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Coles AI and Data Literacy at Bayshore Shopping Centre in Ottawa, ON
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AI and Data Literacy is designed for independent learners or courses in data analytics, statistics, and artificial intelligence. The text integrates conceptual foundations with hands-on learning through applied labs, real-world datasets, and guided exercises. Intended for full-semester academic use, this edition supports students and instructors in developing practical AI and data literacy skills.
Learning resources are available from the publisher (www.datajoyai.com).
Introduction to data and AI basics
Types of data (structured and unstructured)
Data lifecycle and data quality
Data cleaning and metadata
Basics of R programming
Data storage formats (CSV, JSON, Parquet)
Databases, cloud storage, and APIs
Introduction to machine learning
Supervised and unsupervised learning
How AI learns from data
Real-world AI applications
Basic statistics and data analysis
Data visualization and charts
Outlier detection
Prompt engineering basics
Evaluating AI outputs
Human and AI collaboration
AI ethics and fairness
Privacy and responsible AI
AI risks like bias and misinformation
Hands-on labs and practical exercises
AI and Data Literacy is designed for independent learners or courses in data analytics, statistics, and artificial intelligence. The text integrates conceptual foundations with hands-on learning through applied labs, real-world datasets, and guided exercises. Intended for full-semester academic use, this edition supports students and instructors in developing practical AI and data literacy skills.
Learning resources are available from the publisher (www.datajoyai.com).
Introduction to data and AI basics
Types of data (structured and unstructured)
Data lifecycle and data quality
Data cleaning and metadata
Basics of R programming
Data storage formats (CSV, JSON, Parquet)
Databases, cloud storage, and APIs
Introduction to machine learning
Supervised and unsupervised learning
How AI learns from data
Real-world AI applications
Basic statistics and data analysis
Data visualization and charts
Outlier detection
Prompt engineering basics
Evaluating AI outputs
Human and AI collaboration
AI ethics and fairness
Privacy and responsible AI
AI risks like bias and misinformation
Hands-on labs and practical exercises
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