1. Computer Science for Business Professionals
What you'll learn
- Computational thinking
- Programming languages
- Internet technologies
- Web Development
- Technology stacks
- Cloud computing
2. Understanding Technology
What you'll learn
- Internet
- Multimedia
- Security
- Web Development
- Programming
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
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
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
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
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
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
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
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
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
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
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
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.
15. Web Programming with Python and JavaScript
What you'll learn
- HTML, CSS
- JavaScript
- Python
- Django
- SQL, Models, and Migrations
- Git
Top comments (18)
Thx for sharing, past year, were twice IT Courses from Harvard EdX, unfortunate i lost my account and his courses, now im Happy for this, Aprecciate man!
Thanks :)
Thanks for sharing!
Thanks :)
thanks for sharing these courses i have enrolled in the first 2 courses hope these courses will improve my business skills as well as programming!
Thanks :) Sure it will improve your skills All the best
Thank you so much for this!
Thanks :)
Awesome..!
Thanks :)
So many courses, crazy! Thanks for sharing!
Thanks :) All the best ;)
Thanks for sharing! :)
Thanks :)
thanks
Thanks :)
So many amazing topics to learn thank you!
Thanks :)