0:00 when I started doing spatial analytics full time I really struggled to build out my skills and toolkit it took me three years to land my first job at cardo and about four years after that I actually transitioned to a full-time technical role during that time I tried lots of different ways to build my technical skills some of those worked and some of those didn't I spent tons of time reading medium articles taking courses on udemy and tapping on colleague's shoulders to ask annoying questions on how to do a really simple task so I asked myself if I had to completely start over by learning technical or modern GIS what steps would I take to get there as fast as possible so the question is can you move into modern GIS or spatial analytics faster 100 this is exactly what this video is about removing all the fluff and
0:45 focusing on the key steps that you need to take to learn technical geospatial analytics I get multiple messages on LinkedIn asking about my exact process or tips to do this so I figured why not make a video about it stay tuned for the whole video because I'm going to share my top three mistakes that I myself and I see others making when learning more technical or modern GIS so the first thing you need to do is pick your tool set what tools should I use and in which order should I learn them you're going to need to have a combination of tools together some things to visualize data other places to store your data and then some languages to analyze that data so what's the first thing to get started with my recommendation is getting started with qgis this is still the best way to get started it's free to download and anyone can use it plus there's an amazing community of support around this tool is this still the tool that I use
1:30 the most today no would I change anything about the order I learned this in no way you can do everything from analyzing spatial relationships reading and analyzing raster files even up to simple spatial statistical models within qgis and if you can't find something that you need to do I almost guarantee there's a plugin to do this if you're coming from a more traditional GIS set of tools this is a great place to start it'll feel super familiar and it's very easy to use there are of course some limitations with qgis you can't analyze super large data sets otherwise you might end up with a spinning wheel of death like you see here once you've learned this what's the next thing that you need to learn so once you've learned qgis it's time to move on to your next tool now you're going to need a programming language to do more
2:16 spatial analytics and analyze larger data set previously I said that python was the best way to get started with this and I'm actually going back and changing that recommendation a little bit the next tool that I would learn is spatial SQL so I SQL over python the answer is really simple the faster your ability to query change and manipulate data the faster your career will move as a spatial analyst for me this is one of the things that catapulted my career forward and so that's why I'm recommending it here because I feel like as a next logical step this is the way to go spatial SQL is highly popular and really in demand right now especially for lots of different roles and can be helpful across the board but spatial SQL is really hard to learn even in traditional settings I had to learn this by picking up random tutorials asking colleagues and kind of figuring it out as I go along but like I said one of the most valuable skills that I've learned
3:02 to date and I say that for two different reasons the first is that these functional data skills that you're going to build in SQL are highly transferable inside and outside of Geo the second is that this really just helps you scale up what you're already doing this is also going to help you build really practical data skills doing things like ETL and transforming your data and it's also going to help you think about data in a new way programmatically there's also a pretty low barrier to entry SQL is pretty easy to learn once you have a few foundational tools to do so and you can use it as a stepping stone to different data types and other languages I've been building a series all about spatial SQL you can check that out in this playlist here the best part is you can get started at no cost you can download post GIS connect it to qgis and you're up and running in a few minutes now of course you can analyze data to the blue in the face ultimately you're going to have to
3:48 create a map and visualize your data so what should your visualization toolkit look like so there's no right answer here and there's no clear consensus on what tools you should use to visualize your data a lot of times you're going to see in job postings people asking for skills in Tableau or power bi but business intelligence tools just aren't suited for geospatial data they can't handle the volume of data and once you get it above a certain number of features they're going to crash so what are the tools that you should learn if you're focusing specifically on geospatial data well the first again is qgis qgis lets you visualize your data within the application itself you can create map exports that are static or print versions and you can even create some lightweight interactive maps as well it's completely free and open source and if you're a GIS team this is going to be a really great way to get started and if you're thinking about Enterprise GIS if you're all connecting
4:34 to the same database you can all share data back and forth and use that as your core data store for your team that said there are some limitations it's hard to share maps and data back and forth you have to transfer your file back from one to another so sharing becomes a little bit more tedious when you're using qgis the other tool I really like is called Kepler GL this uses deck GL as its rendering Library which is really great for the modern web it has lots of comprehensive data visualization methods and even has some different controls to filter your data and then ultimately share and publish maps all that said you do have to self-publish your own maps and you also can't connect a data source like a database or something like that so you're going to have to take your data out and put it back into Kepler so those are the core limitations there the other options using cardo Now spoiler alert I do work for cardo so I am a
5:20 little bit biased here but I do feel like it offers some great options compared to qgis and Kepler Carter lets you connect any data source whether that be a database or data warehouse seamlessly and you can use that to build Maps visualize them and share them it also adds some different tools into the database or data warehouse in its analytics toolbox to make things like spatial statistics routing geocoding and creating map tiles even easier you can create really complex dashboards with writing SQL or without there are some limitations cardo is completely cloud-based and does have a cost associated if you do want to get started you can get started for free with a trial or if you're a student you get free access to the GitHub student developer pack there's no right answer here but in terms of how you might start with these tools I recommend starting with qgis to do your base visualization in analytics
6:05 and then when you need to create an interactive map moving that into Kepler and taking your data out of qgis and putting it into Kepler once you get more proficient in SQL and you're ready to do some more complex visualizations or scale up with larger data I would take a look at cardo because that becomes a logical time to use a more scalable tool so we have to add one more tool to our toolkit and you might know what I'm going to say but it's python when you want to get into more advanced analytics you really need to add another programming language to do this and python is the best choice to do so my first programming language was actually JavaScript and while this taught me a lot and I had to struggle to learn it I wouldn't recommend that as your analytical programming language python is going to give you a great base to work off of and scale your skills well beyond this language itself I also
6:50 get this question all the time should I learn r or should I learn python ultimately I would recommend python R is really used in academic circles and it's a really great toolkit to get started but python not only as an analytical language helps you do things like process data create data engineering pipelines run in data science notebooks create machine learning models Even build back-end apis it's extremely versatile so once you learn it you can apply to much more things Beyond just spatial analytics python is super easy to get started there's a lot of courses to learn but you can take a look at this video which has my recommendations on how to get started and Learn geospatial Python so now that we know what to learn we have to figure out how to learn it and this is the first mistake that I see a lot of people make people try to learn everything that they can possibly learn with all these different languages and
7:36 tools and this isn't the approach I would recommend you want to become just dangerous enough to use these different skills in practical ways and then over time as you pick your focus areas you can really start to go deep if that's in spatial data science with python or in data engineering or SQL you can really figure out where your interests lie the other problem with this is that people spend a lot of time watching tutorials and I mean really watching them the number one thing that you can do is actually practice writing code even if this is a simple hello world statement or building your skills over time if you're practicing writing code you're building your skills courses are really great and I've learned a lot from them over time but there's a lot of great free tools out there that help you get started and actually have practical exercise to do this I shared this earlier in my videos for geospatial Python and the first video on my spatial
8:21 SQL course as well the other thing you need to know is analyzing geospatial data in practice is way different than watching it so getting your hands dirty hitting some walls and trying to figure out problems is the best way to go knowing how and what a spatial joint is is way different when you have to join a couple million points to a couple hundred thousand polygons what's the best way to practice there's a few things that I can recommend the first is to design your own challenges you can actually think of here's a problem I want to solve and how would I solve that with python or SQL and decide how you want to go from there there's so many great open data sets to test this out basically every city or country in the world has an open data portal so you can go and grab some data and start solving different problems that way you can also look at different data sources like Google open data as well as there's lots of geospatial data sets on things like kaggle and other places too another
9:07 great way I recommend is looking on medium and trying to find projects that you like and find interesting and replicate them or add a different spatial angle to them find three maybe four projects that you really find interesting and take your own spin at them from a geospatial perspective I'd also recommend investing maybe in a few different training tools that are specifically focused on helping you practice there's a few that I really like strata scratch which is built by data scientists and actually puts you into programming challenges for SQL as well as python if you're focusing purely on SQL learnsql.com is another awesome resource that I really like there's lots of tutorials you can take everything or just the bits that you want to focus on the last one is data by Danny if you're going deep deep into SQL this is the best route to go there's an eight week sequel challenge that he has as a complete course that really focuses on deep and intense topics you can do all
9:54 of this totally for free with no cost but if you're going to invest in a tool I would definitely invest in one that gives you actual problems to work on and train the second biggest problem I see people make is feeling like they have to figure it all out by themselves guess what you don't in analytics or geospatial analytics something is inevitably going to go wrong no God please no no no no and you're gonna have to figure out how to fix it this is where you're gonna spend a ton of your time working in technology no matter where you are you're inevitably going to end up on stack Overflow at some point trying to figure out how to do something my other advice is to learn how to read error codes and learn how to read documentation error codes can be super annoying but a lot of the times if you can read them and understand what the problem is that might give you a clue is where two the problem might be and also reading documentation to see what needs
10:39 to go into a function and what comes out of it is going to be a valuable skill as you start to get deeper and deeper into these tools so now we've picked our tools we've learned them and now it's time to find a job and this is actually where I see people make the third most common mistake and that's not being proactive what do I mean by being proactive it's actually three things first building your portfolio second creating and building out your LinkedIn profile and third reaching out to people that you might want to work with so the first step is building out a portfolio why is this so important building a portfolio shows that you first of all know how to do the work but also that you've actually had some practical implications for this so how do you start building portfolio projects if you can actually do this in your current job great that's a great place to start and actually apply your skills and build some different projects if not try to
11:26 find some projects or some passion projects that you want to work on no matter what the project you do make sure it's Unique to you and you're passionate about it that's going to shine through no matter what that is if you're really passionate about the outdoors do something with national parks data and try to figure out which Parks have the most visitors are you passionate about cities Great go find some open data and build a project there another tip is you can even reach out to non-profits or businesses figure out if they have a geospatial problem and actually help solve it for them you can start this for free or turn into a freelancing side hustle as well I'll go into portfolio projects in a future video in Far more detail but just getting started is the most important piece now where should you put your portfolio frankly anywhere building a simple website on WordPress or even a simple HTML page is one way to start make sure you get your work on GitHub if you're hosting code this is a
12:13 great place to go and also check out spatial node which is a portfolio tool just for the geospatial community when you present your portfolio projects they should have three things they should be short and to the point they don't need to go into pages and you don't need to have a 50 page report about them all that said in the second point they should have some detail in it talk about what you did how you did it and what were the outcomes that's actually the third Point what are the outcomes and what did you solve did you find a new trend did you uncover something in the data Focus your point on that outcomes are the focus of all geospatial analytics so make sure you put that front and center now it's time to move in and optimize your LinkedIn how do you do this there's great resources on how to optimize your LinkedIn to get a job I actually really like this video that tells you a lot more about creating a really effective LinkedIn profile using keywords and adjusting key components
12:59 your profile to help you find a great job that's going to be a good fit for you in general try to think of LinkedIn as a search engine if you want to become a geospatial analyst put that in your headline in different parts of your profile you want to be a geospatial data engineer great focus on that and add the relevant skills now in a perfect world you build all this and recruiters start reaching out to you but unfortunately geospatial you can't always count on that geospatial is still a niche and lots of recruiters unless they're working on a company that's really focused only on geospatial don't always know the right things to search or search for it's up to you to first of all look for roles that might be a good fit sometimes in a data analyst profile you might find a listing for someone who wants to build maps are you looking at data engineering you might actually learn that they might be using gdal and that's a great way to search for that here's another post to actually search
13:44 for different terms and optimize researching when you're looking through job listings themselves all this is great but the number one tip I have for LinkedIn is being proactive that sharing your ideas posting about things you're learning posting your portfolio projects and most importantly reaching out geospatial has one of the most active and engaged communities on LinkedIn and other social platforms and people are always learning from and sharing with each other take advantage of that jump into the conversation and share interesting things that you see you're working on or you're excited about did you build a great new portfolio project great share it did you find a new code snippet that was really helpful for you great share that with everyone as well the other thing I would say is don't be afraid to reach out to others if you see someone that's working in a team or a role that you're particularly interested in reach out it
14:29 might not work out the first time but they might have something in the future or they might know someone who needs need of a geospatial expert like yourself give it time keep up the hard work and eventually it will get there now in a lot of other spaces you'll see a ton of emphasis on the technical interview or program interviews geospatial interviews vary widely so what you want to do is be able to ask questions and any information that you can get beforehand is going to be really helpful for you when you jump into those first interviews if you had a tough interview or something didn't work out great figure out what that is and try to change it for next time and re-implement it keep practicing keep trying and eventually you'll translate that into success in the real world geospatial analytics is booming so now is the time to jump in with two feet start learning growing and building your career
