If you’re not sure what a Data Scientist does, you’re not alone.
At a high-level, a data scientist collects and analyzes large amounts of data to find useful patterns and insights. They use tools like statistics, coding, and data visualization to help businesses make smarter decisions.
In this article, Catherine Zhou, a Data Scientist at Codecademy, provides some insight into the world of data science, talks about her day to day, and helps us answer the elusive question, “What does a Data Scientist do?”
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But first… what is data science?
The first step to understanding what a Data Scientist does is to understand what data science is. The following definition comes straight out of Code Foundations, a Codecademy career path designed to provide an overview of the main applications of programming.
Data gives us information about the way the world works. And information can carry meaning – from a click telling us what someone likes, to toxins in the water signaling a health concern. But, data is meaningless unless we do something with it. That’s where data science comes in.
Data science enables us to take data and transform it into meaningful information that can help us make decisions. It is interdisciplinary and combines other well-known fields such as probability, statistics, analytics, and computer science.
The work that you might do could range from writing up reports to building machine learning models. No matter what your interests are, data science is applicable – because these days, we have data on everything!
What does a Data Scientist do?
Now that we know what Data Science is, let’s talk more explicitly about what a Data Scientist actually does.
In our interview with Catherine, she says: “there’s a wide range of ways that Data Scientists may work with strategy, decision making, and implementation of analysis. As you can imagine, the role of a Data Scientist may look very different depending upon what company you’re working for, and what business domain you’re working in!”
Below are just a few of the different tasks that a Data Scientist might do:
Data collection and preparation
Data collection and preparation are key parts of a data scientist’s job. The first step to solve any data-related question is to gather the right data. But raw data often comes with errors, missing pieces, or inconsistent formats. So, the next step for a Data Scientist is to prepare the data, which requires cleaning and organizing it. This might involve removing duplicates, fixing typos, converting dates into a standard format, or combining data from different sources. This is a time consuming, but essential process that every Data Scientist must do.
Machine learning
Instead of just analyzing past trends, Data Scientists use machine learning to build models that can learn from patterns and improve over time. For example, they might create a model that predicts customer behavior, detects fraud, or recommends products. To do this, they train algorithms using clean, prepared data and then test and fine-tune the models to make sure they’re accurate.
Data modeling
Data modeling is an important part of a Data Scientist’s job because it helps them understand relationships within the data and structure it in a useful way. By creating models — like diagrams or mathematical frameworks — Data Scientists can organize complex information, define how different pieces of data relate to each other, and set the stage for deeper analysis.
Data visualization
Data visualizations are how Data Scientists can turn complex numbers and patterns into clear, easy-to-understand visuals. Charts, graphs, and dashboards help tell the story behind the data, making it easier for people — especially non-technical audiences — to see trends, compare values, and make informed decisions.
Algorithm development
Algorithms are the rules or instructions that help computers process data efficiently, whether it’s sorting information, finding patterns, or making decisions. Data Scientists often spend time designing and tweaking algorithms to improve accuracy, speed, or relevance for specific tasks.
Mathematical statistics
By using concepts like probability, distributions, and hypothesis testing, Data Scientists can measure uncertainty, test ideas, and draw meaningful conclusions from data. As an example, a Data Scientist might use statistics to determine if a change in a website led to more user sign-ups or if a pattern they see in the data is just random.
Programming
With programming languages like Python, R, and SQL, Data Scientists can automate repetitive tasks, build machine learning models, and create custom tools for specific problems. For example, they might write code to clean messy data, run simulations, or generate reports that highlight key insights.
What’s the difference between a Data Scientist and Data Analyst?
It’s true, both Data Scientists and Data Analysts are focused on turning raw data into actionable insights that can inform better business decisions. However, these roles differ in scope, complexity, and focus.
Data Analysts are typically focused on looking at historical data to answer “what happened?” and “why did it happen?” Their work is often more structured and driven by specific business queries or performance metrics.
Data Scientists, on the other hand, tend to operate at a more advanced level to answer future-facing questions like “what will happen next?”
What is an average Data Scientist salary?
Looking at Indeed, Data Scientists make an average salary of $126,833 per year in the U.S.
Even with less than a year of experience, Data Scientists earn $101,338 on average. Those with three to five years of experience make $138,080 each year, with a high of $201,230.
How do I become a Data Scientist?
There are many different paths to become a Data Scientist, but here’s a look at the different skillsets and college degrees that will help build a strong foundation for a future career.
College Degrees
The best college degrees for aspiring Data Scientists include:
- Computer Science: Gives you a solid understanding of algorithms, programming, and data structures, all of which are essential for data manipulation, analysis, and building machine learning models.
- Statistics: Helps you master the mathematical and statistical concepts used to analyze data, test hypotheses, and make predictions, which are central to data science.
- Data Science: Many universities now offer degrees specifically in data science, which combine elements of computer science, statistics, and machine learning. This degree is tailored to prepare students directly for data science careers.
- Mathematics: A degree in mathematics, especially with a focus on applied mathematics or computational mathematics, will equip you with the problem-solving and analytical skills needed to work with complex data sets.
- Engineering: Particularly degrees in electrical, software, or systems engineering often have a strong emphasis on programming, data processing, and computational methods, all of which are highly relevant to data science.
Skills
There are a number of skills that are shared by Data Scientists across the board. If you’re thinking of becoming a Data Scientist you’ll want to build your skillset in the following areas:
- Descriptive and inferential statistics
- Probability
- Programming (Specifically SQL, and Python or R)
- A passion for diving deep into the data for the specific field you plan to work in
Of course, there’s a whole lot to learn in each of these areas. But Catherine explains that you shouldn’t feel like you have to learn it all:
“I’m always humbled by how much more I have to learn. Originally when I broke into the field I felt really overwhelmed and felt a lot of imposter syndrome about having to learn a lot. But I realized that when you work in data analysis or statistics you end up specializing in one part of it.
“You might specialize in predictive analysis; you might specialize in reporting; you might specialize in machine learning or artificial intelligence. There are so many subsets — usually Data Scientists will focus on one thing and get really good at it.”
Check out our data science courses to start building the skills you’ll need to launch your career.
Is data science right for you?
When asked if she always wanted to be a Data Scientist, Catherine shared, “This might be weird, but I was also really into probabilistic thinking and used to think about how it applied to my day-to-day decisions. I would try to calculate things like: if I miss this traffic light, what are the chances I’ll miss the next two lights? How much longer would that lengthen my commute?”
If you’ve found yourself trying to make similar calculations, are curious about analyzing human behavior, or get excited about using data to uncover interesting or surprising information, a career in data science may be in your future!
Our Data Scientist: Machine Learning Specialist career path is designed to give you all the skills you need to become a data analyst, data scientist, or machine learning engineer.
Learn more and get started here.
This blog was originally published in April 2021 and has been updated to include updated salaries, and new resources.
Whether you’re looking to break into a new career, build your technical skills, or just code for fun, we’re here to help every step of the way. Check out our blog post about how to choose the best Codecademy plan for you to learn about our structured courses, professional certifications, interview prep resources, career services, and more.
