In the post titled Access, Download, Process and VIsualize sea surface height and geostrophic current from AVISO in R posted in my blog on Monday, Apr 15, 2019, I explained how we can download the satellite data like sea surface height from AVISO in R. I illustrate in detail getting the data using xtractomatic package (Mendelssohn 2018). Though xtractomatic package provide functions that allows us to get access to the ERDDAP server and get the data, but one big challenge is that the data comes is array and need an expensive computation process, especially if you deal with gridded data for a long term time series.
As a data scientist, you need to distinguish between regression predictive models and classification predictive models. Clear understanding of these models helps to choose the best one for a specific use case. In a nutshell, regression predictive models andclassification predictive models` fall under supervised machine learning. The main difference between them is that the output variable—in regression is numerical (or continuous) while that for classification is categorical (or discrete).
Often times when w are working with data, there always a geospatial component to the data—the locations. Most of us have used static maps to reveal information that other plots can not. And interactive maps can enliven geographic information to new insights. The most important type of interactivity, is the display of geographic data on interactive or ‘slippy’ web maps. Interactivity can take many forms, the most common and useful of which is the ability to pan around and zoom into any part of a geographic dataset overlaid on a ‘web map’ to show context.
Forecast time series introduction Time series analysis comprises methods for predicting the future based on the historical in order to extract meaningful statistics and other characteristics of the data. In other words, time series forecasting is the use of a model to predict future values based on previously observed values. Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post.
Introduction Network Common Data Form (NetCDF) is a widely used format for storing array–based data as variables. NetCDF are developed and maintained by Unidata was originally developed for storing and distributiing climate data , such as those generatd by climate simulation or reanalysis models. It has also been adopted in other fields, particularly in oceanography, where large mutidimensional arrays of data are generatted from satellite observation systems. The NetCDF format is a platform-independent because can be transeerred among servers and coputers that are running different operating systems, without a need to convert the file that fit a particular sytem.
