytics

Analytics delivers the value of data for the enterprise.

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Related code patterns

Fingerprinting personal data from unstructured text

Build a custom model using Watson Natural Language Understanding and Watson Knowledge Studio.


Robotic calculations and inference agent

This journey takes you through end to end flow of steps in building an interactive interface between NAO Robot, Watson Conversation API & Data Science Experience


Correlate documents

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 Data Science Experience.


Extend Watson text classification

Augment classification of text from Watson Natural Language Understanding with IBM Data Science Experience.


Detect change points in IoT sensor data

Use time series from IoT sensor data, IBM Data Science Experience, 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

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

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


QISKit SDK

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.


QISKit API

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

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.


iostash

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

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.


Merlin

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

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

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

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

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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