Do you enjoy making sense of numbers, using statistics, and creating visualizations of data? If so, a career in data science is likely a good fit for you. Becoming a data scientist is a smart career move, too: data scientists in the US earn an average salary of over $150,000 per year, and the US Bureau of Labor Statistics expects demand for this role to grow 35 percent by 2032. At the Big Five tech companies—Google, Meta, Amazon, Apple, and Microsoft—data science skills are among the most in-demand skills for their open technical roles.
To break into this lucrative career path, you’ll need to develop the key skills that data science roles today require. These include skills in mathematics and statistics, computer programming, and machine learning (ML), to start. Mastering these skills can be challenging, but it is entirely possible—even if you don’t have a background in math or computer science. In fact, CodeSignal has helped thousands of learners with no technical background build their skills in data science.
This guide will walk you through what it takes to start a career in data science, regardless of your background or experience. We’ll dive into common roles in data science (and how much they pay), skills you need for a data science career, different routes into data science, and more. Let’s get started.
Jump to a section:
- What is data science? Definition, roles, and job outlook
- Useful skills for breaking into data science
- 6 routes for breaking into data science in 2024
- Frequently asked questions
- Accelerate your data science career with CodeSignal Learn
What is data science? Definition, roles, and job outlook
Data science is an interdisciplinary field that uses scientific methods, statistics, and even machine learning to extract knowledge and insights from data. Many types of industries hire data scientists, data analysts, and other related roles to analyze data to drive business decisions.
What does a data scientist do?
A data scientist operates at the intersection of statistics, computer science, and domain expertise to extract actionable insights from data. They employ a variety of tools and methodologies, such as machine learning algorithms, data visualization techniques, and predictive modeling, to analyze complex datasets and solve challenging problems.
A data scientist’s work often involves cleaning and preparing data, performing exploratory data analysis, building and validating predictive models, and communicating findings to stakeholders in an accessible manner. They play a crucial role in decision-making processes across various sectors by turning vast amounts of raw data into strategic insights that can drive business outcomes, enhance customer experiences, and more.
What qualifications do you typically need to become a data scientist?
A majority of data scientists today hold at least a bachelor’s degree. A 2023 study found that 91 percent of data scientists have an undergraduate or more advanced degree. However, earning a bachelor’s degree in a technical field—or even a non-technical field—is not the only path to becoming a data scientist.
Increasingly, employers are focusing on hiring candidates who have the right skills for the role, regardless of their educational background. Many data scientists have built these skills through online courses, bootcamps, and self-directed portfolio projects.
Understanding the different types of data science roles and responsibilities
Within the field of data science, there are a variety of roles with varying levels of seniority and specialization. Three of the most common are data analysts, data scientists, and machine learning (ML) scientists.
Data analyst
A data analyst is responsible for collecting, processing, cleaning, and analyzing large sets of data to extract meaningful insights and inform decision-making within an organization. They utilize statistical analysis (in a tool like SAS, or a programming language like Python) and data visualization tools (Tableau, Looker, and others) to identify trends, patterns, and correlations in data.
Data analysts also play an important role in creating reports and presentations to communicate their findings to stakeholders, aiding in strategic planning and problem-solving. They often collaborate cross-functionally to ensure data integrity and optimize data-driven solutions for various business challenges.
The average salary for a Data Analyst in the US is $89,313 per year, according to recent data from Glassdoor.
Data scientist
Compared to a data analyst, a data scientist delves deeper into data exploration and analysis by using advanced statistical techniques and predictive modeling to extract actionable insights. They harness ML and AI algorithms to develop sophisticated models that can forecast trends and behaviors.
Data scientists generally possess strong programming and scripting skills, often utilizing languages like Python or R to manipulate data and build complex algorithms. Often, they also have skills in big data technologies like Hadoop and Spark that allow them to efficiently process and analyze large datasets. Their skills in data visualization and communication allow them to effectively translate complex findings into accessible insights for stakeholders.
The average salary for a Data Scientist in the US is $154,655 per year, according to recent data from Glassdoor.
ML scientist
Within a data science team, a machine learning (ML) scientist specializes in designing and implementing ML models to solve complex problems—often related to analyzing large and complex datasets. They are adept at utilizing deep learning techniques and neural networks to perform tasks like image recognition, natural language processing (NLP), and recommendation systems.
ML scientists engage in experimentation and A/B testing to fine-tune models and optimize performance. Their expertise in advanced programming languages like Python, as well as frameworks like TensorFlow and PyTorch, enables them to develop and deploy scalable and efficient algorithms. ML scientists also contribute to ongoing research and development in AI technologies to help their organizations best leverage and advance AI and ML innovation.
The average salary for a Machine Learning Scientist in the US is $216,172 per year, according to recent data from Glassdoor.
Useful skills for breaking into data science
There are a few key skill areas that are essential for a variety of data science roles, whether that’s a data analyst, data scientist, or another related role. Here are a few to get you started.
Mathematics
Mathematics, and especially statistics, like at the heart of data science. To become a data scientist, you’ll need a strong foundation in statistical analysis—skills like hypothesis testing, regression analysis, and statistical inference—to interpret data and make accurate predictions. You will also need to understand the theory behind statistics to make sense of data patterns, choose the right statistical tests, and handle missing data appropriately, for instance.
With the rise of ML in data science roles, data scientists should also have a strong understanding of linear algebra, which underpins ML algorithms and data transformations. You may want to build proficiency in calculus and optimization techniques, which can allow you to better develop and fine-tune models.
What if I’m bad at math?
If math isn’t your strong suit, don’t let that deter you from pursuing data science. Many resources are available to help you strengthen your math skills—from online courses to blogs and podcasts tailored to beginners. Data science also involves applying math in practical contexts, which can make learning more intuitive. Focus on foundational concepts in statistics and linear algebra, and practice applying them in data science projects to build your confidence and proficiency.
Analytical skills
In addition to math skills, a data scientist needs strong analytical and critical thinking skills for effective data interpretation, insights extraction, and problem-solving. You’ll want to become adept at hypothesis testing and experimentation so you can validate analyses and conclusions. These skills will allow you to help your organization make informed and strategic choices based on data insights.
Machine learning and deep learning
In the domain of data science, ML skills are crucial for extracting deeper insights from data. If you’re looking to become a data scientist or an ML scientist, you should master supervised and unsupervised learning algorithms, be proficient in neural networks, and use deep learning frameworks such as TensorFlow and Keras. You’ll also need skills in model evaluation, tuning for optimal performance, and Natural Language Processing (NLP) for text analysis. These capabilities are essential for developing sophisticated models that can interpret and predict complex patterns in data.
Learning tip: Ready to take your data analysis skills to the next level? CodeSignal Learn’s Journey into Machine Learning with Sklearn and Tensorflow learning path, designed for individuals with basic Python skills, takes you from learning the fundamentals of ML to building and optimizing neural networks.
Data visualization
Data scientists need proficiency in designing effective charts and graphs; this can be done using Python libraries like Seaborn and Matplotlib, or with tools like Tableau or PowerBI that create interactive data visualizations and dashboards. An understanding of color theory is also important in this context, as it influences the perception and interpretation of visualizations, and can help highlight trends and distinctions in the data more intuitively. You’ll also want to hone your skills in data storytelling and presentation to effectively communicate the complex information being presented in the visualizations they create.
Programming
To embark on a data science career, you should have proficiency in at least one programming language: likely Python or R. This foundation enables you to leverage data manipulation and analysis libraries like pandas and NumPy effectively. While mastering one language is essential, familiarity with several can broaden your skill set and opportunities.
You will also want to be familiar with version control systems, particularly Git, for managing code changes and collaboration. Additionally, being comfortable with development environments and tools, including Jupyter notebooks and VS Code, will enhance the efficiency of your coding and data analysis.
Should I learn Python or R?
Choosing between Python and R depends on your specific data science goals. Python is versatile, with a vast library ecosystem for various applications, making it ideal for machine learning, web development, and automation. R, on the other hand, is tailored for statistical analysis and graphical models, preferred in academia and research. Consider your project needs and the industry you aim to join when deciding which to learn.
Learning tip: Totally new to Python? CodeSignal Learn’s Intro to Programming with Python learning path takes you from the basics of programming to using functions, debugging, and more using Python.
Tools and frameworks
Lastly, to become a data scientist, it’s important to learn tools and frameworks that handle large-scale data processing like Hadoop and Spark. Hadoop is essential for distributed storage and processing of big data, while Spark offers fast, in-memory data processing. Familiarity with these technologies will enable you to tackle complex data challenges effectively.
6 routes for breaking into data science in 2024
There is no one right way to start a career in data science. Each person’s path will vary based on their previous educational and work experiences. The path you choose should ensure you’re mastering job-relevant data science skills and building a portfolio of relevant projects.
Participating in online courses and bootcamps
Online courses or bootcamps are great ways to start your journey toward a career in data science, especially if your educational background isn’t directly related to the field. These programs are designed to bridge the gap between theoretical knowledge and practical skills, and provide you with hands-on experience in using data science tools. You’ll have the opportunity to tackle real-world problems through projects, which help you both develop skills and build a portfolio of data science projects.
What online courses should I take?
The best online courses in data science offer many opportunities for practice, provide one-on-one learning support, and focus on achieving skill mastery—not just completing a course.
CodeSignal Learn offers a range of free learning paths for launching or boosting a career in data science. Each path includes several courses and hundreds of practices—all supported by Cosmo, a built-in AI tutor and guide who provides personalized feedback and unblocks you when you get stuck. Here are a few learning paths to get you started:
- Introduction to Data Analysis with Python
- Journey into Data Science with Python
- Journey into Machine Learning with Sklearn and TensorFlow
Pursuing a formal education in data science
Earning a bachelor’s or master’s degree in data science or a related field is a common path for those aiming to become data scientists. These programs typically include coursework in statistics, machine learning, and programming, along with capstone projects that allow for practical application of skills.
Many data scientists earn degrees in other technical fields, like computer science, engineering, statistics, and mathematics; all of these help build the skills a student needs to later become a data scientist. If you are willing to spend a minimum of four years pursuing an education full-time, earning a bachelor’s degree in a data science-related field is one path you can take to pursue this career.
Building a strong portfolio of data science projects
Building a portfolio of real-world data science projects is another great step toward breaking into a career in data science. Developing projects that solve practical problems showcases your ability to apply data science skills effectively.
Consider participating in Kaggle competitions, or similar competitions, that can demonstrate your ability to tackle data science challenges. Whatever you produce, upload your projects to GitHub to both serve as a repository of your work and to demonstrate your familiarity with version control systems, an essential skill in the field.
What should I do as a project?
When selecting a project, choose a topic that genuinely interests you—this will keep you motivated and engaged. Consider solving a problem you’ve encountered personally, or something relevant to current events. Projects that involve cleaning and analyzing datasets, building predictive models, or even developing a tool or application that addresses a specific need can be particularly impactful.
Getting certified
Obtaining a certification in data science is another strategic way to kickstart your career in this field. Certifications from reputable institutions or platforms can provide you with a structured learning path that builds your skills in essential data science topics like analytics, machine learning, and programming with Python or R. These certifications validate your skills for potential employers, and they also ensure you have a strong foundational knowledge and hands-on experience with real-world data science projects.
CodeSignal Learn offers mastery certifications in data science and data analysis skills, which are recognized by top employers in the CodeSignal community.
Getting experience through data science internships
Securing a data science internship is a pivotal step in starting a career in this field. Internships offer you a hands-on experience where you can apply theoretical knowledge to real-world problems, all using core data science skills like data extraction and presenting insights. They also provide exposure to essential tools like SQL, Tableau, and Hadoop, and introduce you to adjacent technologies like machine learning and AI, which make you a more versatile candidate across various industries.
The value of these internships extends beyond skill development; they are a gateway to building a professional network with peers, mentors, and industry experts. This network can be invaluable in navigating the job market and identifying career opportunities—or even lead directly to job offers.
Frequently asked questions
Here are a few of the common questions asked by individuals aspiring to start a career in data science:
How can I get into data science with no experience?
Breaking into data science without prior experience is challenging, but entirely possible. Start by taking online data science courses which cover fundamental concepts and practical applications within the field. These courses often provide a comprehensive curriculum, including programming languages like Python or R, essential for data manipulation and analysis.
Then, take on personal projects or find an internship that can offer practical experience. These experiences both enhance your resume and provide tangible evidence of your capabilities to potential employers. Upload your projects to Github to showcase your work to potential employers. Lastly, remember that networking is a great way to break into data science. Connect with professionals in the field who can offer guidance—and maybe even connect you to job opportunities.
How to know if my degree is good for data science?
A wide range of degrees can equip you with the skills you need for a career in data science. Fields such as computer science, statistics, mathematics, and engineering are directly applicable, as they provide a strong technical background in programming and statistical analysis. However, degrees in the social sciences and even humanities can also prepare you with valuable skills in critical thinking, research methodology, and data interpretation.
How can I break into data science with an unrelated undergrad?
The key to breaking into data science if you have an undergraduate degree not related to data science is to supplement your education with data science-specific skills, such as machine learning, data visualization, and big data technologies. You can gain these skills through online courses, certifications, or self-study.
How can I identify what I need to learn and cover any knowledge gaps?
To identify what you need to learn and cover any knowledge gaps in data science, start by assessing the core competencies required in the field: statistical analysis, machine learning, programming languages (Python, R), and data visualization tools, to start. Review current job listings for data science roles to better understand what specific skills employers are looking for and how your skillset aligns; then, consider taking online courses to help you fill any gaps in your skills.
Accelerate your data science career with CodeSignal Learn
Starting a career in data science may not be easy, but it is possible—even if you don’t have a degree or work experience in the field. This guide has provided an overview of a range of data science roles and responsibilities, core skills you need for a career in data science, and six different routes you can take for breaking into the field.
A great place to get started is by completing the Journey into Data Science with Python learning path in CodeSignal Learn. This series of 7 courses and over 200 practice exercises will take you from the basics of using popular Python libraries for data science, to performing large-scale data manipulation, to building skills in supervised and unsupervised machine learning: all core skills for launching a career in data science. Start learning for free today.