Difference between Data Science and Machine Learning
- 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 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.
- 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.
- 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 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 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.