AI and Data Literacy for Business Professionals (2024)

Focused View

Shadi Balandeh, PhD

1:34:59

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1. Statistics Essentials

1.1 AI and Data Literacy Handbook-2024.pdf

1. Introduction.mp4

01:43

2. Descriptive Statistics Measures of Central Tendency and Variability.mp4

05:24

3. Descriptive Statistics Distributions.mp4

07:29

4. Inferential Statistics Sampling Techniques.mp4

04:44

5. Inferential Statistics Hypothesis Testing.mp4

04:42

6. Inferential Statistics Correlation.mp4

04:32

7. Quiz Statistics Essentials.html

2. AI Essentials

1.1 AI and Data Literacy Handbook-2024.pdf

1. Introduction.mp4

01:13

2.1 Image credits .pdf

2. History of AI.mp4

06:21

3. Supervised vs Unsupervised Machine Learning.mp4

03:49

4. Train, Validation, Test Datasets.mp4

02:29

5. Overfitting and Underfitting (Bias & Variance).mp4

05:21

6. Quiz AI Essentials.html

3. Evaluation Metrics for ML Models

1.1 AI and Data Literacy Handbook-2024.pdf

1. Introduction.mp4

00:32

2. Evaluation Metrics for Classification Models.mp4

09:06

3. Evaluation Metrics for Regression Models.mp4

05:32

4. Quiz Evaluation Metrics for Machine Learning Models.html

4. Advanced Topics in AI

1.1 AI and Data Literacy Handbook-2024.pdf

1. Introduction.mp4

00:52

2. From Neural Networks to Large Language Models.mp4

03:32

3. Generative AI.mp4

01:52

4. Reinforcement Learning.mp4

03:11

5. Responsible AI.mp4

05:42

6. AI Rapid Fire.mp4

06:10

7. Quiz More Advanced Topics in AI.html

5. Data Visualization Literacy Essentials

1.1 AI and Data Literacy Handbook-2024.pdf

1. Introduction.mp4

00:48

2. Chart Types.mp4

04:57

3. Data Visualizations Checklist.mp4

04:58

4. Quiz Data Visualization Literacy Essentials.html

6. Final Exam

1. Final Test.html

Description

Data and Artificial Intelligence Literacy - From Core ML and Statistical Concepts to the Latest in AI in Simple Terms

What You'll 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 is this for?

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

What You Need to Know?

No prerequisites

More details

Description
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 topics like Hypothesis Testing, Sampling Techniques, Distributions, Machine Learning Evaluation Metrics, and more.Â 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 â€“ data and AI.
Who this course is for:
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

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I'm a Data Science Manager with a PhD in Physics, and my passion lies in harnessing the power of data to make smart, effective decisions. I am a physicist turned data scientist who now leads a team of data scientists at a large Canadian company (all opinions are my own). I advocate for data science literacy for everyone, so that we can all engage in an educated conversation around data and AI.

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