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Data Science for GIS Pros – 5 tips to make the most of your skills

Data science is a rapidly growing field with many applications, and as the amount of geospatial data increases, GIS skills are becoming more and 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 to uncover geographic patterns and relationships.

If you’re looking to expand your geospatial analysis and data science skills from recreational to professional, this information is for you. Here are five tips on how to make the most of your skills.

1. Be data-bound.

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

Get used to writing and reading data from the start so that you are comfortable with it during the analysis process. The more familiar you are with data, the easier it is to manipulate it, create reports and draw conclusions from it.

Consider the case of two people interested in getting into data science. One person keeps a journal while the other keeps a spreadsheet. Both pursue similar things. However, their respective records are completely different. The diary records the daily activities on paper while the spreadsheet records the events and related dates. By using these two types of records, the spreadsheet person sets himself up for greater success.

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

2. Do your research.

When you’re just starting out as a data scientist, it’s important to learn as much as you can about the field so you can broaden your horizons and better understand the possibilities.

The best way to do this is to look where other professionals go. You might be surprised at how many ways 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 look at the code for open data projects.

3. Network, network, network.

When you’re just starting out, it’s important to build as many connections as possible. Make new friends, learn from others, and look for opportunities to help fellow scientists.

When you’re working with a large team on an advanced project, you’ll need to work with other specialists such as software engineers, statisticians, and others, so it’s important to ensure you can discuss issues openly with a variety of specialists.

The best data scientists are those who are willing to ask for help and are able to reach their full potential. One of the best data science communities to practice these skills is Kaggle. This community focused on coding has more than 3 million users and offers competitions to improve your skills.

4. Don’t be afraid to experiment.

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

This doesn’t mean that you should deviate from the norm at all times, but it does mean that you should be prepared to follow the data wherever it takes you. Don’t be afraid to experiment.

Also, don’t be afraid to ask other data scientists for help. You’ll probably be surprised at how helpful people can be. You can even make 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 integral part of data science, but data science is not just about analyzing data. It is also about drawing 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 jobs as a newly hired data scientist is to create a dataset 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 across many different industries. As the volume of geospatial data increases, the line between GIS analysts and data scientists continues to blur.

Whether you’re a mapmaker looking to use data to make business decisions or you’re a data scientist, there’s likely an opportunity for you to use your GIS skills in today’s increasingly digitized world.

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

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