When Siphu Langeni describes her journey from working in healthcare to working in commerce as a data scientist, she tells people: “I went from saving lives to saving livelihoods.”

Siphu is a data scientist at Shopify, the platform that merchants and entrepreneurs use to start, grow, market, and manage in-person and online businesses. Before switching to tech, she worked in a clinical setting as a Certified Registered Nurse Anesthetist.

A major life event, plus “a healthy combination of confidence and a little bit of crazy,” prompted Siphu to start over and enroll in a data science certificate program. She dove into her data science coursework and watched video tutorials about coding in her spare time. “I felt like I couldn’t get enough,” she recalls.

Siphu landed an internship with Shopify in the summer of 2021, which gave her hands-on industry experience, and ultimately led to a full-time job as a data scientist in Shopify’s commerce algorithms and product understanding department.

Starting over in a new industry was tough initially, but it was a much-needed change, Siphu explains. “Everybody is job-seeking — and that’s cool,” she says. “But when you’re looking for a career, you need a thing that’s going to keep you coming back every day. I felt like the problem spaces within data science were so impactful.”

Curious what a career in data science could look like for you? Check out our skill path Data Science Foundations. It’s a beginner-friendly course that will teach you how to clean, analyze, and visualize data responsibly, plus introduce you to some of the jobs you can get in data science. Here’s how Siphu learned how to use code in data science, found a mentorship program, and ultimately got hired as a data scientist at Shopify.  

What got me interested in the job

“I wanted to do something different, but I didn’t know what that was necessarily. Working in healthcare, especially in very high acuity areas, there’s a tendency to run into burnout, so I knew it was a very real risk. I was at a point in my career where I felt really good; it’s comfortable to be in a place where people see you as kind of an expert at what you do.

I was getting a lot of exposure to decision-makers and policy-makers, and I found that I always seemed to provide the most compelling story or argument when I was able to back it up with data. I was incorporating a lot of data, but I wasn’t calling it ‘data’ at that point.

I decided to go back to school full time and study for an advanced diploma in data science. The stars aligned where the opportunity presented itself, and I was mentally ready to be in a position of starting over — because it’s tough.”

How I got in the door

“Imagine moving from one completely different space to another. There’s a lot of ambiguity, a lot of questions, a lot of blanks that need to be filled in.

After leaving school, I needed something that would allow me to be more industry-ready and employable. One of the ways I was able to do that was through a mentorship program called SharpestMinds. I was crawling going into that, and when I came out of it, I was running.

I was paired with a mentor working in the industry, and we were able to produce an end-to-end data science project. The project was related to the NLP (Natural Language Processing) space, so using language to predict certain things. I needed annotated data, so in the process, I actually created and authored my first Python package. That project let me learn a lot of technical skills and build confidence.

I started at Shopify as an intern, which ended up being a really great experience for me. It gave me hands-on experience to better understand the role of data science and what it even is. Fortunately, my team thought I was pretty good, so they kept me and I stayed on board. I’m actually on the same team that I interned on.”

What I actually do every day

“If you ask 10 data scientists what they do, they’ll all give you 10 different answers. One of the reasons why Shopify was one of my top choices is because I did want to be practicing data science and be very close to the coding side of things.

I love trying to figure out a problem, then translating that to code, pushing it to production, and actually seeing it do something cool. I feel really great when I’m working on coding.

The team that I’m on specifically is called ‘leverage,’ which focuses on giving our merchants the ability to leverage the scale that Shopify has and that network effect that they could never otherwise have on their own. That involves working very closely with product, engineering, and UX, building products that help make it easier for merchants to do that.

One example of something we’ve worked on is a product classification model. This is a really cool machine-learning model that inputs text and image features, and is able to categorize products appropriately. In order for businesses to have good SEO, to be discoverable, and to have proper taxation, their products have to be categorized correctly. This is one of those things that we provide to make it easier for merchants to be able to get their business going.”

Here’s what you need to get started

Like lots of data scientists, Siphu primarily uses Python as her main programming language. Working with software engineers, Siphu realized that she writes “production-grade code” that’s not reusable without solid documentation. “Good documentation has saved me on many occasions,” she says.

Shopify is all about empowering entrepreneurs and using data to level the playing field. With billions of products sold on its platform, Shopify works with a ton of data — “petabytes worth,” according to Siphu. So she uses SQL to access and manipulate the large volume of data. A strong understanding of SQL is very valuable as a data scientist, because you’re often using advanced queries to access data. “Data isn’t useful just by nature of it being there,” she says. “You have to be able to turn it into something that you can pull insights out of.”

Want to learn how to use Python and SQL like a data analyst? In our Career Path Data Scientist: Analytics Specialist, you’ll learn how to “talk” to data, make visualizations to see the big picture, and conduct tests. You’ll also walk away with some portfolio-ready projects under your belt that you can use in job interviews.

Beyond those technical skills, data scientists also have to be able to communicate and contextualize their findings. “So you find all the secrets that are locked away in the data — what if no one has any actionable insight or anything they can do as a result of what you’ve discovered?” Siphu says. “Then you’ve just had fun with the data, which is really cool, but then what?”

To explore the other types of careers that exist within data science, like Machine Learning Specialist or Natural Language Processing Specialist, check out our full catalog of data science courses.

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Data analytics is the process of taking raw data and turning it into something meaningful we can understand. By finding trends and patterns, you can make predictions and uncover new information that helps inform decisions. There’s a great demand for Data Analysts in healthcare, marketing, retail, i…

Data science is a rapidly growing field, with roles like Data Scientist and Machine Learning Engineer ranking high on top job lists from LinkedIn and Glassdoor.

And the industry is only getting bigger, according to Codecademy Data Science Domain Manager Michelle McSweeney. “We’re seeing data transform our society and everything we do, whether it’s measuring how well something performed or deciding what we’re going to do next,” she says.

Data science involves leveraging data to find actionable and business-advancing insights. If you’re interested in analyzing data to figure out why things happen and find big-picture solutions to problems in our world, or you want to work with artificial intelligence (AI) and machine learning, a career in data science could be right for you.

Ahead, we’ll explore seven popular roles in data science and their responsibilities, plus help you get started on your journey to landing a job in the field.

What is data science?

Data science sits at the intersection of probability, statistics, math, and software engineering. It allows us to make sense of and utilize the huge amounts of data we create every day, and it’s used in virtually every industry.

In finance, for example, data science helps banks identify risks and fraudulent behavior. In healthcare, data science underlies wearable tech devices (like Fitbits and insulin pumps) that monitor your health and help physicians assess and treat health conditions. And you’re probably already familiar with data science’s use in entertainment: Every time you get a suggestion from Netflix or Spotify, that’s data science at work.

The jobs you can get in data science

Here’s a look at the different types of roles and specialties within data science, and the programming languages and skills you need to pursue them.

Data Scientist

Data Scientists help organizations make the most of their data. Their daily tasks range from analyzing data to find insights and forecast future trends, to building machine learning models, algorithms, and applications. They also may use data to discover potential improvements and optimizations for their company’s systems and processes.

A Data Scientist’s scope and specific responsibilities depend on what the company does, how the data team works together, and the questions or problems they’re trying to solve, Michelle says.

Broadly speaking, “Data Scientist” is an umbrella term used to encompass four specializations: Analytics Specialists, Inference Specialists, Machine Learning Specialists, and Natural Language Processing (NLP) Specialists.

Analytics Specialist: Also known as Data Analysts, Analytics Specialists collect and analyze data to find patterns, trends, and insights that help guide decision-making. Their primary tool is SQL, which is used to manage relational databases, and they also need top-tier communication and data visualization skills to present their findings. “If you want to make beautiful dashboards or communicate data with a story, data analytics is the place to be,” Michelle says.

Analytics Specialists are the most in-demand and versatile of the Data Scientists as practically every organization creates and manages large volumes of data.

Inference Specialist: Inference Specialists help businesses understand what their data is telling them to do, Michelle says. They’re responsible for finding the why — figuring out how specific variables influence and drive business outcomes and results.

These Data Scientists mainly work with R, a programming language designed for statistical analysis, and may run hypothesis, statistical, and A/B tests. “If you’re wowed by statistics, inference is the place to be,” Michelle says. Good to know: There’s a huge demand for Inference Specialists in banking and financial institutions.

Machine Learning Specialist: Machine Learning Specialists build machine learning models and algorithms, and they may also be tasked with feature engineering, tuning hyperparameters (which guide the machine learning process), and identifying users who might be at risk of churn.

These Data Scientists use Python for predictive analytics and cluster analysis, and they need a solid understanding of data structures and algorithms.

“Machine Learning Specialists are at the forefront of what we’re able to do with data,” Michelle says. ”Machine learning, plus the computing power we have today, is pushing our capabilities beyond what we would have thought possible.”

NLP Specialist: NLP Specialists serve more of a niche role, working primarily with text and AI to teach computers how to understand and interact with humans. They typically work with programming languages like Python and Java, and their responsibilities might include cleaning and formatting unstructured data and building chatbots. “If you want to learn about language and apply text to machine learning without getting too deep into the math behind it, NLP is the place to begin,” Michelle says.

Machine Learning Engineer

Distinct from (yet similar to) Machine Learning Specialists, Machine Learning Engineers build machine learning systems and applications like facial and speech recognition software. They apply machine learning to streaming data at both input and output, and they primarily rely on Python and its machine learning libraries like pandas and scikit-learn. They also often work with big data, and they’re in high demand with organizations that sell consumer goods that require recommender systems (like TV streaming or e-commerce websites).

The main difference between Machine Learning Specialists and Machine Learning Engineers is that Machine Learning Specialists use machine learning to better understand their data, and Machine Learning Engineers turn machine learning models into products that end users can engage with.

Take Netflix as an example. The preview images used for different shows are based on a cohort analysis, which are performed by Machine Learning Specialists. The recommendation engine that suggests things you might like is created by Machine Learning Engineers.

Business Intelligence Analyst

Business Intelligence (B.I.) Analysts are more focused on practical outcomes than other data science roles, and their primary duties include answering business questions and delivering business-advancing insights to stakeholders. To do so, they need a deep understanding of their industries and departments. Michelle describes the role as “part domain knowledge and part technical knowledge.”

B.I. Analysts typically don’t need as much programming experience as other data science roles. They still use SQL, but their insights are shared with B.I. tools like Microsoft Excel (which you can learn how to use in our Analyze Data with Microsoft Excel course) and Tableau.

According to Michelle, this is the fastest route to a career in data science. “You don’t have to go deep into statistics or machine learning, you just need to understand the basics of how data and databases work and those two B.I. tools,” she says. “With that, you can start making a huge contribution to an organization because it allows you to ask and answer questions with data.”

How to know what kind of data scientist a company is looking for

Most Data Scientist job postings don’t distinguish between specializations. So it’s important to read the job description carefully.

Michelle recommends making a list of companies you want to work for and reading through their Data Scientist roles to get a sense of what they’re looking for. Here are some clues and keywords to look for:

• If the listing mentions data visualization and SQL, they’re looking for an Analytics Specialist.
• If a job description includes predictive analytics, machine learning, and processing large amounts of data, they’re looking for Machine Learning Specialists.
• If a posting talks about A/B and hypothesis tests, they’re looking for Inference Specialists.

Keep in mind that the line between Data Analysts and Data Scientists is very blurry, Michelle notes. The titles are often used interchangeably in job postings, so unless you’re pursuing NLP or machine learning roles, look for Data Analyst jobs as well.

How to become a data scientist

Ready to become a Data Scientist? We’re here to guide you along every step of the way.

Our Data Scientist career paths will teach you the foundations of data science and the skills and knowledge you’ll need for your specialization.

As you progress along your path, you’ll build projects that you can use to build a portfolio — and you’ll also earn a certificate upon completion that you can showcase in your resume and LinkedIn profile to help you land a job. We’ll also help you navigate your job search with interview prep, code challenges, tips from recruiters, and other helpful resources you can find in our career center.

Even if you don’t want to become a Data Scientist, learning how to use data effectively can help you advance in your career. “These career paths can serve almost any role,” Michelle says. “They’ll help you better understand your data, customers, clients, and processes.”

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Data Scientists try to make sense of the data that’s all around us. Learning Data Science can help you make informed decisions, create beautiful visualizations, and even try to predict future events through Machine Learning. If you’re curious about what you can learn about the world using the data p…

As a LGBTQ+ job-seeker, the thing that’s often top of mind (besides, I hope I get the job) is assessing how safe you are bringing your identity and lived experience into the application and interview process.

While lots of tech companies are making strides to show up for LGBTQ+ employees, the job search can present extra challenges, hurdles, and nuanced situations for members of the LGBTQ+ community, according to Josh Torres, an executive leadership and career coach in Oakland who works with historically marginalized communities in tech.

From a legal perspective, LGBTQ+ individuals are protected from employment discrimination on the basis of sexual orientation or transgender status. But it can still take a lot of mental energy to try and answer the interview questions while representing yourself the best that you can, explains Chris Rollins, a career coach in Tampa who specializes in LGBTQ+ leadership.

Here are some tips from career coaches who work with the LGBTQ+ community for navigating the job-search process and landing a job at a company that celebrates you.

Look at the company’s public presence

Take a look at how the company or organization outwardly supports the LGBTQ+ community. For example, “it’s a major red flag if a company does nothing for Pride or is not talking about LGBTQ+ issues in their workforce in some way,” Josh says.

Find out more about who’s in charge, like the company’s CEO and senior leadership team. “Do your research to find out as much as you can about who that person is at the top,” Chris says. “The way that they make decisions, share information, and work with their team all trickles down directly to their leaders.”

Some other broader questions to consider: Is corporate social responsibility baked into the company’s mission and work? Does the company sponsor events that support the LGBTQ+ community? And is advocacy consistent throughout the organization, or is it still lacking?

Ask questions about benefits

The type of benefits that a company offers often reflect how a company prioritizes inclusion, Chris says. For example, do company-sponsored health insurance plans cover gender-affirming care options and medical necessities for people who are trans? Is the parental leave policy equitable and extensive for parents and caregivers of all genders? And does the company cover adoption fees? What about IVF and other reproductive assistance?

All of these topics are fair game to ask a recruiter or hiring manager in a job interview (BTW, you should definitely ask questions in a job interview).

Beyond benefits, if you’re returning to an office environment, find out more about how amenable the workplace is to LGBTQ+ people. Ask if there are gender-neutral restroom options available, Josh says. “Or is there a place where you can feel safe as an employee going if you don’t necessarily identify as male or female,” he says. Though these things may seem insignificant to some, they can have a major impact on your daily work life.

Talk to current employees

Throughout the interview process, you typically get to speak to a panel of people who work in various roles at the company, which is a great way to get background information about employees’ experiences, Josh says.

If there isn’t someone on your interview panel who represents the lifestyle or aspect that you’re trying to find more information on, you can always ask to speak to someone on the company’s employee resource group (ERG) for LGBTQ+ people, Josh says. Think of this as a “gut-check” to get a sense of how LGBTQ+ folks really feel working at the company, he says.

Not sure who the right person is to talk to? “Do a search on LinkedIn for an LGBTQ+ or Pride ERG and see if any employees at the company have that mentioned in their profile,” Chris says.

Chris suggests asking each of the people you’re interviewing with about the company values to see how they embody them. “You can do your own assessment there to see to what extent they take [equity] seriously, and is it actually part of the culture,” he says.

Share as much as you’re comfortable

Ultimately, it’s up to you what you share with an interviewer or even a future employer down the line, Josh says. For some folks, there may be aspects of their gender identity and expression that are visible in a job interview or in the workplace, while others prefer to wait until they’ve developed a foundation of trust to open up or come out to coworkers.

“In an interview where you are creating a narrative around yourself, is [your LGBTQ+ identity] pertinent to your story?” Josh says. “Is it something that is going to bring a different lens to the work that you’re doing and add value?” More often than not, your diverse lived experience and identity are assets that will only enhance the work that you do at an organization. “If you are comfortable stepping into that identity, there’s a lot of power in it as well,” he says.

Still feeling nervous about that job interview? Head to our career center for more advice on answering common technical questions and crafting a resume that will make you stand out from other applicants. There’s even a collection of projects that you can complete and use in your portfolio. Remember: You got this!

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