Updated! We first profiled Francisco Sosa in 2016, but we checked in with him again in 2020 to find out how he’s feeling about his data science skills now.
Like many Dataquest students, Francisco Sosa came to data science from Excel.
He was working as a Human Resources Analyst, using Excel to analyze data and solve problems. He was making an impact, but he could tell that Excel was holding him back. He decided to try to learn programming.
He sampled Codecademy, Udacity, and DataCamp, but felt that all were too basic: "They take you by the hand," Francisco said. "You don’t have to think. The instructions tell you exactly what to do. You think you're learning but you're not."
Francisco found Dataquest's approach refreshingly different. The guidance he needed to learn was there, but the platform provided more of a challenge. "You learn better in the moments when it’s harder and you need to work to find a solution," he said at the time. "Dataquest finds a balance between being too easy and being so hard that you get discouraged."
Four years later, he still feels that way. Now working as a Data Scientist for Healthcare.com, he says: “I definitely still use the skills that I learned at Dataquest. I use those skills every day.”
Life as a Working Data Scientist
There is no typical day in the life of a data scientist, of course, but Francisco says that most days, he’s working with Python on tasks related to either data science or data engineering. “Most of the work I do is put machine learning models into production,” he says, but members of the data team are expected to be able to build end-to-end products, so his job also entails some data engineering.
It also includes a lot of pandas, which Francisco says is the data team’s tool of choice for most data cleaning and exploration tasks. That’s something he got from Dataquest, he reminds us: “I think the pandas course in Dataquest is probably the best I've ever done. That's the one I recommend to everyone.”
Four years out from his big career switch, it’s clear that Francisco is right where he wants to be: doing data science daily as part of a dedicated team at a fast-growing company.
How to Be Like Francisco
Back in 2016 when Francisco Sosa first started looking for data jobs, he faced a real challenge: there weren’t many to be found.
"Because Guatemala is not the most cutting edge country,” he told us, “I started by looking at companies that might have large amounts of data. I found some interesting companies, but they didn’t have any jobs listed on their website."
But Francisco was not discouraged. Instead, he applied for whatever jobs he could find, and tried to work data into the conversation. "I would get an interview for a marketing job, and then in the interview I would talk about what I could do with data to help the company."
And it worked! In 2016, Francisco was hired as a Data Scientist at Allied Global, an outsourcing company where he joined a newly-created team of data scientists working to optimize the way that the company’s call centers operate.
“I definitely still use the skills that I learned at Dataquest. I use those skills every day.”
He worked there for roughly a year before moving, and continued to do freelance consulting work as a data scientist before ultimately taking another full-time role: his current job at healthcare.com.
He suggests that Dataquest students interested in career-switching like him practice what they’re learning in Dataquest course by building their own projects after finishing the guided projects on the platform. “I think working with real data and real problems surfaces weaknesses you have in your skills, and that's where you learn the most,” he says.
“When applying for jobs,” he says, “it's really intimidating to look at the job postings and all the skills they list. But don't be afraid, and don't think you need to be 100% on all the skills, or even have all the skills that they list. Just apply.”
“People hiring for data science often don't exactly know what they're looking for. They just go online and look at some other job postings and copy all of the possible skills that data scientist might have. So you should just apply.”