Mathematical and Statistical Foundation of Data Science
Mathematical and statistical building blocks used across data science: discrete math, graphs, linear algebra, vector geometry, and probability.
Use mathematical concepts in the field of data science.
Employ the techniques and methods related to the area of data science in a variety of applications.
Apply logical thinking to understand and solve the problem in context.
Modules
References
G. Strang, Introduction to Linear Algebra, 5e, Wellesley-Cambridge Press, 2016.
Bendat & Piersol, Random Data: Analysis and Measurement Procedures, 4e, Wiley, 2010.
Montgomery & Runger, Applied Statistics and Probability for Engineers, 5e, Wiley, 2011.
David G. Luenberger, Optimization by Vector Space Methods, Wiley, 1969.
O'Neil & Schutt, Doing Data Science, O'Reilly, 2013.
Kenneth H. Rosen, Discrete Mathematics and Its Applications, 7e, McGraw-Hill, 2011.
Mott, Kandel & Baker, Discrete Mathematics for CS & Mathematicians, 2e, PHI, 2002.
Mitzenmacher & Upfal, Probability and Computing, 2e, Cambridge, 2017.
Agresti, Franklin & Klingenberg, Statistics: The Art and Science of Learning from Data, 4e, Pearson, 2017.