During each 8 week session, the Fellowship runs simultaneous courses for novices and more advanced students. Both courses follow a similar schedule:

• One 90-minute virtual (Zoom) workshop on a weekday for discussing new material and introducing that week’s tutorial

• One 3-hour co-working session on the weekend for practical hands-on learning through group collaboration and one-on-one assistance from mentors. These sessions may be shared by multiple courses to facilitate the exchange of skills between a broader group of students.

Level 1 - Data Analytics:

This course covers the fundamentals of coding for data analysis and uses them to answer real world questions using publicly available datasets. No coding experience is expected.

• Explore the world of data science, AI and machine learning through case studies and discussions with working industry experts

• Learn the fundamentals of Python programming for data analysis, including data structures, conditional and loops, and how to use external libraries such as Pandas for processing data

• Visualize semi-structured data using Pandas and Matplotlib

• Independently identify and tackle real-world questions using data analysis

• Use time-series data to forecast future events

Level 2 - Machine Learning Engineering:

This course expands on prior data analysis skills using Python by tackling complex problems with larger and messier datasets. We then use a variety of machine learning methods to answer relevant questions from that data. Students are expected to have demonstrated experience using Python for data analysis or to have completed the Level 1 course before enrolling in Level 2.

• Explore the intersection of data science, AI and machine learning with other disciplines through case studies and discussions with working industry experts

• Become proficient using Python for data analysis, following industry standards for best practices

• Learn the fundamentals of version control using Github

• Explore the mathematics and statistics behind common machine learning methods, and recognize when and how to use them

• Train and customize deep learning models (neural networks) to answer specific questions

• Create self-contained machine learning models that can be deployed within apps

• Discuss the techniques behind popular AI tools such as ChatGPT and build your own at small scales