Final Exam: Introduction to Math

Math    |    Beginner
  • 1 Video | 32s
  • Includes Assessment
  • Earns a Badge
Final Exam: Introduction to Math will test your knowledge and application of the topics presented throughout the Introduction to Math track of the Skillsoft Aspire Essential Math for Data Science Journey.

WHAT YOU WILL LEARN

  • associate attributes with the graph, individual nodes, and individual edges
    compute derivatives of linearly changing functions using built-in functions
    compute determinants and transpose matrices
    compute eigenvalues and eigenvectors of a matrix
    compute integrals from first principles
    compute integrals of quadratic and polynomial functions
    compute the inverse of an invertible matrix
    compute the minimum spanning tree and the shortest path for a graph
    compute the shortest path and a minimum spanning tree for a graph
    compute the velocity of a moving particle
    create a matrix and perform matrix operations
    create and work with sets in Python
    create and work with sets in Python, perform checks for subsets and supersets
    create directed graphs using NetworkX
    create matrices using SciPy
    create partial derivatives with multiple independent variables
    define the integral as the limit of a sum and as the area under a curve
    derive the characteristic equation
    describe differentiation and derivatives
    determine the optimal solution from feasible solutions
    differentiate between definite and indefinite integrals
    differentiate between eigen decomposition and Singular Value Decomposition (SVD)
    explore properties of eigenvalues and eigenvectors
    formulate the integer programming model for a capital budgeting problem
    formulate the integer programming model for Becca Luxury Goods
    frame integration as inverse operations
    gain a basic understanding of calculus and describe differentiation and derivatives
    identify and work with diagonal and zero matrices
    import an image to perform SVD
    install libraries in Python
  • list the steps in the Simplex method to solve linear programming problems
    mathematically define eigenvectors and eigen values
    mathematically define matrix decomposition
    mathematically define QR and Cholesky decomposition
    model entities and relationships in the real-world using graphs
    model problems using objectives, decision variables, and constraints
    outline derivatives and slope
    outline how partial derivatives work
    outline how to compute the slope at a point by exploring the geometric definition of derivatives
    perform LU decomposition
    perform matrix addition
    perform matrix multiplication
    perform QR decomposition
    perform Singular Value Decomposition (SVD) on a matrix
    perform topological sorting in a directed acyclic graph
    perform union and intersection operations on sets
    recall the assumptions and benefits of integer optimization models
    recognize properties of matrices
    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
    simplify an image with SVD
    solve linear programming problems using the Pulp library
    solve minimization problems using the SciPy library
    solve the linear programming problem graphically
    solve the manufacturing and distribution problem using Pulp
    use built-in functions to compute derivatives
    use derivatives in real-world scenarios
    use LP relaxation to find a starting point for the integer programming solution

IN THIS COURSE

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    Introduction to Math
    33s
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