pixiedust-facebook-analysis - A Jupyter notebook that uses the Watson Visual Recognition, Natural Language Understanding and Tone Analyzer services to enrich Facebook Analytics and uses PixieDust to explore and visualize the results

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In this Code Pattern, we will use a Jupyter notebook to glean insights from a vast body of unstructured data. Credit goes to Anna Quincy and Tyler Andersen for providing the initial notebook design. We'll start with data exported from Facebook Analytics. We'll enrich the data with Watson’s Natural Language Understanding (NLU), Tone Analyzer and Visual Recognition.

watson-visual-recognition watson-natural-language watson-tone-analyzer jupyter-notebook bluemix data-science ibmcode ibm-developer-technology-cognitive pixie-dust pixiedust notebook

pixiedust-traffic-analysis - A Jupyter notebook using PixieDust and PixieApps to visualize San Francisco traffic accidents

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In this Code Pattern we will use PixieDust running on IBM Data Science Experience (DSX) to analyze traffic data from the City of San Francisco. DSX is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. The intended audience for this Code Pattern is application developers and other stakeholders who wish to utilize the power of Data Science quickly and effectively.

notebook data-science pixiedust dsx ibm spark journey jupyter-notebook ibmcode ibm-developer-technology-cognitive bluemix

visualize-food-insecurity - Use Watson Analytics and Pixie Dust to visualize US Food Insecurity

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This Code Pattern will guide you through downloading, cleaning and visualizing data using different tools. In particular this Code Pattern showcases food insecurity in the US, along with its associated factors. Often in data science we do a great deal of work to glean insights that have an impact on society or a subset of it and yet, often, we end up not communicating our findings or communicating them ineffectively to non data science audiences. That's where visualizations become the most powerful. By visualizing our insights and predictions, we, as data scientists and data lovers, can make a real impact and educate those around us that might not have had the same opportunity to work on a project of the same subject. By visualizing our findings and those insights that have the most power to do social good, we can bring awareness and maybe even change. This Code Pattern walks you through how to do just that, with IBM's Data Science Experience (DSX), Pandas, Pixie Dust and Watson Analytics.

ibmcode watson-analytics pixiedust data-science ibmdsx

predict-opioid-prescribers - A pattern focusing on how to use scikit learn and python in Watson Studio to predict opioid prescribers based off of a 2014 kaggle dataset

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Read this in other languages: 日本語. Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work.

datascience machinelearning python3 scikitlearn-machine-learning pandas pixiedust dsx ibmcode call-for-code

analyze-customer-data-spark-pixiedust - Introductory IBM Code pattern for PixieDust

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In this code pattern historical shopping data is analyzed with Spark and PixieDust. The data is loaded, cleaned and then analyzed by creating various charts and maps. The intended audience is anyone interested in quickly analyzing data in a Jupyter notebook.

pixiedust visualization spark