Data Science
For Teachers
Professional Development
June 23-27 9am-1pm, Pacific Time, Live & Online
For all teachers and educators interested in Data Science
Hands-on training in Python Programming, Data Analytics, Machine Learning
Receive free 4K Video curriculum with slides, coding notebooks, problem sets, project guidelines
Attend live lectures and small cohorts
Submit a final project that tells a data story with Python code.
What you’ll gain
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A full curriculum including 4k video playlists on youtube with links that students can watch on their own that cover everything.
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Access to all coding notebooks, problem sets, project guidelines, lesson guides, and slides in Google Drive.
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A Data Science Educator’s certificate of completion recommending 3 credits of PD as certified by Berkeley Coding Academy.
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A portfolio project reviewed by BCA staff that features Python code and machine learning to tell a data story.
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Confidence solving problems in Python and applying Python to navigate the real world of big data.
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An understanding of machine learning, the code behind AI, and the ability to teach this material to others.
What’s Covered
Module 1 - Python Programming
Gain proficiency coding in Python. With a focus on Python functions, solve problems in computer science using types, lists, loops, conditionals, dictionaries, and more.
Module 2 - Data Analytics
Analyze real data using the pandas, seaborn, matplotlib, and numpy libraries. Build professional data visualizations from scratch using Python and prepare data for machine learning.
Module 3 - Machine Learning
Make predictions from real data using machine learning algorithms in sklearn. Linear Regression, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, and XGBoost are included.
Lead Instructor
Berkeley Coding Academy program director Corey Wade has been teaching Data Science and AI for 5+ years. He teaches Data Science and Intro to Programming at the Independent Study Program of Berkeley High School during the school year, and Data Science: The AI Journey at Berkeley Coding Academy during the summer. His lessons in Python, Data Science, and Machine Learning have been published in articles and books including The Python Workshop, and Hands-on Gradient Boosting with XGBoost and Scikit-Learn.
First and foremost, Corey is a classroom teacher. He has taught for over 20 years in the Berkeley Unified School District, primarily in mathematics. He went back to school to obtain a Data Science certificate at Springboard and worked in industry developing a data science community college curriculum with Pathstream before returning to the classroom to teach full-time.
Corey is excited to empower teachers so that more students can learn the code behind AI. He envisions a world in which everyone gains competency in programming and works together to direct AI for the benefit of humanity.
✺ How It Works ✺
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Each session starts with an interactive lecture where we code together. Lectures are not passive watching experiences, but proactive coding experiences. New concepts are presented during lectures 3 times per day.
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Participants apply what they have learned in cohorts. For Python Programming, participants work on problem sets. For Data Science and Machine Learning, participants develop their own projects.
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Participants who want to go deeper into the curriculum (or to reinforce concepts) will be given optional 4K videos to watch. We have 4K videos for all concepts covered in the curriculum.
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During select days, we will discuss best practices for teaching this curriculum to middle school and high school students in a Q&A format.
Next Steps
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Review Syllabus
Our syllabus previews the learning outcomes and topics of study in the PD. We cover a lot of material in a short amount of time while still managing to go in-depth.
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Watch Video
The following video provides an overview of how we teach students over the summer. Watch this to get a better sense of what and how we teach.
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Zoom Info Session
We are offering an info session for teachers/educators on the first Thursday in May. Fill out the following Google Form to join us on 5.01 5:30-6:00PM PT.
✺ Frequently asked questions ✺
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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.
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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.
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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.
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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.
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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.
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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.