Data Science vs. Machine Learning: What’s the Difference?
What is the difference between data science and machine learning? Which potential career path is right for you? Find out more here.
What is Data Science& Machine learning
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. That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services, build better infrastructure and public systems, and more.
Skills needed
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
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.
Social media platforms like Facebook, Twitter, Instagram, YouTube, and TikTok gather users’ information. Based on previous behavior, it it predicts your interests and desires, and recommends products, services, or articles that are relevant to you.
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.
Skills needed
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