Difference between Data Science and Machine Learning

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 and unstructured data.
  • That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services and public systems, and more.

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 patterns In the data.
  • 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

Data Science

  • Strong knowledge of programming languages like Python, R, SAS, and more.
  • Familiarity working with large amount of structure 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.

Machine Learning

  • 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 modelling and analysis.


Data Science

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 analyses an organization’s data infrastructure to plan databases and implement solutions to store and manage data.

Business intelligence analyst: gathers, cleans, and analyses sales and customer data, interprets it, and shares findings with business teams.

Machine Learning

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 implement it.

Cloud engineer: Builds and maintain cloud infrastructure.

Computational linguist: develop and design computers and deal with how human language works.

Human-centered AI systems designer: design, develop, and deploy systems that can learn and adapt with humans to improve systems and society.

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