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 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).
In fact, recommender systems are becoming more popular, and they can be a valuable tool for many of the roles included in this list. Beyond providing users with personalized suggestions, they also help businesses learn more about their customers, and they’re used by companies like Spotify, Netflix, Uber, and Google. Check out our Build a Recommender System skill path to learn how to build one from scratch. (And if you want to skip the basics and start building right away, try Learn Recommender Systems.)
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.”
Want to become a B.I. Analyst? Check out our Business Intelligence Data Analyst career path.
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.
- Want to learn how to analyze data for insights and communicate your findings with data visualization techniques? Try our Data Scientist: Analytics Specialist career path.
- More interested in pulling meaning from data and performing hypothesis, statistical, and A/B tests? If so, check out our Data Scientist: Inference Specialist career path.
- If machine learning is your jam and you want to dive into neural networks, feature engineering, and predictive analytics, our Data Scientist: Machine Learning Specialist career path could be right for you.
- And if you’re passionate about linguistics and its applications in tech, we’ll teach you text generation and preprocessing, along with language parsing and quantification in our Data Scientist: Natural Language Processing Specialist career path.
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.”