Introduction to Data Science

9-12 graders

Credits awarded on transcript

Algebra I completed with B- or better

UC A-G approved for [C] Mathematics credits

Self paced instructor-guided

Personalized 1-1 support

Office hours 1 hour per week

1449 per student, per semester

90 minutes per class

4-8 students per class

Twice per week over 36 weeks

1449 per student, per semester

2 hours per day (summer)

8-10 students per class

4 days per week 2, 4, or 6 weeks

589 per student, per week

Data is everywhere around us. We generate more data every 40 minutes than all of the data generated since the dawn of civilization until 2003. The ability to work with data, understand what it tells us, and use it in your communication has become an essential life and career skill.

90%

of all the world's data was created in just the last two years

1000x

computing power in a smartphone vs. a 1970s mainframe computer

11%

of all U.S. high school students complete any statistics coursework

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.

This is a high-school level course that introduces students to the exciting opportunities available at the intersection of data analysis, computing, and mathematics. In this course students will learn to understand, ask questions of, and represent data through project-based units. The units will give students opportunities to be data explorers through active engagement, developing their understanding of data analysis, sampling, correlation/causation, bias and uncertainty, modeling with data, making and evaluating data-based arguments, and the importance of data in society. At the end of the course, students will have a portfolio of their data science work to showcase their newly developed knowledge and understanding.

This is a beginner course and no prior experience with programming is required. During the first half of the course we cover key programming concepts that include variables, data types, comparisons and boolean operators, functions, control structures, and iteration. We will be using industry standard tools like Jupyter Notebooks, Python, and Data Commons. Students will get the chance to explore data sets in areas that they are familiar with. The course ends with a capstone project where the student get to apply what they have learned and round out their portfolio of data science work to showcase their newly developed abilities.

Some key differences between a traditional statistics course and the data science course include:

Larger data sets (Big Data) that can only be analyzed programmatically vs small, tailored data sets.
Use of modern statistical analysis and simulation tools vs a formula-based approach.
Use of Python programming for data analysis vs pen and paper based computations.

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 [C] Mathematics credits. NCAA Approved.

Course Outline

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Data Tells a Story
Students are introduced to data science through a reflection of their own experiences using self-generated data, an exploration of a larger dataset of people’s media use, and an analysis of business data. Students will make sense of the questions: What part of the story is told by data? What is variation? How is data generated? Students will learn simple spreadsheets and will explore univariate, bivariate, and multivariate data.
Learning from Data Distributions
Students will learn ways of modeling data, starting with the basic models of measures of center and spread, as well as considering sampling. It takes a deeper dive into the concepts, limitations, and the impact of outliers. Students will explore distributions and the role of probability. Students will collect their own data and compare it to a larger data set.
Bivariate Data and Causality vs. Spurious Correlation
Students will learn about bivariate data through discussions and data explorations with several analytical tools. Students will explore scatter plots as a visual way to represent the relationship between two variables, draw their own lines of best fit, and learn how data scientists determine and analyze lines of best fit.
Probabilistic Modeling
Students will consider the modeling process and the role played by variation, reflecting on the data collected from simulations and the ways data can help answer probabilistic questions and leverage this power for decision-making. In the process of creating powerful simulations, students will learn the basics of data analysis in Python.
Categorical Data and Introduction to Linear Algebra
Students will collect and analyze categorical data. They will also delve into the use of vectors to organize data into a multi-dimensional space to understand how data are similar or different to each other. Students will work in spreadsheets and Python based Jupyter notebooks.
Modeling with Data and Understanding Bias
Students will build a prioritization model to create a ranking, based on what they value, collect variables based on their values, gather and clean data, create functions to combine variables, normalize data, and create a weighting system for prioritizing their data. Students will do a sensitivity analysis on their weighting system and learn how bias impacts mathematical models.
Introduction to Machine Learning
Students will gather and clean data to make predictive models. They will be introduced to machine learning and will use machine learning on the same data to make more efficient and accurate predictions.
Being a Data Scientist
Students will have an opportunity to work through the full cycle of data science: making their own decisions about the questions they are interested in exploring, finding data to answer that question, cleaning the data, creating and analyzing a model, communicating with the data visually and reflecting on their process. This will be an interactive process mirroring how data scientists work on a project.

Summer of Code
see detailed summer schedule

Week 1

Distribution

Models

Variability

The exploration of data starts with asking interesting questions. You will begin by analyzing the data in your daily life, understand variations like univariate, bivariate, and multivariate, and explore data distributions. What story does the data tell?

Week 2

Correlation

Sampling

Visualization

Model data using spreadsheets and understand basic measures of center and spread and learn about sampling. Explore visualizations such as scatter plots, best-fit lines, and understand the difference between causality vs spurious correlation.

Week 3

Modeling

Probability

Learn to transform data using tools such as Edu-Blocks and Python in a Jupyter notebook with basic programming. Collect and analyze experimental data and learn to simulate a basic probabilty model to learn about making real-life decisions from data.

Week 4

Clustering

Similarity

Vectors

Learn how concepts from linear algebra, such as vectors, are used to organize data into multi-dimensional space and gain a new understanding of data similarity. Plot vectors in one, two, and three dimensional space and learn about clustering data.

Week 5

Bias

Prediction

Ranking

Learn about bias in data and build a prioritization model to rank data. Use spreadsheets and data APIs to run sensitivity analysis, make predictive models, and build your own machine learning model using k-means clustering. What story can you tell?

Week 6

Capstone

You will work through the full data science cycle (Ask, Prepare, Process, Analyze, Share, Act) to answer a question that will positively influence you and your community. You will take action on the insights that you uncover through data analysis.

To take any of our courses, students must be familiar with opening a browser, navigating to a website, and joining a Zoom meeting.

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.

Students are required to have their camera on at all times during the class, unless they have an explicit exception approved by their parent or legal guardian.

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, webcam, and an external mouse.
	A high-speed internet connection with at least 15mbps download speed (check your Internet speed).

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.

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