Geometries are the glues that hold together geospatial data. They form an integral part of any spatial data processing. In this tutorial, I will go through some of the different types of geometries available in Postgis. We also touch on some of the most used functions with real-world data examples. In my last article, I explained how to install PostgreSQL and activate Postgis extensions.

Recognizing users' strong interest in the emerging field of spatial data science, Esri is adding a new course--Spatial Data Science: The New Frontier in Analytics--to its popular lineup of massive open online courses (MOOCs). Opening in 2020, the course will explore how incorporating spatial data, tools, and methods enhances analytical and predictive models. Data scientists, GIS analysts, and others with a strong background in statistics and analytics will find the course beneficial. Attendees should plan to spend three to four hours per week on the course. Esri will award a certificate of completion to everyone who completes the MOOC.

About this course: Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week.

I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this growing sphere exciting, but did not have any contextual experience working with these technologies. I am embarking on a two year journey to explore this field. If you haven't read it already, you can see Part 1 here: From webdev to computer vision and geo. I ended up getting myself moving by exploring any opportunity I had to excite myself with learning.