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I am a data analyst
& visualization engineer
turning your data into
web-based data-driven
visualization projects.

     

training

geoPy

training

Postprocessing of raw input data files is a delightful step in data analysis and data format conversion for javascript web-based visualization. This modules help reading, plotting and converting all sorts of georeferenced input data formats.

Data Structures

Basic input data formats such as CSV or JSON are usually translated into Pandas format to clean and filter the data we aim for, using arrays, dictionaries and lists. The output is converted into JSON or Parent/Child JSON format.

Basic Plots

A quick overview of the data can be done with plots using the Matplotlib library through scatter plots, regression fit, time series and histograms, together with all necessary plot formatting in text, legend and labels.

GeoData
Conversion

Model output, dataproducts and observational datasets often come in georeferenced data formats, such as NetCDF, TIF, SHP or MAT files. Reading and all possible conversion among these file formats are explained. Generating an output in either JSON or GeoJSON format is also useful.

Geodata
Regrid and Mask

Georeferenced data comes in a wide variety of map projections. In this module how to regrid both NetCDF and SHP files is explained. Masking dataproducts over particular regions defined by SHP files is also covered in this section.

Cartography

Mapping largely involves representation of either discrete elements, contour plots and areas or binning via hexagons or other polygons. These operations are performed to ultimately display the data in D3.js, Leaflet, Folium or KeplerGL.

Text Analysis

Text analysis from web scrapers or social network feeds can be done with the NLTK python package for basic statistics such as word frequency or word rank.

d3.js

training

The golden standard of modern, contemporary data visualization. All that is needed to display magnitudes, time series, networks and maps is explained in this module, including transformations and interactivity.

Set Up

Basic set up to start writing code in D3.js and to see it working. This includes Observable Notebooks or setting up a local server. D3.js scripts are usually embedded into HTML code plus some CSS styles, a quick review of these basic elements is also covered.

Basic Shapes

The heart and soul of D3.js resides into drawing very basic shapes such as circles, lines or rectangles. The attributes of these shapes are subject to mathematical operations, including examples of loops, numerical operations, functions and conditional statements.

Plug Data

Plugging data into D3.js shapes bring the whole dataviz into life. This module cover three simple data structures: CSV, JSON and GoogleSheets formats, and how to plug them into our dataviz project.

Advanced Shapes

D3.js goes well beyond circles and lines to draw also polygons, arcs, lines and areas on cartesian axis, adding all possible axis of creativity.

Transformations

Conventional layouts can be modified through simple transformations of the groups of elements. This includes rotation of elements and display elements into rectangular or Circular grids.

Interactivity

New layers of information can be easily added or explored via a number of interactive elements, such as zooming, dropdown menus, sliders or tooltips, all of them through smooth transitions.

Cartography

Maps are one of the oldest data visualization tools. This section covers map projections, dots, lines, trajectories and regions on a map, tile filling of the space and hexagonal binning. These features are plotted using D3.js on native or Leaflet cartographies.

Project Development

Data visualization merges with journalism when telling a story, few clues are given regarding layout axis, layers of information, choice of background color, legends, credits and data sources.

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