Key areas of data science modeling including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science. These include algorithms for statistical models and machine learning methods including regression, classification, neural networks, and clustering; principles behind creating informative data visualizations; and statistical concepts of measurement error and prediction.
Key areas of data science modeling including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science. These include algorithms for statistical models and machine learning methods including regression, classification, neural networks, and clustering; principles behind creating informative data visualizations; and statistical concepts of measurement error and prediction.
