Finite Math Online Course for Academic Credit

Finite Mathematics - DMAT 145 - is a common freshman course for business-oriented students to learn more mathematics for application to their business and economic degrees.

Completion of DMAT 145 - Computational Finite Mathematics - earns 3 academic credit semester hours with an official academic transcript from Roger Williams University, in Providence, Rhode Island, USA, which is regionally accredited by the New England Commission of Higher Education (NECHE), facilitating transfer of credits nationwide to other colleges and universities.

Roger Williams University Course Catalog Listing: DMAT 145 - Computational Finite Mathematics

DMAT 145 - Computational Finite Mathematics - Learning Outcomes

• 1. To identify, manipulate, and understand the core concept of functions
• 2. To understand and compute the key components of linear equations
• 3. To understand and compute matrix algebra and systems of linear equations
• 4. To understand and compute the concept of linear programming, and various methods of analysis
• 5. To study topics in Mathematics of Finance
• 6. To understand and compute set notation, analysis, and counting
• 7. To understand and compute the core topics of Probability and Sampling
• 8. To understand and compute with the Conditional Probaiblity formula
• 9. To understand and compute Markov Chains
• 10. To understand and compute the basics of Game Theory

DMAT 145 - Computational Finite Mathematics - Syllabus of Topics

1.	Getting Started
	1.1.  Email and Chat
	1.2.  Learning About the Course
	1.3   Required Hardware
	1.4.  Software Fundamentals

2.	The Big Picture
	2.1.	Solving (easy) equations in 1 variable.
	2.2.	What if you can't solve for x?
	2.3.	Finding solutions numerically
	2.4.	Finding solutions graphically
	2.5.	Solving equations of more than 1 variable

3.	Functions
	3.1.	Function notation
	3.2.	Data sets
	3.3.	Graphing functions
	3.4.	Data sets and smooth curves
	3.5.	Domain and Range
	3.6.	Algebraic combinations of functions

4.	Linear Functions
	4.1.	Algebraic definition
	4.2.	Slope
	4.3.	Graphing linear functions by hand
	4.4.	Properties of linear functions
	4.5.	Linear data sets
	4.6.	Applications

5.	Matrices
	5.1.	Connection to systems of linear equations
	5.2.	Matrix operations
	5.3.	Solutions of systems of linear equations
	5.4.	Matrix Inverses
	5.5.	Row Operations
	5.6.	Applications to Cryptography
	5.7.	Leontief Models

6.	Linear Programming
	6.1.	Systems of Linear Inequalities
	6.2.	Feasibility
	6.3.	Minimization and Maximization
	6.4.	Geometry of Linear Programming
	6.5.	Simplex Method

7.	Mathematics of Finance
	7.1.	Simple and Compound Interest
	7.2.	Present Value
	7.3.	Classification
	7.4.	Applications

8.	Sets and Counting
	8.1.	Definitions
	8.2.	Tree Diagrams
	8.3.	Permutations

9.	Probability
	9.1.	Sampling and Probability
	9.2.	Independence
	9.3.	Tree Diagrams and Combinations
	9.4.	Conditional Probability
	9.5.	Binomial Probability
	9.6.	Markov Chains
	9.7.      Game Theory
	
	
         

Distance Calculus - Student Reviews