PD for Teachers
FAQ
I’m interested in Data Science, but have never coded before. Is this PD for me?
Absolutely! No programming experience is expected. We actually love working with first-time coders. We provide regular check-ins to make sure that you are learning at the appropriate pace and provide 1-1 help where needed. Also, we teach teenagers, so we are used to working with students brand new to the material. We always provide smooth on-ramps for beginners.
I currently teach Data Science, but would like more training. Is this PD right for me?
This PD is ideal for any current data science teacher who wants to get better at programming in Python, creating data visualizations from scratch in Python (seaborn, matplotlib), and building and understanding machine learning models (sklearn). If you are yet not comfortable loading and analyze big data on your own to make meaningful predictions via machine learning, this PD is definitely for you.
What about math teachers?
Berkeley Coding Academy program director Corey Wade is a math teacher. If anything, the Python unit of our Code-First Data Science curriculum is a little slanted towards math. Additionally, data science is a subfield of statistics and therefore a subfield of math. Our PD program is an excellent launching point for all math teachers who want to go beyond the surface and begin to see data science and AI for what they really are.
How might this PD fit within a CS curriculum?
Our Python Programming unit can be used as an introduction to Python within a Data Science class, or it can be assumed as prerequisite if the majority of your students already know Python. Our Data Science: The AI Journey curriculum has been successful both as an introductory programming class, and for students who have already taken AP Computer Science. The common ground is that all students are new to navigating big data with the pandas, matplotlib, seaborn, and sklearn libraries. Our curriculum is a great elective to add to any high school computer science department.
Would this PD work for humanities teachers?
Yes! The integration of the humanities with data science is important to facilitate academic growth and new perspectives. As one example, machine learning models are used within various subjects to detect bias in text. The big advantage is that a machine learning model can scan through thousands of documents in a fraction of the time it would take a real human being. Social scientists use data science regularly to analyze relevant datasets connected to their fields. In English, the field of Natural Language Processing, a subfield of Data Science that includes large language models like ChatGPT, is hotter than ever and changing the landscape of education and the economy.
Will this curriculum work for Middle School?
At Berkeley Coding Academy, some of our most successful students have been at the middle school level. Students need to have a foundation in algebra and geometry, and be ready for abstract analytical thinking. Our first two units in Python Programming and Data Science should work well for the middle school level for most mathematically ready students. Our third unit, Machine Learning, requires more abstract thinking, and can work well for motivated students committed to the learning process.
How is this different than the YouCubed Data Science training?
The main difference is that all of our lessons are centered around Python code. Students who take our curriculum, and staff who teach it, become competent programmers. They all build machine learning models to make predictions from real data. Our curriculum include slides as secondary materials, not as primary learning materials.
What kind of credits will I receive?
All teachers who complete our program with a final project will receive a Berkeley Coding Academy Data Science Educators Certificate worth 4 units that highlights your ability to teach Python Programming, Data Analytics, and Machine Learning to students.