What Can You Build When You Learn Data Science at Metis?

There are still nine million unfilled jobs in the United States as of 2021. By 2030, the labor shortage could reach 85 million. The lack of skilled workers, however, is not the only factor at play. According to Forbes, employers may be screening for the wrong qualifications.

In the same article, LinkedIn’s Head of Global Talent Acquisition Jennifer Shappley said that the hiring trend has started to change in recent years. More companies are looking for skills relevant to the job in a bid to fill these open roles. She added, “It’s the skills that matter right now, not how or where they were acquired.”

For this same reason, Metis requires its students to punctuate their training with a comprehensive project portfolio. It typically involves independent research geared toward developing an innovative solution for a real-world problem. Such a project can showcase students’ abilities and boost their chances of success when applying for a job.

If you’re looking to upskill and are curious about the types of projects you would be working on at this data science bootcamp, read on as we look at three actual projects that Metis alumni have worked on.

What Projects Can You Build When You Learn Data Science at Metis?

Metis students do not simply dive into pre-packaged projects. They begin by scoping their own data science projects, which involves key steps like identifying project goals, mapping out the project workflow, sourcing their data, and making critical design choices like selecting an appropriate machine learning algorithm.

Students then use an iterative process to improve their work continually and eventually build full end-to-end data science solutions. Here are some of the projects that Metis alumni have completed.

Anti-Recommendation News Machine

Alexandra Garney used to manage school-based civic engagement programs. She took a brief course on big data and enjoyed it, so she continued to learn R and Python. Her passion for data science led her to enroll at Metis. There, she took a Bootcamp Prep course before diving into an immersive bootcamp. “I loved it,” she said, as she described her enthusiasm for hands-on experience and cutting-edge knowledge. “I wasn’t learning old stuff.”

Alexandra’s favorite topic was natural language processing (NLP), and so she came up with an anti-recommendation news machine as one of her projects. “If you are seeking out news that’s different from what you are consuming, then what are the ways that you can access recommendations for that?” she wondered.

This project was also inspired by her work in civic engagement. “We get stuck in a particular information chamber, and people often want to hear about other people,” Alexandra explained. “They like to have well-rounded opinions that they can feel good about.”

Recommendations are derived from what we frequently consume, which she found quite constricting. As a result, Alexandra’s recommendation machine “took in news article titles and then made recommendations that were from a different perspective but were in the same subject as the one as you inputted”.

Her key takeaway from this project: a problem does not have a single solution. “Before I did data science, I used to have this idea that there was just one solution and it would work as you expect it to,” she said. Working on the project taught her otherwise. That is, there could be multiple solutions that she can assess to find the best one.

Alexandra’s efforts were not in vain. She landed a job as a data scientist much sooner than she expected post-bootcamp. “I wanted a highly technical role, and that’s what I got. I am working on a lot of NLP stuff, too,” she said. It turns out her NLP project assisted her in obtaining her current position. Alexandra remarked, “It drove a lot of the interview processes.”

Alexandra now works as a data scientist for Realm, a housing startup.

Mapping Subway Growth Post-Pandemic

In her first year of teaching, Elizabeth Naameh was only supposed to teach math—until the principal asked her to teach AP Computer Science as well. She said, “I’d never written a line of code in my life.” Nevertheless, she agreed. As she immersed herself in EdX, Coursera, and Udemy, AP Computer Science quickly became her favorite class to teach.

Eventually, Elizabeth learned of Metis and the bootcamp’s focus on data analytics and data science. She was confident she would receive quality instruction because the bar for entry was so high. “When you’re investing $10,000 to $16,000 for an education program, you want to make sure you are getting the best quality.”

True enough for Elizabeth, she loved the rigor of the course, the top-notch instructors, and the camaraderie she developed with her cohort. She especially enjoyed her first project: mapping subway growth in post-pandemic New York.

In this project, Elizabeth dealt with a complex set of data from the New York City subway traffic. The goal was to “figure out the trends as we return to a normal life in a post-pandemic world and how do we keep people safe,” she explained, noting how “social distancing is kind of antithetical to the subway system”.

She intended to use the data to develop a strategy for investing in alternative transport modes such as bus routes, bike lanes, and bike parking.

While working on the project, Elizabeth realized that “60 to 80 percent of data analysis is cleaning and wrangling data”. Still, she was most excited about the geo-mapping. “I had to pull in additional external data sets, which is something you’ll do often as an analyst.”

She emphasized how the project prepared her for work. “You have to go outside, use your intuition, and pull in and join and merge more data.” A month after graduation, Elizabeth landed a job. And the best part: she received her desired salary without having to haggle.

Elizabeth now works as a Data Analytics Coach at Multiverse, a UK-based edtech startup that is expanding to the United States. Her life’s mission is to assist people in obtaining high-paying tech jobs while remaining debt-free and equipped with practical skills.

“That’s what I love about Metis,” she said, referring to how Metis gave them authentic challenges, dealing with huge datasets as in real jobs.

NLP Movie Recommendation

Prathap Rajaraman wanted to apply his analytical skills to a wide range of innovative industries but was not ready to leave his job as an actuary. That’s when he found Metis’ Online Flex Data Science Bootcamp. One of the projects he worked on during his training was an NLP movie recommendation, which he chose because of his interest in video streaming services such as Netflix, Hulu, and Amazon Prime.

It examines the plot summaries of a user’s inputted movies and suggests other similar movies to watch. Its goal is to create a “barebone replication of a Netflix movie recommendation system”.

Prathap started by extracting data from The Movie Database (TMDb), which he chose for its simplicity. He then compiled plot summaries and other quantitative information from films. Next, he used NLP techniques to help translate these into quantitative data. He was able to narrow the categories down to 18 and determine which films were closely related.

“The possibilities are endless,” he said as he explained his method.

“The plot summary does a good job of capturing keywords for an action film, but it doesn’t do a good job of capturing a style of humor,” Prathap said. So, while it worked well for action movies, he explained that it struggled with comedies because “a style of humor will not be accurately captured in a movie summary”.

Another challenge was obvious recommendations. He used Star Wars as an example, where a consumer inputs Star Wars in the search bar and receives direct recommendations for the sequels. While that makes sense, Prathap pointed out there’s no practical value. “You don’t have to be a data scientist to figure out that if you like Star Wars, you might like the Star Wars sequels.” Fortunately, he was able to resolve these issues.

Prathap finds his product and the entirety of his time at the bootcamp to be rewarding. He discovered that studying machine learning methods does not require a PhD or a computer science background. “It’s something that a data scientist could learn from a bootcamp,” he said, adding, “Picking that [skill] up from a bootcamp was pretty cool.”

Prathap is fresh out of the bootcamp and has yet to quit his actuarial job. But he has made himself available on the job market, armed with his newly-acquired skills and a portfolio of projects to show for them.

Demonstrate Your Data Science Skills with Metis

Metis gives you a project experience that is true to the real industry condition. Your projects can demonstrate your tech abilities and employers can use them to evaluate your competencies.

So long as you are proactive, Prathap said, you’ll reap huge benefits from a bootcamp. Just make sure it is clear to you why you want this because, as Elizabeth puts it, “If you are rooted to your purpose, it’ll help you get through it.”

If you are interested in leveling up your skills and learning through practical application, apply now to Metis.