1. Computer Science for Business Professionals

What you'll learn

• Computational thinking
• Programming languages
• Internet technologies
• Web Development
• Technology stacks
• Cloud computing

Link

2. Understanding Technology

What you'll learn

• Internet
• Multimedia
• Security
• Web Development
• Programming

Link

3. Introduction to Computer Science

What you'll learn

• A broad and robust understanding of computer science and programming
• How to think algorithmically and solve programming problems efficiently
• Concepts like abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development
• Familiarity with a number of languages, including C, Python, SQL, and JavaScript plus CSS and HTML
• How to engage with a vibrant community of like-minded learners from all levels of experience
• How to develop and present a final programming project to your peers

Link

4. Computer Science for Lawyers

What you'll learn

• Challenges at the Intersection of Law and Technology
• Computational Thinking
• Programming Languages
• Algorithms, Data Structures
• Cryptography
• Cybersecurity

Link

5. Designing Organizational Structure

What you'll learn

• Explore how to build an effective organization that motivates employees to pursue your vision
• Identify the tools you have at your disposal to enable your organization to create and deliver value and compete in the marketplace Cultivate leadership skills you can create and deliver value and compete in the marketplace
• Cultivate leadership skills you can apply to your work

Link

6. Fundamentals of TinyML

What you'll learn

• Fundamentals of Machine Learning (ML)
• Fundamentals of Deep Learning
• How to gather data for ML
• How to train and deploy ML models
• Understanding embedded ML
• Responsible AI Design

Link

7. Applications of TinyML

What you'll learn

• The code behind some of the most widely used applications of TinyML
• Real-word industry applications of TinyML
• Principles of Keyword Spotting and Visual Wake Words
• Concept of Anomaly Detection
• Principles of Dataset Engineering
• Responsible AI Development

Link

8. Deploying TinyM

What you'll learn

• An understanding of the hardware of a microcontroller-based device
• A review of the software behind a microcontroller-based device
• How to program your own TinyML device
• How to write code and deploy to a microcontroller-based device
• How to train a microcontroller-based device
• Responsible AI Deployment

Link

9. Introduction to Probability

What you'll learn

• How to think about uncertainty and randomness
• How to make good predictions
• The story approach to understanding random variables
• Common probability distributions used in statistics and data science
• How to use conditional probability to approach complicated problems

Link

10. High-Dimensional Data Analysis

What you'll learn

• Mathematical Distance
• Dimension Reduction
• Singular Value Decomposition and Principal Component Analysis
• Multiple Dimensional Scaling Plots
• Factor Analysis
• Dealing with Batch Effects

Link

11. Statistical inference and Modeling

What you'll learn

• Organizing high throughput data
• Multiple comparison problem
• Family Wide Error Rates
• False Discovery Rate
• Error Rate Control procedures
• Bonferroni Correction

Link

12. Introduction to Artificial Intelligence with Python

What you'll learn

• Graph search algorithms
• Reinforcement learning
• Artificial intelligence principles
• Machine learning
• How to design intelligent systems
• How to use AI in Python programs

Link

13. Introduction to Programming with Python

What you'll learn

• functions, arguments, return values
• variables, types, exceptions
• conditionals, Boolean expressions
• loops
• objects, methods
• file I/O, libraries

Link

14. Principles, Statistical and Computational Tools

What you'll learn

• Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
• Fundamentals of reproducible science using case studies that illustrate various practices.
• Key elements for ensuring data provenance and reproducible experimental design.
• Statistical methods for reproducible data analysis.
• Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.
• How to develop new methods and tools for reproducible research and reporting, and how to write your own reproducible paper.

Link

15. Web Programming with Python and JavaScript

What you'll learn

• HTML, CSS
• JavaScript
• Python
• Django
• SQL, Models, and Migrations
• Git

Link

Connect with Me 😊

🔗 Links