This course was last updated on 10/2021.
We analyzed factors such as the rating (3.3/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.
Do you want to learn how to access, process and analyze geospatial data using open source tools?
Do you want to master the fundamental concepts of a geographic information systems?
Do you want to acquire new hands on geospatial skills that will prepare you for a GIS job in the geospatial industry?
You are here because you want to learn geographic information systems with open source tools, right?
I am happy to have you here!
You may be new to GIS, or you may have a little GIS experience.
This course is perfect for anyone who wants to learn GIS from scratch to access, process, analyze, visualize and share spatial data.
What makes me qualified to teach you?
I am Dr. Alemayehu Midekisa, PhD. I am a geospatial data scientist, instructor and author. I have over 15 plus years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products.
In this Introduction to Spatial Data Analysis with QGIS course, I will help you get up and running a QGIS software. By the end of this course, you will not only master the theoretical concepts of s geographic information systems, but also most importantly equipped with a set of new GIS skills including accessing, processing, analyzing, visualizing and sharing spatial data.
One of the common problems with learning GIS is the high cost of software. In this course, I entirely use open source free software. Additionally, I will walk you through a step by step video tutorials to handle a GIS data in QGIS platform. All sample data will be provided to you as an added bonus through out the course.
Jump in right now to enroll. To get started click the enroll button.
✓ Install QGIS
✓ Import CSV and GIS data into QGIS
✓ Understand projection and coordinate systems
✓ Download and map raster data from public repository
✓ Learn to differentiate between vector and raster data
✓ Perform spatial query with tabular data
✓ Perform point pattern analysis
✓ Generate Heatmap using Kernel Density Algorithm
✓ Learn to create and share online maps using web GIS platforms
✓ Learn to perform various spatial analysis including buffer, joins, clipping and other spatial analysis
✓ Complete a final GIS project on downloading, processing, analyzing and visualizing geospatial data
• This course has no requirements.
• This course is meant for professionals in various sectors including public health, water resources, urban planning, and others that deal with spatial data.
• This course is perfect for someone who wants to apply for a GIS Analyst or GIS Specialist job position.
• Anyone who wants to learn how to access, process and analyze spatial data.
The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.
YES, Introduction to Geospatial Data Analysis with QGIS has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.
Alemayehu Midekisa has created 21 courses that got 2,115 reviews which are generally positive. Alemayehu Midekisa has taught 18,723 students and received a 4.1 average review out of 2,115 reviews. Depending on the information available, we think that Alemayehu Midekisa is an instructor that you can trust.
Lead Instructor & Geospatial Data Scientist
Dr. Alemayehu Midekisa, PhD is an applied remote sensing scientist with 15 plus years of expertise in big Earth observation data and various methods such as machine learning, time series analysis, deep learning, and cloud computing. He is proficient in different scripting languages including Python, JavaScript, R, and Google Earth Engine. He is a former NASA Earth and Space Science fellow. With global experience in USA, Europe and Africa, his research focus is in the application of multi-sensor remote sensing data utilizing Landsat, VIIRS, Sentinel 2, MODIS, GPM, and SMAP to answer complex environmental problems in land use, water resource, agriculture, and public health.
