MPCS 53110 Foundations of Computational Data Analysis (Winter 2021)

Section 1
Instructor(s) Chaudhary, Amitabh (amitabh)
Location Online Only
Meeting Times Wednesday 5:20pm - 8:20pm
Fulfills Elective Specialization - Data Analytics (DA-1)

Syllabus

*This course will be conducted remotely and will be online only for Winter 2021*


Foundations of Computational Data Analysis covers mathematical prerequisites for the Data Analytics Specialization courses in machine learning, and large-scale 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.

Course Prerequisites

B+ or above in MPCS 50103 Math for Computer Science or passing the Discrete Math placement exam
B+ or above in MPCS 55001 Algorithms (completed or taking concurrently)
B+ or above in MPCS 51042 Python Programming (or in the core programming requirement with prior knowledge of Python)

Other Prerequisites

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

Overlapping Classes

This class is scheduled at a time that conflicts with these other classes:

  • MPCS 50103-1 -- Mathematics for Computer Science: Discrete Mathematics
  • MPCS 52011-1 -- Introduction to Computer Systems
  • MPCS 51250-1 -- Entrepreneurship in Technology