Microsoft Excel Power Pivot Power View 3D Maps Data Mining add-Ins for Excel SharePoint 2013 Power BI Q & A Power BI Apps for Mobile Devices Groups Data Catalog Excel Workbooks Power BI Desktop Microsoft Excel Microsoft Excel enables you to collect and analyze data, and to develop reports. Excel Services enables you to render a workbook in a browser window and refresh the associated data connection. Others have access to the workbook or specific parts of it without being able to modify the workbook or calculations, thereby ensuring a single version of the workbook is maintained and viewed by everyone. With an Excel Services workbook you can also perform live, interactive analysis through any supported browser, including sorting and filtering of data, as well as expanding or collapsing of PivotTables. Excel 2016 Excel 2016 for Windows has all the functionality and features you're used to with some added features and enhancements. Several of the top new and improved features include the following. New chart types to help you create some of the most commonly used data visualizations of financial or hierarchal information, or for revealing statistical properties in your data. The new chart types include Treemap, Sunburst, Waterfall, Histogram, Pareto, and Box and Whisker. Built-in functionality that makes it easy to quickly get and transform your data. These new capabilities, previously only available as a separate add-in called Power Query, can be found natively within Excel. The FORECAST function has been extended to forecasting based on Exponential Smoothing. This functionality is also available as a new one-click Forecast Sheet button.
In Machine Learning, you work with data in the form of datasets, which are tables of data with accompanying metadata that describes the schema and analytical usage of each column. The modules provide many tools that are important and useful in data science, including a variety of leading-edge machine learning algorithms. Other modules provide data entry functions, tools for transforming data for statistical analysis, and support for machine learning workflows such as training, scoring, and validation processes. The following image shows a typical experiment with multiple modules. One module is used to choose a subset of columns (), another creates a linear regression model to predict a numeric outcome (), and successive modules train the model () and generate predictions using a trained regression or classification model (). Image based on “” article For more information about Microsoft Azure Learning, see these articles. () For tutorials, templates, and pre-developed solutions and walkthroughs for a variety of scenarios, see the following. Azure Machine Learning Studio includes many sample datasets that have been pre-configured to make analysis easier. () Stream Analytics Azure Stream Analytics provides complex event processing over streaming data in the cloud. The processing is low-latency, highly available with built-in recovery and checkpointing capabilities, and scalable. You can combine streams of data with historic records or reference data to derive business insights. The key scenarios are: Perform complex event processing on high-volume and high-velocity data Collect event data from globally distributed assets or equipment, such as connected cars or utility grids Process telemetry data for near real-time monitoring and diagnostics Capture and archive real-time events for future processing