It's no secret that jobs in data science are in high demand right now. The possibilities expand much further when the equation includes the geospatial analysis. One must thoroughly understand geographical data to effectively address several significant concerns, including food production, climate change, and the transition to a carbon-free economy. We can generate significant change and obtain deeper insights by merging vector information, field measurements, and satellite and drone photos.
How to Learn Geospatial Data Science
To get into the field of geospatial data science, you must learn the following:
Basics of GEE and GIS
It's time to start getting your hands dirty with some GIS concepts now that you have a basic understanding of Python and its primary libraries for data processing. For detailed information on Python and its popular libraries, you can refer to the Data science course in Delhi. In a typical GIS course, the facts regarding how satellite data is gathered, the path of light through the atmosphere, and how it impacts sensor readings may be flung at you. Although these topics are crucial for study, they might not be required for a hands-on introduction to geographic data analysis.
Google Earth Engine (GEE)
Although Google Earth Engine (GEE) is strong and offers a wealth of data ready for use, it also has significant drawbacks. The Google cloud must be used for everything. Although it offers free use of its resources, expenses may be involved, especially when processing data on a big scale. The best option is to work with any data from any source using open Python modules.
Basic Python programming
That subject is crucial. Without it, learning about vector or raster datasets, for example, is useless because you won't have the skills to examine them, and everything will be abstract. I'm aware that QGIS and other GIS programs are available. But, it differs from simply viewing the raw data in a geojson file or a numpy array and analyzing it on your own. Also, starting with an understanding of Python programming can lead to new opportunities you have yet to consider.
Advanced Python Programming
You've been conducting your tests in Jupyter notebooks up to this point. Although it's a fantastic data exploration and analysis tool, you should consider using something other than it to develop a functional application. Also, developing a notebook that conducts a thorough analysis and writing fully functional software that will be installed on a server are two very different things.
Other Advanced Subjects (Sky's the limit)
As you go along, you'll quickly learn that many geospatial analysis tasks are easily scalable, depending on the quantity of data and processing power available. To scale up, you'll require some understanding of how to create a cloud server with larger resources and how to use distributed computing.
Conclusion
In conclusion, one of the most fascinating and significant topics of our time is geospatial data science. We can influence significant changes in areas like climate change, food production, and public health by fusing the strength of Python with the enormous volumes of geographical data available.