Foundations of Computational Data Analysis
Title  Foundations of Computational Data Analysis (53110) 

Quarter  Winter 2020 
Instructor  Amitabh Chaudhary (amitabh@cs.uchicago.edu) 
Website  
Syllabus  Foundations of Computational Data Analysis covers mathematical prerequisites for the Data Analytics Specialization courses in machine learning, and largescale data analytics (MPCS 53111 and 53112): basic statistics and linear algebra. Topics in statistics include discrete and continuous random variables, discrete and continuous probability distributions, variance, covariance, correlation, sampling and distribution of the mean and standard deviation of a sample, central limit theorem, confidence intervals, maximum likelihood estimators, and hypothesis testing. Topics in linear algebra include Gaussian elimination, matrix transpose and matrix inverse, eigenvectors and eigenvalues, and singular value decompositions. The languages Python and R will be used for implementation, analysis, and visualization. 
Prerequisites (Courses)  B+ or above in MPCS 50103 Math for Computer Science or passing the Discrete Math placement exam

Prerequisites (Other)  Core Programming; Univariate Calculus and Basic Multivariate Calculus (double integrals, partial derivatives). 
Satisfies  Elective

Time  Wednesday 5:308:30pm 
Location  JCL 390 