A common theme I've noticed from talking with many data analysts and data engineers is that they didn't come from a "data" background. Helen Mary Barrameda someone who exemplifies this theme. She is based in the Philippines and started her career as a freelance writer in 2004 writing lifestyle pieces. She used her earnings from her writing gigs to pay for her engineering education, and eventually became a geodata engineer working in the field of Geographic Information Systems (GIS). In this episode, she discusses falling in love with Python, how her engineering background helps with a career in data, working from home before everyone did it, winning the a NASA challenge, and more. How web development can help with your data analyst skills Helen started her career in geomatics. Prior to this conversation I didn't know much about this industry: Geomatics is the integrated approach of measurement, analysis, and management of the descriptions and locations of geospatial data.Source: University of Florida Over time, Helen realized there wasn't a lot of creativity in the geomatics space. Most of her time was spent doing research and publishing for scientific journals. It was time for a change. Due to personal reasons, she decided to look for jobs and projects she could do entirely at home before the working from home trend really started. Given the engineering background, she started picking up projects in web development marketing automation, data analysis. She realized she could be a lot more creative with these web development and data jobs versus her old career in geomatics. Falling into data engineering As Helen continued taking on projects while working from home, data became more prominent in her projects. She worked with an ad tech company where she helped with getting data from their website into their CRM. She was doing a lot of data cleaning and was actually doing ETL (Extract Transform Load) for her clients. Before she knew it, she was doing data engineering work. Helen decided to find some data science workshops in the Philippines and started working on a Master's degree in data science. While her geodata engineering experience helped with some of the coding skills required in her data science projects, she felt that learning algorithms was still difficult. Like many people I've spoken with on this podcast, people fall into data analysis and data engineering. You work on data projects without realizing you are actually doing things that a data scientist is doing. And if you want to formalize the skills you are acquiring, you can go back to school like Helen did. In Helen's words: I learned the practical application of data skills before learning the theory. Building a "portfolio career" As Helen discussed her various roles and projects, she brought up a phrase I haven't heard before when you are progressing through your career: the "portfolio career." In HR speak, I think this is analogous to people who call themselves "generalists." Helen has gotten exposure to a variety of industries and people. She brought up an interesting point about how most data analysts progress through their careers. They are laser focused on their field or industry, and maybe don't have the time, need,