AI and Data Literacy for Business Professionals (2024)
AI and Data Literacy for Business Professionals (2024), available at $44.99, has an average rating of 4.5, with 25 lectures, 7 quizzes, based on 60 reviews, and has 1594 subscribers.
You will learn about Grasp the fundamental concepts of descriptive and inferential statistics through practical examples Learn the core concepts of AI and machine learning, such as the history of AI, different types of ML models, and much more (without detailed math or coding) Explore latest AI topics, such as the principles of responsible AI, Large Language Models, generative AI, and more, in a simple yet practical way Understand the main evaluation metrics for machine learning models, including their use cases and limitations. e.g How is "Accuracy" different than "Precision"? Learn how to consume data visualizations as an AI and Data-literate Business Professional. Learn the "Questions to Always Ask". Identify common cognitive biases and pitfalls that impact data-driven decision making Included is a 60-page data and literacy handbook, ensuring you can focus on learning without the need for extensive note-taking This course is ideal for individuals who are Business professionals without rigorous data science background aiming to enhance their AI and data literacy with the latest topics. or Example: Executives, Product Managers, Project Managers, Marketing Managers, Data Enthusiasts, Data Analysts, Educators and Trainers It is particularly useful for Business professionals without rigorous data science background aiming to enhance their AI and data literacy with the latest topics. or Example: Executives, Product Managers, Project Managers, Marketing Managers, Data Enthusiasts, Data Analysts, Educators and Trainers.
Enroll now: AI and Data Literacy for Business Professionals (2024)
Summary
Title: AI and Data Literacy for Business Professionals (2024)
Price: $44.99
Average Rating: 4.5
Number of Lectures: 25
Number of Quizzes: 7
Number of Published Lectures: 25
Number of Published Quizzes: 7
Number of Curriculum Items: 32
Number of Published Curriculum Objects: 32
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: CA$84.99
Quality Status: approved
Status: Live
What You Will Learn
- Grasp the fundamental concepts of descriptive and inferential statistics through practical examples
- Learn the core concepts of AI and machine learning, such as the history of AI, different types of ML models, and much more (without detailed math or coding)
- Explore latest AI topics, such as the principles of responsible AI, Large Language Models, generative AI, and more, in a simple yet practical way
- Understand the main evaluation metrics for machine learning models, including their use cases and limitations. e.g How is "Accuracy" different than "Precision"?
- Learn how to consume data visualizations as an AI and Data-literate Business Professional. Learn the "Questions to Always Ask".
- Identify common cognitive biases and pitfalls that impact data-driven decision making
- Included is a 60-page data and literacy handbook, ensuring you can focus on learning without the need for extensive note-taking
Who Should Attend
- Business professionals without rigorous data science background aiming to enhance their AI and data literacy with the latest topics.
- Example: Executives, Product Managers, Project Managers, Marketing Managers, Data Enthusiasts, Data Analysts, Educators and Trainers
Target Audiences
- Business professionals without rigorous data science background aiming to enhance their AI and data literacy with the latest topics.
- Example: Executives, Product Managers, Project Managers, Marketing Managers, Data Enthusiasts, Data Analysts, Educators and Trainers
Welcome to our Data and AI Literacy course, featuring 110 minutes of engaging content across 25 bite-sized videos, carefully crafted quizzes, and a complimentary 60-page AI and Data Literacy handbook ( so that you don’t have to take notes!)
This course is tailor-made for business professionals without a data science background, eager to enhance their AI and data literacy.
In today’s data-driven world, the ability to understand and leverage AI and data is no longer a luxury but a necessity for business professionals.
It covers foundational topicslike Hypothesis Testing, Sampling Techniques, Distributions, Machine Learning Evaluation Metrics, and more. Additionally, it addresses critical AI topics, including the principles of Responsible AI, Large Language Models, and more, ensuring you’re well-versed in the most current and essential aspects of AI.
This comprehensive approach empowers you to make informed decisions, be a valuable thought-partner to data teams, and maintain a competitive edge in the rapidly evolving job market landscape.
This course is packed with numerous practical examples to solidify your understanding, while intentionally avoiding complex math and coding, making it accessible and immediately applicable in your professional life.
Don’t miss this opportunity to become proficient in the languages of the today and the future – data and AI.
Course Curriculum
Chapter 1: Statistics Essentials
Lecture 1: Introduction
Lecture 2: Descriptive Statistics: Measures of Central Tendency and Variability
Lecture 3: Descriptive Statistics: Distributions
Lecture 4: Inferential Statistics: Sampling Techniques
Lecture 5: Inferential Statistics: Hypothesis Testing
Lecture 6: Inferential Statistics: Correlation
Chapter 2: AI Essentials
Lecture 1: Introduction
Lecture 2: History of AI
Lecture 3: Supervised vs Unsupervised Machine Learning
Lecture 4: Train, Validation, Test Datasets
Lecture 5: Overfitting and Underfitting (Bias & Variance)
Chapter 3: Evaluation Metrics for ML Models
Lecture 1: Introduction
Lecture 2: Evaluation Metrics for Classification Models
Lecture 3: Evaluation Metrics for Regression Models
Chapter 4: Advanced Topics in AI
Lecture 1: Introduction
Lecture 2: From Neural Networks to Large Language Models
Lecture 3: Generative AI
Lecture 4: Reinforcement Learning
Lecture 5: Responsible AI
Lecture 6: AI Rapid Fire
Chapter 5: Critical Thinking in Data-Driven Decision Making: Pitfalls and Cognitive Biases
Lecture 1: Five Pitfalls of Data-Driven Decision Making
Lecture 2: 12 Key Cognitive Biases in Decision-Making to Avoid
Chapter 6: Data Visualization Literacy Essentials
Lecture 1: Introduction
Lecture 2: Chart Types
Lecture 3: Data Visualizations Checklist
Chapter 7: Final Exam
Instructors
-
Shadi Balandeh, PhD
Data Science Leader
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 7 votes
- 4 stars: 12 votes
- 5 stars: 41 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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