Credits awarded on transcript
Precalculus completed with B- or better
UC A-G approved for [D] Science credits
90 minutes per class
8-10 students per class
Twice per week over 36 weeks
1249 per student, per semester
Self paced instructor-guided
Office hours on-demand
879 per student, per semester
4 hours per day (summer)
8-10 students per class
5 days per week 2, 4, or 6 weeks
489 per student, per week
Decisions that used to be straightforward are increasingly more complex and driven by data. Individuals across all disciplines need to constantly separate fact from friction. The need to analyze and interpret data has permeated every discipline — across engineering, business, finance, social sciences, humanities, and even journalism. Several leading academics now agree that the mathematics we teach in high school is rooted in the 1950s space race and needs to be updated to reflect the realities of the digital and information age of today.
2Sigma School takes an interactive approach to data exploration, rather than a lecture based approach. Our classes are hands-on and use several tools that are used by leading data scientists as well as higher education universities, as illustrated by the following video clip of a live session in a small cohort.
The Advanced Data Science is equivalent to a 1-semester college level course, adapted for high school. It combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand the phenomenon and draw conclusions? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.
Data science is more than just a combination of programming and statistics. Effective data science requires understanding problem domains and correctly interpreting domain-specific approaches. The examples in this course are largely drawn from real-world data sets, and one of the main goals of this course is to develop the ability to apply analysis and prediction techniques to real-world scenarios.
This is an advanced course meant for students who have experience with Python programming or who have previously taken the AP Computer Science A class. At the end of the course, students will have a portfolio of their data science work to showcase their newly developed knowledge and understanding.
In order to maximize our time together during the live sessions, we use a flipped classroom model that includes pre-work for every class. This allows students to program with the support of an instructor during the class. The pre-work includes pre-recorded videos, online reading, and some programming practice.
University of California A-G approved for recommended third year of [D] Science credits.
Our technology requirements are similar to that of most Online classes.
|A desktop or laptop computer running Windows (PC), Mac OS (Mac), or Chrome OS (Chromebook).|
|Students must be able to run a Zoom Client.|
|A working microphone, speaker, and webcam.|
|A high-speed internet connection with at least 10mbps download speed (check your Internet speed).|
Students must have a quiet place to study and participate in the class for the duration of the class. Some students may prefer a headset to isolate any background noise and help them focus in class.
Most course lectures and content may be viewed on mobile devices but programming assignments and certain quizzes require a desktop or laptop computer.
This course includes several timed tests where you will be asked to complete a given number of questions within a 1-3 hour time limit. These tests are designed to keep you competitively prepared but you can take them as often as you like. We do not proctor these exams, neither do we require that you install special lockdown browser.
In today's environment, when students have access to multiple devices, most attempts to avoid cheating in online exams are symbolic. Our exams are meant to encourage you to learn and push yourself using an honor system.
We do assign a grade at the end of the year based on a number of criteria which includes class participation, completion of assignments, and performance in the tests. We do not reveal the exact formula to minimize students' incentive to optimize for a higher grade.
We believe that your grade in the course should reflect how well you have learnt the skills, and a couple of timed-tests, while traditional, aren't the best way to evaluate your learning.