career – Nithin's Blog

GIS, Excel and More

It’s been a while since I posted about what I’ve learned or worked on. Here are some things I’ve done since March:

Spatial Data Science: GIS, GeoPandas

A few months ago, I wrote about how I wanted to learn Spatial Data Science. Specifically, I wanted to learn GIS, GeoPandas, GeoJson and PySAL.

Shortly after that, I became the task lead on a project involving a data file at work. The task heavily involved GIS and GeoPandas, and I ended up learning many of the topics that I outlined on the blog post. The only exceptions were network analyses – which I didn’t cover – and buffer analyses and ArcOnline maps with comments – which I got help with. This project was quite expansive – so there’s a lot more to write about in future blog posts.

Beyond the technical analysis, I also created a slide deck (which involved lots of analyses of ridership data) that I presented to our group’s leadership.

I’ve also become the de facto project manager on this task, so I’m picking up some project management and process management skills.

New interest unlocked: Spatial Data Science

A cool map of Montreal, QC, Canada. “Lovell’s map of the city of Montreal : including Westmount, Outremount, Verdun, Montreal West and St. Laurent” by Norman B. Leventhal Map Center at the BPL is licensed under CC BY 2.0.

A few months ago I attended the Spatial Data Science Conference in New York City, hosted by Carto.

Spatial Data Science is a field that deals with spatial data – that is, data points that have a geography associated with them. For example, a database of sales by ZIP code would be spatial data. In the planning world, spatial data tends to show up in conjunction with GIS (Geographic Information Systems), the most prevalent of which is ArcGIS (and its open-source equivalent, QGIS).

In college, I worked for a semester at the maps library at the University of Michigan. I like maps, and spatial data, so when I found out about this conference I was excited to see how spatial data was used in industry.

I will have to type up my notes from the conference in a different post, but I’m writing this post because I want to learn more about spatial data science. I’m ~manifesting~ this by writing about it, hoping that it will keep me accountable. Back in 2023, I wrote about GIS being one of the three technical skills I wanted to learn.

Here are some things that interest me about spatial data science, that I would like to learn. I don’t have a plan or goal associated with this yet, because I want to make sure that anything I commit to is realistic (which I haven’t done the greatest job of doing in the past…oops).

Things I want to learn about Spatial Data

Breaking down my technical skills goals – Full-stack

Me hacking the mainframe after learning all these technical skills. Source: “Hacker Stock Photo” by devdsp is licensed under CC BY 2.0.

In this post, I talked about the three technical skills I wanted to learn: SQL, Full-stack, and QGIS. In this post, I talked about some things I’ve accomplished. Today, I’m going to break down my full-stack learning goals to hold me accountable.

Course Breakdown and Pacing

This course has 13 parts. Each part is supposed to take around 15-20 hours, or about a week. I’m working part-time until June 9th, so until then I’ll plan on doing one part a week, with a week buffer.

After June 9th, I’ll plan on doing 1.5 parts per week, since I won’t be working and will have more free time.

For Part 1 – 4, I’ll consider each section complete (and will not move onto the next section) after I’ve completed all of the non-starred exercises, as per the course instructions. For the remainder of the sections, I’ll consider a section done when I’ve done 75% of the exercises.

Based on that pacing, here’s what I should accomplish each week:

Technical skill learning update: switching to projects

A few weeks ago, I wrote about how I wanted to learn three new technical skills this year. The reason I want to learn these skills is that I’ve identified roles that interest me that require these skills.

In this article, I’ll share my progress on these goals, the obstacles or I’ve faced, and some solutions I propose to address them.

Three technical skills I want to learn in 2023

Here are some skills I want to learn in 2023, based on the informational interviews I’ve done so far. I’ve also included resources for each of them.

SQL

What it is: programming language used to query data from relational databases

You should learn it if: you want a role that deals with data (business analyst, data analyst), or a role in which data-driven decision making is important (product analyst, strategy/operations, many consulting roles)

How to learn it: The Mode SQL tutorial came highly recommended by someone who works in data analysis.

Full-stack development

What it is: Full stack development refers to the skills needed to build modern web apps.

How to learn it: A free resource is https://fullstackopen.com/en/.

GIS

GIS refers to geographic information systems, a tool used in urban planning and environmental roles to work with spatial data.

The contacts I spoke to in the urban planning world made it seem like GIS is a good way to get an entry level role in the field, which you can use to advance within ~1-2 years.

The industry standard GIS tool is ArcGIS, but its windows only and is paid. There’s an open-source alternative (QGIS), and their tutorial is a good way to get started.

Pre-mortems: A solution to the planning fallacy

In the process of trying to plan better, I realize that I end up underestimating the amount of time that it takes me to do something, and overestimating what I can get done in a given period of time.

From talking to friends and colleagues, this appears to be a common observation, including by people who we might consider as successful.

The name for this is Hoftstadter’s law, or the planning fallacy. We tend to consistently blow past not only our best case scenarios for how long things take, but also our worst case scenarios.

What’s the solution? One option is to try a ‘premortem’. Assume things have already gone wrong and you missed your deadline. Ask yourself what happened and what went wrong, and then plan accordingly.