Milan's Data Science Insights #011

Hi Everyone,

After a few extra issues, I am returning to the regular style. The format it still shaping, but one thing is sure - you can expect loads of geospatial goodies this year as well! Let's jump in.

1. Courses

I am super delighted to let you know that my second official LinkedIn for Learning course is out - Geospatial Raster Data Analytics in Python with the following learning goals:

• Accurately describe the characteristics of raster data and differentiate it from other geospatial data types.
• Acquire, read, and visualize raster data from various sources, including population and elevation data, using appropriate software and tools.
• Perform basic data exploration techniques, including histogram analysis and log scaling, and interpret the results to enhance data visualization.
• Manipulate raster data by successfully performing operations on data sets such as clipping, resampling, and reprojecting.
• Combine multiple single-band raster files into a multiband raster file, conduct analytical exercises on the resulting data, and interpret the outcomes.

This course builds on my previous course, so make sure to check that as well - it is called Geospatial Data Analytics Essential Training.

2. Top Posts

In case you are not a regular on LinkedIn, I will share the highlights of my week here as well.

• A huge collection of time series analytics tools by Maryam Miradi, PhD
• A coding video sneak peak to my bran new raster analytics course
• Once you mastered raster data, I recommend to check this remote sensing analytics overview by Ulrik Martensson
• I also collected 25 of my Python tutorials on various geospatial data science topics.
• While I am certainly voting for Python, this collection of QGIS plugins by Mashford Mahute is a must for everyone using that software
• Speaking of Python - this colletction, aptly titled 'Life is short, Iuse Python' by Alex Wang certainly has a point and compiles a great list of must-have tools for many different data science subdomains.

3. Article Digest

As you might have seen, I called myself out to the #365Papers challenge, meaning I will follow a great advice I got as a PhD student many years ago: make sure to read at least one paper per day. Now I flip this around a bit and share one paper per day here on LinkedIn, so you can easily follow this learning journey.

• ????????????: ???? ?????????? ?????????????????????? ?????????? ?????? ?????? ??????????????????????, ????????????, ?????? ???????????????????? by G Astruc, N Gonthier, C Mallet and L Landrieu: “ultimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner.”

• A large-scale assessment of how close global cities are to the magic 15-min: ?? ?????????????????? ?????????????????? ?????? ?????????????????? 15-???????????? ???????????? by Bruno et. al via @Nature Portfolio
• Object detection in urban settings is getting really hot. I just came across this nice tutorial on Towards Data Science by M. Aboutaleb ???????????????? ???? ???????? ???????????????????? ???????????????? ???????? ?? ???? ??
• A comprehensive overview on spatial indexing by Ben Feifke: "???????????????????? ???????????????? ??????????????????: ?? ???????????????????? ???? ??????????????, ??2, ?????? ??3" on Towards Data Science
• The paper of the day is on mobility data analytics - ???????????? ???????????????????? ?????? ???????????????????? ???????????????????? ???? ?????????????? ??????-????-?????????????? ???????????????? by Sebastiano Bontorin et al.

• Psychology and geospatial data: ???????????? ????????????????-?????????? ???????????? ???????????? ?????????????????????? ???? ???????? ?????? ?????????? ?????????????? ???????????? ??????????
• A colorful article by Robert Simmon titled ???????? ?????????? ???? ?????????? ???? ???????? ???????????????????? shows how data journalists use remote sensing data, from wildfires, deforestation, hurricanes, to battlefields.

All the best,

Milan