SKILL BENCHMARK

# Math for Data Science Literacy (Beginner Level)

• 37m 30s
• 25 questions
The Math for Data Science Literacy benchmark will measure your ability to recall and relate the underlying math concepts in data science and machine learning solutions. You will be evaluated on your ability to recognize the foundational concepts of math for data science like the basics of statistics, probability, algebra, and calculus. A learner who scores high on this benchmark demonstrates that they have the basic math skills to understand and grasp the data analysis process and machine learning algorithms.

## 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