Data science and machine learning are two concepts that fall within the field of technology and using data to further how we create and innovate products, services, infrastructural systems, and more. Both correspond with career paths that are in-demand and high-earning.
The two relate to each other in a similar way that squares are rectangles, but rectangles are not squares. Data science is the all-encompassing rectangle, while machine learning is a square that is its own entity. They are both often used by data scientists in their work and are rapidly being adopted by nearly every industry.
Pursuing a career in either field can deliver high returns. According to US News, data scientists ranked as third-best among technology jobs, while a machine learning engineer was named the best job in 2019 [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers.
Read on to learn the difference between data science and machine learning.
Data science vs. machine learning: What’s the difference?
Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions. Machine learning is a branch of artificial intelligence.
In recent years, machine learning and artificial intelligence (AI) have dominated parts of data science, playing a critical role in data analytics and business intelligence. Machine learning automates the process of data analysis and goes further to make predictions based on collecting and analyzing large amounts of data on certain populations. Models and algorithms are built to make this happen.
What is data science?
Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured data. This knowledge gets applied to business, government, and other industries to drive profits, innovate products and services, build better infrastructure and public systems, and more.
To gain a better understanding of data science, watch this video:
Read more: What is Data Science?
To build a career in data science, such as becoming a data scientist, you’ll want to gain programming and data analytics skills.
Strong knowledge of programming languages Python, R, SAS, and more
Familiarity working with large amounts of structured and unstructured data
Comfortable with processing and analyzing data for business needs
Understanding of math, statistics, and probability
Data visualization and data wrangling skills
Knowledge of machine learning algorithms and models
Good communication and teamwork skills
I liked that the [IBM Data Science Professional Certificate] had introductory courses covering a wide range of topics with practical assignments, engaging and clear video lectures, and easy-to-understand explanations … this program strengthened my portfolio and helped me in my career.
— Mo R.
Careers in data science
Besides the obvious career as a data scientist, there are plenty of other data science jobs to choose from.
Data scientist: Uses data to understand and explain the phenomena around them, to help organizations make better decisions.
Data analyst: Gathers, cleans, and studies data sets to help solve business problems.
Data engineer: Build systems that collect, manage, and transform raw data into information for business analysts and data scientists.
Data architect: Reviews and analyzes an organization’s data infrastructure to plan databases and implement solutions to store and manage data.
Business intelligence analyst: Gathers, cleans, and analyzes sales and customer data, interprets it, and shares findings with business teams.
Read more: Your Guide to Data Science Careers (+ How to Get Started)
What is machine learning?
Machine learning is a branch of artificial intelligence that uses algorithms to extract data and then predict future trends. Software is programmed with models that allow engineers to conduct statistical analysis to understand patterns in the data.
As an example, we all know that social media platforms like Facebook, Twitter, Instagram, YouTube, and TikTok gather users’ information. Based on previous behavior, it it predicts interests and needs, and recommends products, services, or articles that are relevant to what you’ve searched before.
As a set of tools and concepts, machine learning is applied in data science, but also appears in fields beyond it. Data scientists often incorporate machine learning in their work where appropriate to help gather more information faster or to assist with trends analysis.
Read more: How Much Does a Machine Learning Engineer Make?
To become a successful machine learning engineer, you’ll need to be well-versed in the following:
Expertise in computer science, including data structures, algorithms, and architecture
Strong understanding of statistics and probability
Knowledge of software engineering and systems design
Programming knowledge, such as Python, R, and more
Ability to conduct data modeling and analysis
Read more: Machine Learning Skills: Your Guide to Getting Started
Careers in machine learning
If you decide to pursue a career in machine learning and artificial intelligence, there are several options to choose from.
Machine learning engineer: Researches, builds, and designs the AI responsible for machine learning, and maintaining or improving AI systems
AI engineer: Build AI development and production infrastructure, and then implements it
Cloud engineer: Builds and maintains cloud infrastructure
Computational linguist: Develop and design computers that deal with how human language works
Get started in data science
Whether you decide to pursue data science or machine learning, you’ll need technical skills in programming and statistics to land a job. IBM’s Data Science professional certificate is designed to help you land a job as a data scientist or related career. No degree or experience is required. Start today and you could earn your certificate in 11 months or less.