Methods for Data Analysis

This course is designed to develop an understanding of core statistical and machine learning techniques which provide the theoretical tools data scientists use to generate knowledge. Students will be asked to apply the course content to real-life scenarios and think creatively as well as critically through issues. By the end of the class, students will be able to apply statistics and machine learning techniques to data, and interpret and communicate their results. Feedback from guest speakers or program graduates about the current state of data science and the job market.

• Entity Resolution
• Inferential Statistics
• Gaussian Distributions, Other Distributions and The Central Limit Theorem
• Testing and Experimental Design
• Bayesian vs. Classical Statistics
• Probabilistic Interpretation of Linear Regression, and Maximum Likelihood
• Graph Algorithms
• Raw Data to Inference Model

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