Lesson 4.2 Data Science and Artificial Intelligence
Lesson 4.2 Data Science and Artificial Intelligence
What is Data Science & Why It Matters
Data Science In 5 Minutes from Simplilearn
Watch above video and answer the following questions.
Assign the questions to students before they start watching to help them focus on key concepts.
Questions:
- What are the key job titles in data science and their related roles?
- What are the main steps in a data scientist’s workflow?
- What is a business problem in the context of data science?
- What is data acquisition, and why is it important?
- What does data preparation involve?
- What is data cleaning, and how does it improve data quality?
- What is data transformation, and when is it needed?
- What are the key steps involved in data analysis?
- What is data modeling?
- How are visualization and communication used to present data insights effectively?
☑️What is data science?
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
☑️What’s the difference between data science and artificial intelligence?
Both data science and artificial intelligence (AI) are umbrella terms for methods and techniques related to understanding and using digital data. Modern organizations collect information from a range of online and physical systems on every aspect of human life. We have text, audio, video, and image data available in large quantities. Data science combines statistical tools, methods, and technology to generate meaning from data. Artificial Intelligence takes this one step further and uses the data to solve cognitive problems commonly associated with human intelligence, such as learning, pattern recognition, and human-like expression. It is a collection of complex algorithms that "learn" as they go, becoming better at solving problems over time.