SC/MATH 2130 (3.00)

Principles and Techniques of Data Science

Description

This course is a continuation of SC/MATH 1130 3.00. This intermediate-level class bridges SC/MATH 1130 3.00 and upper-year ITEC, EECS, and statistics courses. In this class, we continuously explore key areas of data science, including question formulation, sampling design, experimental design, data collection, data cleaning, EDA, SQL, visualization, linear regression model, machine learning models & cross-validation, feature engineering, and time series analysis. This class covers key principles and techniques of data science through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis. These include languages for transforming, querying, and analyzing data; algorithms for machine learning methods including regression; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction. Through real data science case studies, students will gain hands-on familiarity with common data science tools, particularly Python and SQL. Prerequisite: SC/MATH 1130 3.00; SC/MATH 1131 3.00; SC/MATH 2030 3.00; SC/MATH 2015 3.00 or SC/MATH 2310 3.00.

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