Introduction to Data Science Course

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.

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.