developing activity-based intelligence (ABI) applications

The Developing Activity-Based Intelligence (ABI) Applications training course provides the skills needed to become an ABI full-stack developer. This course teaches participants how to quickly turn ABI analysts technical needs into mission-focused software applications.

By attending Developing Activity-Based Intelligence (ABI) Applications workshop, Participants will learn to:

  • Implement entity extraction techniques using Natural Language Processing
  • Standardize and automate the creation of geotemporal and relational metadata sets
  • Apply contemporary analysis techniques to Big Data
  • Implement performant spatiotemporal search strategies

COURSE AGENDA

  • Creating, publishing and subscribing to message queues using ZeroMQ
  • Joining and posting to IRC channels using Willie
  • Performing Extract, Transform and Load (ETL) Operations
  • Finding and accessing RESTful web services
  • Web-scraping and automated page interaction using selenium
  • Geospatial data in shapefile and KML file formats using pyshp and fastkml
  • Structured and unstructured text in Microsoft Office and CSV file formats using comtypes and PDFMiner
  • Principles of effective tactical data science
  • Role of the data science with the intelligence production cycle
  • Core fundamentals of Python
  • Coordinate format conversions between DD, DMS and MGRS using geotrans
  • Geometry type conversions between point, line, and polygon using Shapely
  • Spatial metadata extraction using OpenSextant
  • Text decomposition and named entity recognition (NER) using elasticsearch
  • Mosaics and raster format conversion using GDAL
  • EXIF metadata extraction using exifread
  • CRUD operations with SQLite
  • Applying full-text indexing using FTS3
  • Implementing geospatial indexing using R*Trees
  • CRUD operations with MongoDB and Berkeley DB
  • Geospatial and text indexes in MongoDB
  • Linear regression techniques using NumPy
  • Time series analysis techniques using pandas
  • K-means clustering techniques using Scikit
  • Distance-based buffering and filtering using Shapely
  • Kernel density estimation using Scikit
  • Shortest and least-cost path analysis using NetworkX
  • Betweenness and closeness centrality using NetworkX
  • Display vector and raster representations using shapefiles and KMLs
  • Creating pivot tables using Microsoft Excel
  • Generating plots and charts using matplotlib
  • Deploying ABI Solutions
  • Building a Windows stand-alone tool
  • Developing a Python plug-in for QGIS
  • Creating a simple web interface using SimpleHTTPServer
  • Implementing a RESTful web service using bottlepy

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