Math for Data Science Literacy (Beginner Level)

Topics covered

• compute the degree of a graph node
• create a matrix and perform matrix operations
• define and perform scaling, dot product, and cross product operations on vectors
• define the integral as the limit of a sum and as the area under a curve
• define what's meant by a vector, a vector notation, a matrix notation, and an ordered set notation
• describe what statistics, populations, and samples are
• differentiate between definite and indefinite integrals
• enumerate differences between random and non-random variables and describe the role of probability
• frame differentiation and integration as inverse operations
• gain a basic understanding of calculus and describe differentiation and derivatives
• identify use-cases for linear algebra
• list the assumptions and benefits of linear optimization models
• mathematically define eigenvectors and eigenvalues
• outline how partial derivatives work
• outline how to enumerate properties of matrices and vectors
• perform checks for subsets and supersets
• recognize how metrics such as mean, median and mode describe data
• recognize how the slope between two points is calculated
• recognize several types of matrix operations
• recognize the assumptions and benefits of integer optimization models
• recognize the differences between discrete and continuous data and outline topics in discrete mathematics
• recognize the different types of graphs, their characteristics, and use cases
• recognize the use of optimization to make decisions involving trade-offs
• represent vectors in alternative notations
• use built-in functions to compute derivatives