Data science is a high-demand field. The US Bureau of Labor Statistics (BLS) projects job growth to skyrocket by 34% from now until 2034, making it one of the fastest-growing professions in the country.
A big reason for this rising demand is the ongoing need for data-driven decisions. Data scientists are the ones who dig into all that raw data and turn it into actionable insights that organizations need to thrive.
Data scientists rely extensively on technical, computational, and analytical skills. But that’s only half the story. In an increasingly AI-driven age, you’ll also need strong communication and problem-solving skills to thrive in the field.
This guide breaks down the technical and soft skills that set you up for success in data science.
Key Points
- Data scientists analyze raw data and turn it into actionable insights used to make decisions, forecast trends, and further innovation.
- Core data science skills include coding, data visualization, statistics, math, business storytelling, and collaboration.
- Having a solid foundation in technical and applied skills helps data scientists stand apart from the crowd in this fast-paced, high-growth field.
- While on-the-job experience helps, a solid portfolio of real-world projects, freelance or volunteer work, and internships can put you on the path to success.
10 Skills Every Data Scientist Needs
Data scientists are masters at turning raw data into real, actionable solutions. Every data scientist needs the skills to collect, clean, analyze, model, and communicate data to solve practical problems.
The most common data science skills include programming, data wrangling and visualization, statistics, and machine learning. Data scientists also need strong communication and storytelling skills, as well as a curiosity-driven mindset.
There are many types of data scientists, with skills and roles varying by specialization. But these are some of the top data science skills across job descriptions and training programs.
1. Programming for Data
Data scientists spend much of their time coding. So, proficiency in computer programming languages like Python, R, and SQL is key. These are the building blocks for working with data at every stage of the workflow, from the initial pull to the final insight.
Each programming language has its purpose. For example, SQL is used to access and manage large databases. R is often used in statistical analysis. And Python can be used for anything from data cleaning to machine learning.
2. Data Cleaning and Wrangling
Most data science projects start with data wrangling. This involves taking raw data, cleaning it, and converting it into a structured format. This is crucial since raw datasets often contain missing values, outliers, and inconsistencies.
Strong data wrangling skills let data scientists spot patterns and extract key insights in the name of creating usable datasets. This is fundamental in the data workflow process because it shapes the quality of everything that follows in the workflow.
Common data wrangling tools include Trifacta, Altair, and Tamr. For database management, popular tools include MySQL and Oracle.
3. Statistics and Probability
At its core, data science relies on statistics and probability. Therefore, it’s helpful to master concepts like probability distributions, confidence intervals, linear regression, and dimension reduction.
Data scientists rely on these skills to make sense of raw data. You’ll use them to test hypotheses, interpret results, write new algorithms, gain trustworthy insights, and build advanced machine learning models. It’s all about getting to the heart of what the data is actually saying.
4. Exploratory Data Analysis (EDA)
EDA is the first deep look at a dataset. It’s how you assess a dataset and uncover early signals that shape the rest of the project. Basically, it’s the bridge between raw data and modeling.
It points you to patterns and anomalies within existing data, helping you build and test predictive models and ask the right questions. That way, your data produces the most valuable insights.
Here’s a look at a typical EDA workflow:
- Wrangle or clean data.
- Visualize data using heatmaps, charts, scatter plots, or histograms (charts that show how your data is distributed).
- Summarize stats (like mean, median, and variance).
- Analyze data to find missing values, outliers, patterns, anomalies, and quality issues.
- Transform data.
- Generate new hypotheses for future analysis.
- Use statistical analysis like dimensionality reduction, bivariate analysis, and multivariate exploration.
- Communicate the insights you find.
5. Machine Learning Fundamentals
Machine learning (ML) is one of the most important skill areas for data scientists. At a high level, ML is about teaching computers to recognize patterns and make predictions based on data.
Two core approaches show up in most data science work:
- Supervised learning: You train a model on labeled data so it learns the relationship between inputs and outputs. These models often require more direct human oversight.
- Unsupervised learning: The model trains on unlabeled input and output data to uncover structure or patterns on its own. It analyzes the data to identify patterns and relationships. These models may be less accurate, but they require far less human intervention.
Other useful machine learning skills for data scientists include:
- Feature selection (choosing the most relevant dataset features for performance and accuracy)
- Model evaluation
- Techniques to cut down on overfitting
- Understanding which metrics to choose to avoid unreliable, inaccurate, or biased models
If you’re curious how data science compares to what machine learning engineers do, this breakdown offers a helpful side-by-side look at both roles.
6. Data Visualization and Storytelling
Data visualization helps you turn complex findings into clean visuals, like charts or dashboards, that people can understand at a glance. It’s how you present your findings to both technical and non-technical teams (including stakeholders).
But simply displaying insights through charts or other visuals isn’t always enough. That’s where storytelling comes in. Being able to interpret the results and highlight what matters helps you explain the “so what” in a clear, engaging way.
7. Business and Domain Understanding
Data scientists work in many fields, from finance to healthcare. Domain knowledge is key to understanding industry-specific challenges and workflows. It also makes it easier for you to ask the right questions and understand constraints and success metrics. From there, you can better build relevant solutions.
For example, if you’re working in healthcare, knowing how clinicians diagnose a condition helps interpret false positives and negatives correctly. That way, you avoid building a model that flags the wrong patients.
8. Experimentation and Causal Thinking
Causal thinking helps you figure out why something happened (not just what happened). It’s the skill of recognizing which variables might influence an outcome and which ones are simply moving alongside it.
Thinking in terms of cause and effect changes how you frame problems and structure analyses. It pushes you to question what factors could be shaping the data and what might be misleading you.
And, of course, effective data scientists are skilled in experimentation. That often means setting up tests, like A/B tests or quasi-experimentation, to distinguish meaningful effects from noise.
9. Deployment and Production Thinking: MLOps Basics
In today’s world, more and more businesses are using machine learning operations (MLOps) to make decisions. That’s why MLOps has become such a crucial data science skill.
MLOps is a set of practices that support the machine learning lifecycle, from development and testing to deployment. It allows for experiment tracking, model monitoring (which may include detecting model drift), versioning (tracking changes over time), and model retraining. Essentially, its purpose is to make sure machine learning models are scalable and reliable in production environments.
The MLOps pipeline generally starts with data collection and preparation, followed by model training, testing, deployment, monitoring, and improvements or updates.
10. Communication and Collaboration
Technical skill gets you in the door, but it’s your ability to communicate that helps your work land. You’ll need to explain tradeoffs and break down complex ideas in a way that fits your audience.
Data science work is never solo. It happens alongside product teams, engineers, designers, and stakeholders who depend on your insights to make decisions. That’s why communication and collaboration are core hiring requirements.
Tools That Help You Apply These Skills Faster
Skills are the foundation, but the right tools help you put them into practice more quickly. Here are some of the most common tools data scientists rely on today:
- Data integration services that combine data across many sources (like apps, cloud platforms, and machines)
- Digital notebooks used in model development, interactive data analytics, and interactive computing (like Jupyter or Google Colab)
- Cloud-based databases and frameworks like Microsoft Azure and Google Cloud
- Digital libraries that use prefabricated chunks of code used in machine learning projects
- Data visualization tools like Tableau, Microsoft Power BI, and Google Charts
- Open-source version control systems for machine learning projects (like Git or R)
Do Data Scientist Requirements Vary by Role?
There may be a core data scientist skill set, but requirements can vary by role or industry. For example, you may need investing or banking experience to work for an asset management company. Or you may need an advanced degree (like a Ph.D.) to specialize in research for academic journals.
As you start down the data science career path, think about where you want to work. Is there a specific company or industry you’re interested in? Reviewing job descriptions is one of the easiest ways to see what employers expect from different roles.
Some common paths include:
- Data quality analyst
- Machine learning scientist
- Product data scientist
- Cybersecurity data scientist
- Data visualization expert
- Data engineer
- Data mining experts
- Analytics data scientist
Pay can vary widely across data science roles and industries, too. For instance, data scientists specializing in computer systems design earn a $117,800 mean annual wage. It’s $111,130 for those working in the insurance industry.
Deciding between a path as a data scientist or a data engineer? Learn the key differences here.
How to Build Data Science Skills With or Without a Degree
A bachelor’s degree (or higher) in statistics, computer science, engineering, business, or mathematics can make it easier to become a data scientist. But it’s not the only path. Some employers hire candidates with informal education (like a coding bootcamp).
With or without a degree, the best way to get your foot in the door is through real-world projects. Do this via volunteer or freelance work. You can also gain experience by working with public datasets or government open-source data portals.
Create a portfolio showcasing your work. GitHub is a good place to host your projects and show how you collaborate and the impact you deliver.
How to Showcase Data Science Skills to an Employer
Social proof and real results can go a long way to getting an employer’s attention.
Start by including real-world experience in your portfolio. This could be from personal projects, volunteer work, or collaborative open-source projects. Again, GitHub is a good starting point. Creating online tutorials can also help.
Highlight any measurable outcomes from those projects. Whether you helped improve a system’s efficiency or accurately analyzed a financial trend, every little bit helps. Be sure to clearly indicate which skills or tools you used, since this shows hiring managers how you might apply yourself on the job.
Another way to showcase your skills (and how you think) is through short case-study write-ups. Include the code, tools, and overall process behind your work. A clear narrative shows how you think, something employers often value just as much as the final result.
And don’t forget your resume. Tailor it to each job description so the right data science skills rise to the top. Even when roles share common requirements, every employer has their own priorities.
Career Outlook
The data science career path typically follows a simple structure: junior roles, then mid-level positions, and eventually senior, lead, or machine learning–focused roles. Many data scientists start out in entry-level roles like data analyst or junior data scientist.
Still, everyone’s path is their own. Set goals for yourself and explore roles that match your interests.
You might find the path of a data analyst or a data engineer is perfect for you. Or you might opt for machine learning engineering or AI specialties. And if you realize a different path suits you better, don’t be afraid to change directions.
Master the Foundations of Data Science and Grow Your Career
Data scientists rely on a mix of statistics, programming, data analysis and visualization, machine learning, and interpersonal skills. With a balanced toolkit, it’s possible to launch a highly rewarding—and lucrative—career.
Since there are so many specialties to choose from, take the time to explore your options. It’s a fast-growing, fast-moving field. Staying ahead of the curve means continually learning through a combination of educational programs (including workshops or bootcamps), volunteer work and internships, and real-world projects.
Ready to take the next step in your career? Explore Intuit’s data science jobs and find the opportunity that matches where you want to grow.
FAQs
How can data scientists effectively showcase their skills on a resume?
Set up your resume to highlight your most relevant skills, education, and experience. This doesn’t have to be limited to paid work. Anything that helps validate your data science skill set and achievements goes a long way. Use the individual job description to customize your resume accordingly.
Do data scientists need a degree to succeed
Employers typically look for at least a bachelor’s degree. So, if you’ve got one in a field like computer science, mathematics, or statistics, you’re well on your way. But even without a degree, it’s possible to get hired with enough real-world projects and experience.
Should data scientists specialize or can they succeed as generalists?
It depends on the individual. Data scientists usually start with the same fundamental skills. Becoming a generalist can be a great way to get started as you explore different opportunities. But there’s nothing to prevent you from choosing a niche to specialize in later on.