Data Science vs Machine Learning: What’s the Difference?

Two concepts that are connected to technology and using data for the creation and innovation of goods, services, infrastructure systems, and more are data science and machine learning. Both align with highly attractive and in-demand job paths.

Similar to how squares are rectangles but rectangles are not squares, so too do the two relate to one another. Machine learning is a separate square, while data science is the comprehensive rectangle. Both are being quickly embraced by almost every business and are frequently used by data scientists in their job.

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.

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

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.

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.

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

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

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