Introduction to Data Science

Introduction to Data Science

• Data Science is the discipline that deals with the collection, processing, management, analysis, interpretation, and visualization of large, heterogeneous, and complex datasets.
• The goals are extracting non-obvious and useful information, knowledge from big data, in order to improve scientific, social and business decision making.
• Data Scientist's goal is to solve a scientific or business problem, not just analyze the data or build a predictive model.

The Value of Data

• Data is the raw material of science and business.
• Data leads to evidence, understanding, and progress.

Applications of Data Science

• Recommendation Systems
• Autonomous Vehicle Control/Robotics
• Personalized Medicine/Genomics
• Personal Assistants/Voice Recognition

Data Science Skill Set

• Data Science = Data Analytical Thinking + Automation

Data Scientist

• Responsible for guiding the Data Science Project from start to end.
• Requires quantifiable goals, good methodologies, cross-disciplinary interactions, and a repeatable workflow.

Introduction to Data Science

• Data science deals with collecting, processing, managing, analyzing, and visualizing complex, large, and heterogeneous datasets.
• Data science aims to extract useful, non-obvious information and knowledge from large volumes of data to improve decision-making in scientific, social, and business contexts.
• Data science combines data analytical thinking and automation.

The Value of Data

• Data is viewed as the raw material of science and business.
• Data leads to evidence, understanding, and progress.

Data Examples

• Human genome sequencing produces ~100 GB per genome, with ~1 million genomes expected to be sequenced.
• Credit card transactions number in the billions per year.
• Smartphone applications generate 300 hours of video every minute and host over 40 billion photos.
• The amount of digital information increases tenfold every five years.

Applications of Data Science

• Autonomous vehicle control and robotics
• Recommendation systems
• Personalized medicine and genomics
• Personal assistants and voice recognition

Data Science Roles

• The data scientist is responsible for leading the data science project from start to finish.
• Success requires quantifiable goals, good methodology, cross-discipline interaction, and repeatable workflow.
• The project may include a data engineer, data analyst, data visualization specialist, statistician, and machine learning specialist.

This quiz explores the fundamental concepts of data science, including its goals, applications, and the essential skill set required for data scientists. Delve into how data drives evidence and decision-making in scientific and business fields, along with real-world applications such as recommendation systems and personalized medicine.