Data Science for GIS Professionals – 5 Tips to Make the Most of Your Skills

Data science is a rapidly expanding field with many applications, and with the increasing volume of geospatial data, GIS skills are becoming more common job requirements for data scientists.

Both GIS specialists and data scientists work with large data sets to make quantitative and causal discoveries. They process large amounts of data to answer questions efficiently and uncover geographical patterns and relationships.

If you are looking to take your skills in geospatial analysis and data sciences from recreational to professional, then this information is for you. Here are five tips for making the most of your skills.

1. Be Data-Bound.

One of the most important steps to your success is getting comfortable with data – so comfortable that you live and breathe it. This may seem obvious, but some new data scientists experience culture shock when first confronted with the sheer volume of data they are asked to manage.

Get used to writing and reading data from the get-go so that you are comfortable with it during the analysis process. The more familiar you are with data, the easier it will be to manipulate it, create reports, and make inferences from it.

Consider the case of two people who are interested in getting into data science. One person keeps a diary, while the other keeps a spreadsheet. Both people track similar things. However, their respective records are completely different. The diary records daily activities on paper, while the spreadsheet records the events and associated data. By using these two types of records, the spreadsheet person is setting themself up for greater success.

Be a spreadsheet person. For even more experience, consider creating powerful visualizations in the cloud with your data, bringing it to life.

2. To Your Research.

When you are just getting started as a data scientist, it is important to learn as much as you can about the field so that you can expand your horizons and better understand the opportunities.

The best way to do this is to start looking in places where other professionals go. You will be surprised by how many options to learn data science are available online if you broaden your search.

There are many online databases, forums, and communities where you can share your ideas and ask and answer questions from fellow data scientists. You can also check out the code for open data projects.

3. Network, Network, Network.

When you are just getting started, it is important to build as many connections as possible. Make new friends, learn from others, and keep your eyes open for opportunities to help out fellow scientists.

When you are working on an advanced project with a large team, you will need to collaborate with other specialists, such as software engineers, statisticians, and others, so it is important to make sure that you are comfortable candidly discussing problems with a variety of specialists.

The best data scientists are the ones who are comfortable asking for help, and who are able to leverage their skills to their full potential. One of the best data science communities for practicing these skills is Kaggle. This community, focused on coding, has more than 3 million users, and offers competitions to help hone your skills.

4. Don’t Be Afraid to Experiment.

When it comes to data analysis, there is no such thing as “having it all.” Digital data analysts need to be willing to experiment, try out new techniques, and learn from their mistakes.

This does not mean that you should deviate from the norm at all times, but it does mean that you should be ready to follow the data wherever it leads you. Do not be afraid to experiment.

Also, don’t be afraid to ask other data scientists for help. You will likely be surprised by how helpful people can be. They may even offer suggestions on where to start your data science journey.

5. Be a Problem Solver First.

The best data scientists are problem solvers. They try new techniques, learn from their mistakes, and are flexible enough to see things from different angles.

Data analysis is an essential part of data science, but data science is not only about analyzing data. It is also about making inferences from non-spatial or geospatial data. GIS can be a powerful tool for data scientists, who can use it to analyze and visualize data in a variety of ways.

In many cases, one of your first tasks as a newly-hired data scientist will be creating a data set to visualize, analyze, and solve a problem, so focus on developing your problem solving skills.

Conclusion

Data science is a rapidly growing field that has applications in many different industries. With the increased volume of geospatial data, the line between GIS analysts and data scientists is becoming finer.

Whether you’re a cartographer looking to leverage data to drive business decision-making, or you’re a data scientist, there’s likely an opportunity for you to leverage your GIS skill set in today’s ever-more-digitized world.

Use these five tips to make the most of your skills, reach your full potential, and build a strong portfolio to make your mark in the industry.

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