How Madison Became a Python Developer with Lighthouse Labs

Madison Wiebe loved the research, analysis, and experimentation involved in her neurobiology degree, but she realized that to effectively use data she would need to learn to code. After some research, Madison landed on the Data Analytics Bootcamp from Lighthouse Labs, thanks to their self-paced learning style and lifelong career support that would nurture a robust career path. Now a Python Developer at TraceRent thanks to an employee partnership connection from the bootcamp, Madison shares her tips for incoming students on making the most of the Lighthouse Labs experience.

What inspired you to start a career in data this year?

I have an educational background in neurobiology, and while I loved the research, analysis, and experiments of my undergraduate degree, I learned little in the way of coding. I love taking data and figuring out what it means, but I realized that I wouldn’t be able to effectively use data to identify patterns and draw conclusions if I didn’t have tech skills. 

There are so many data bootcamps now — Why did you choose the Data Analytics bootcamp at Lighthouse Labs?

I was interested in learning from a Coding Bootcamp to develop a strong foundation in Python and brush up on Excel and other tech tools that are essential to answering those larger and more complex research questions. The element that caught my eye about Lighthouse Labs was their lifetime career support. Knowing how difficult it is in this job market to get into a career rather than a one-off job, I knew that if a job didn’t work, I’d have ongoing support to help me land a better fit. Lighthouse Labs would be there to help me with a mock interview or technical interview, get support from mentors, peers, or the alumni network, or lean on their ability to find the best places for someone with my background.

I was also attracted to the self-directed aspect of their Flex Bootcamp. I was working part-time, so having the ability to choose when to learn was big for me. The lectures were at set times, but I could choose when to put in the 8-10 hours of studying. I loved that it was a fully remote Bootcamp, too. I know there are advantages to in-person learning, but the remote element made it super accessible so that anyone could do it.

How did you choose between the Data Science Bootcamp and the Data Analytics Bootcamp at Lighthouse Labs?

I was part of the first Data Analytics Flex cohort, so what made the decision for me was that the Flex Bootcamp timeline was a bit shorter. The data science and the data analytics students were all in the same cohort working on the same material for the first three quarters of the Bootcamp.

The main difference between the two data bootcamps is that data science students learn language learning models (LLMs) and supervised and unsupervised learning projects, leaning more heavily into that AI/machine learning area of expertise. 

In the Data Analytics Program, we focused more on data cleaning, data visualizations, and a bit of statistics. We didn't go quite so much into the automation/AI side. I was more interested in analysis than AI, so the shorter timeline with a more focused emphasis on my particular area of interest was what made the decision for me.

Did you feel like you had to know basic coding to apply to Lighthouse Labs?

No, I did not. I didn't know anything beyond Excel before the Bootcamp, and even then I wasn’t familiar with all it could do. I knew how to write a few functions and some basic formulas, but I had never written any macros, and I certainly had never coded with Python! 

To get into the Bootcamp, Lighthouse Labs was more interested in my problem-solving ability. They wanted to see how I approached a problem, the logic I used, how I structured my process and defined each step toward a solution. The emphasis in the interview process was more about logic than actual technical skills since they're going to give you those in the Bootcamp! 

What was a typical week like in the Data Analytics Flex Bootcamp? 

Even though it wasn't an immersive program, the majority of my time was still taken up by the Bootcamp. I spent 5-6 hours a day on the material. Since I didn't have a coding background, I was very diligent about doing all of the readings and exercises, so it took me a bit longer. I made sure to leave time in the evenings to take a break. The Flex Program allows you to have breathers without completely falling behind, which gave me the flexibility to put in that extra time at my discretion for those areas that I was having a harder time initially grasping. 

I would recommend taking the Flex Program over the immersive because you get all the same access to the mentors and curriculum resources, but you have more time to make use of them! 

What were your data instructors like?

Our instructors for lectures were both data professionals in the industry — one had a background in business, and the other was from an academic field and pursuing a master's at the time. They both had a lot of expertise in all of the material in different areas, which was nice! One instructor predominantly used SQL and the other used more Python. Depending on which module we were working on, a different instructor would step in where they have more expertise, and you could ask them detailed questions and real-world questions, too, like how something is used on the job and the advantages over other tools. 

What kinds of projects did you work on in the Bootcamp?

Being in the Data Analytics Program meant that I only worked on individual projects. The first two projects were dictated by the curriculum. The first module emphasized learning SQL and we had to perform an analysis using all SQL tools. The second one was a statistical modeling Python project, where we were given multiple data sources and had to use several Python libraries to import, clean, and combine the data. Our final product analyzed and visualized trends within the processed data.. 

The third project was a capstone project that we got to choose. The intention was to pull all of the knowledge gained over the course of the Bootcamp and integrate it into one final product. By using weather data from across British Columbia, I wanted to build a tool that would estimate how much energy could be expected to be produced in a given location from solar energy versus wind energy. The idea was that this tool would be useful to a residential homeowner looking to invest in some renewable energy to integrate into their home.

What was the overall Lighthouse Labs community like?

In retrospect, I would have loved to connect more with my peers throughout the Bootcamp, but at the time, I just wanted to dive into the material to understand it. I was more focused on learning than networking, but fortunately I didn’t completely miss my opportunity! After  graduating I have access to not only my graduating class, but everybody who's ever taken the Lighthouse Labs Bootcamp! We communicate through Discord, and there are different channels based on location and Program so you can connect with specific folks from other cohorts with different expertise. It's got something for everybody! 

How did Lighthouse Labs prepare you for the tech job hunt? 

Career Services was integrated into the curriculum with specific lectures on how to prepare a resume and CV, common interview questions to prepare answers for, and how to prioritize what to lead with. Often when you're on the job hunt, you don't really know what you have to offer, and you don't know what hiring managers and recruiters care about. Lighthouse Labs helped me filter my skills and work experience so that I could leverage transferable skills into a tech job, and identify how to tailor my resume to each opportunity in an industry that I had no previous experience in. 

They laid the groundwork with the lectures and resources, then we put it all to work after graduation. We were classified into different job seeker statuses depending on how urgently we were looking for a job. If you're only applying for 1-2 jobs a week, you might meet once a month to check in, get resources, and evaluate your progress and areas to improve.

For the most part, the people who enroll in the Bootcamp are active job seekers eager to land a job in tech — it’s their whole focus and priority. We would meet with a Career Services Advisor every other week on top of daily communication about job prospects and connections with employee partners. The Career Services Advisors would send my resume to openings they had found through recruiters or employers who may not have posted the job publicly. Many employers know that Lighthouse Labs graduates have a solid grounding in the technical skills that they’re likely looking for, so they contact Lighthouse Labs before they look anywhere else! Based on what the employer is looking for and based on the graduate pools, that connection is made between Career Services and hiring teams for these employers.

Which tech roles did you feel qualified to apply for after graduating?

I noticed that a job title didn't mean a lot because every company seems to have a different name for somebody who works with data, whether that's a developer, engineer, specialist, consultant, or anything like that. Most of what I felt qualified for were positions involving creating dashboards, so I was looking for something like a visualization specialist, business analyst, program analyst, operations analyst — basically, anything with “analyst” in it and working with Tableau, Power BI, and Python, all felt attainable. 

Now you’re a Python Developer at the tech company, TraceRent! How did you land this job? 

My current position at TraceRent was only possible through a connection that Lighthouse Labs made to a government funding program for employing recent graduates. My employment right now is fully government-funded!  

The original job description was for a Power BI visualization expert. I was intimidated because in the Bootcamp we focused on Tableau over Power BI as a visualization software, but I was confident that I would be able to transfer concepts from one software to another. My Career Services Advisor gave TraceRent my resume, and they contacted me within a couple of days to discuss opportunities. I met them the next day for an interview!

What was the interview process like for you? Did you feel prepared for the technical interview?

In the interview, they asked about skills from one end of product development to the other. They asked questions about how I would structure my data, how I would clean data, and what sorts of things I consider when doing a time series analysis and predictive modeling. 

Even though I was interviewing for the Power BI Specialist position, ultimately the job I got was as a Python Developer because of a previous project I did with time series analysis through the government initiative, Riipen’s ICT Ignite Program. 

The Ignite Program is something that Lighthouse Labs also pushes its graduates to apply for. I applied for it, got accepted, and then had a three-month contract through them. This made me a good fit for my current work at TraceRent since a lot of my day-to-day involves predictive modeling and time series analysis. 

So far, are you using what you learned at Lighthouse Labs now on the job? 

I use what I learned either during the Bootcamp or during the Ignite program as a Python Developer. Every day I’m using fundamentals I learned during the Bootcamp and have rarely had to look up industry practices. Lighthouse Labs’ comprehensive project management laid the appropriate foundation for creating projects, such as how to structure files; how to connect to a GitHub repository; how to save, comment, and process all the different functions that you use day-to-day to understand your data; and how to manipulate data. There's never been a time that I've been looking for a way to accomplish an output and the answer has been something that I've never heard of before. I left Lighthouse Labs well-prepared for the workforce. 

At this point in your tech career, was Lighthouse Labs worth it for you? 

I would have never gotten my foot in the door of the tech sector if I hadn't used Lighthouse Labs. I gained traction through their connections and their programs even when it came to all the jobs that I would apply for independently. I still use the resume-writing tools that Lighthouse Labs gave me. The only real tangible opportunities that have come my way have been through the programs, collaborations, and partnerships that Lighthouse Labs has, whether it's with the government or a particular employer partner — everything has been through that network instead of through a cold call on a job board.

What is your advice for incoming Data Bootcamp students on how to make the most out of the Lighthouse Labs experience? Anything you wish you knew before you started the Bootcamp?

1. Use your mentors! I tried to solve a lot of the problems myself. If I was trying to learn something and it wasn't making sense or I kept getting an error, I would try to troubleshoot it myself because I felt like I should at least try before asking somebody for help. But the mentors are there to help you get started! If you're about to start a project or you're about to design your capstone project and you're feeling lost, you can put in an assistance request, and the mentors will help you get organized. Working with the mentors streamlined so much for me when it came to the end of the Program. I realized that reaching out to my mentors could have saved me so much time if I had worked with them earlier in the Bootcamp!
2. Everybody is feeling the same imposter syndrome that you are. Everybody is struggling along, but everybody will make it, learn something, and feel confident by the end. 

Find out more and read Lighthouse Labs reviews on Course Report. This article was produced by the Course Report team in partnership with Lighthouse Labs.