Center for Geospatial Information Science Winter Skill-Set Courses
·
GEOG788F: Big data analytics with
Python (1 credit; register here)
·
GEOG788E: Web GIS (2
credits, register here)
·
GEOG788C: GIS and Hadoop (1
credit; register here)
The skill-set course sequence at the UMD Center for Geospatial Information
Science is designed to provide a
comprehensive overview of current state-of-the-art in geospatial information science (GIS) and computational social
science (CSS), through intensive training over a short time frame. The skill-set courses provide
advanced training in key aspects of GIS and CSS, alongside hands- on lab-based exercises across a
diversity of devices, software libraries, exercises, and data environments that emphasize “learning by doing”.
Each course is taught by faculty and staff in the School of Geographical
Sciences and Center for Geospatial
Information Science at the University of Maryland, with extensive experience in building and applying geographic
information systems for use in research and professional
environments.
Details are available at: http://geospatial.umd.edu/education/landingtopic/winter-workshops
Big data analytics with Python (1 credit)
Overview:
This course is designed to provide an introduction to statistical analysis
over big data sets (and tackling big data problems),
primarily in geography and spatial sciences, but with broader appeal throughout the socio-behavioral
sciences. Students will be introduced to a range of methods that can be applied to the exploration,
modeling, and visualization of big quantitative data. The course covers a range of topics including:
core aspects of data analysis in Python, as well as packages for focused data analytics, web scraping,
data visualization, and modeling.
Upon a successful completion of the course the students will be able to:
·
Understand basic concepts and
packages of Python relating to data analysis;
·
Demonstrate proficiency in
using Python, iPython Notebook, Numpy, SciPy, Matplotlib, etc.;
·
Demonstrate basic technical proficiency in the use of the Python for collecting and scraping
web data, particularly as produced on the Twitter messaging platform;
·
Demonstrate proficiency in
modeling, predicting, analyzing, and plotting big data;
·
Solve real-world problems
using big data
Suggested background:
This course assumes
a basic knowledge of GIS concepts
and capabilities, and an intermediate programing experience is
expected. The target audience is students who deal with large datasets across disciplines and need to
build proficiency in manipulating big data.
Hands-on training:
·
Anaconda and demo packages
·
Demos on ipython and
ipcluster (for parallel computing)
·
Draw statistics charts with
scipy, numpy, matplotlib
·
Shapefile access / write
using Fiona and Shapely (and matplotlib)
·
Introduction to Twitter API
(entities, streaming, search)
·
Twitter crawler using Tweepy
(streaming and search)
Web GIS
(2 credits)
Overview:
This course is designed to explore web-based GIS technologies, and to help
students develop the knowledge and skills necessary
to plan, design,
develop and publish
a web-based GIS solution.
This course provides students with a comprehensive and up-to-date understanding
of: 1) the concepts, theories,
and development trend of Web/Internet GIS and its prevalent applications in multidisciplinary fields; 2) various technologies and
techniques for creating, analyzing, and
publishing GIS data and services
via the Internet. Students will be taught state-of-art technical skills and
knowledge necessary to develop Web GIS applications and to manage Web GIS
projects, in the context
of real-world applications of Web GIS in various
fields. Students will gain exposure
to almost all of the main Web GIS tools including ArcGIS Server, ESRI JavaScript API, Google Map
Suggested background:
The target audience
is students who wish to develop proficiency in developing Web GIS products. Students taking the course must
be familiar with geographic data structures, basic GIS concepts, and demonstrate basic understanding of
object-oriented programming in a GIS environment.
Hands-on training:
·
Create map applications using
ArcGIS Online;
·
Create a web app with custom
symbols and popups using ArcGIS Online;
·
Publish a map service on
ArcGIS Server and create a web app with it;
·
Create web apps using ArcGIS
Web App Builder;
·
Create web apps using ArcGIS
API for Javascript;
·
Publish geoprocessing
services and spatial analytics online;
·
Create and design web apps
using Google Map API;
·
Create and design web apps
using Leaflet, CartoDB, and Mapbox
GIS and Hadoop (1 credit)
Overview:
In a world of unstructured data, one of the few common structuring attributes is geography. This places geographic information and spatial
data models front and center
in the ongoing development of
data management and access resources for big-data silos and the systems that
rely on them. This course will focus on training students on the latest
knowledge and techniques for processing spatial information embedded
in big data. Students will work with the Apache Hadoop framework, which is evolving
as one of the standard
architectures for big data systems
used in research
and industry. The course will introduce basic concepts and structures of high-performance computing
environments atop Apache Hadoop, with detailed instructions on deploying such an environment. Students in this course will develop
proficiency in Open sources toolkits, such as GIS Tools for Hadoop, and will be introduced to the various
pathways available to leverage the Hadoop framework to conduct spatial and related analyses on big data.
Upon a successful completion
of the course the students will be able to:
·
Design a solution and architecture on a Hadoop
platform for a spatial analysis of big data on a cluster of machines;
·
Apply MapReduce extensions, such as
SpatialHadoop, to work with spatial data;
·
Automate geospatial big data processing using
Hive and GIS Tools for Hadoop;
·
Run spatial operations/analysis on billions of
spatial data records inside Hadoop;
·
Visualize analysis results of big data via
cartographic and geovisual media.
Suggested background:
The prerequisites for this course include
an introductory course
for Geographic Information Systems (GIS). Students taking the course must be familiar with
geographic data structures,
basic GIS concepts,
and demonstrate basic understanding of geospatial analysis.
Hands-on training:
·
Install and configure Virtual Machine with Ubuntu;
·
Install and configure Hadoop with
Pseudo-Distributed Mode;
·
Demo word count example to introduce basic
Hadoop mechanism;
·
Install and configure Hive on current Hadoop,
install postgre if needed;
·
Compare performance of SQL querying and
aggregation with postgre and Hive;
·
Install Hadoop with Fully-Distributed mode on
AWS via Cloudera.
