College Algebra Syllabus and Course Overview – Fall 2023

The Basics

About Your Instructor

• Name: Madhavi Pratapati

• Email: [e-mail]

• Phone: [phone]

• Office Hours: In-person and via Zoom (see syllabus for times and links)

Course Information

• Course Title: College Algebra

• Section: 006

• Meeting Times: MW 10:05am–11:25am

• Location: Zoom via Blackboard

• Instructional Methodology: Synchronous Virtual Class Meetings (DLS)

• Prerequisites: Appropriate placement scores or completion of required math courses

Required Materials

• Textbook: Finite Mathematics for Business, Economics, Life Sciences, and Social Sciences, 14th Edition by Barnett, Ziegler, Byleen, and Stocker. Pearson Publishing. (Online access included with tuition and fees.)

• Calculator: Scientific calculator capable of handling exponents, logarithms, and simple probability calculations. Graphing calculators are not required.

Course Topics and Weekly Schedule

This course covers foundational topics in College Algebra, including equations, functions, inequalities, and introductory probability and statistics. Below is the weekly breakdown of topics:

Week	Sections	Material
1	1.1, 1.2	Linear Equations and Inequalities; Graphs and Lines
2	2.1, 2.2	Functions; Elementary Functions
3	2.3, 2.4, 7.7	Operations on Polynomials; Factoring Polynomials; Quadratic Equations
4	2.3, 3.4	Quadratic Functions; Polynomial and Rational Functions
5	A.4, 7.2	Operations on Rational Expressions
6	A.5, 2.6, 2.5	Integer Exponents; Rational Exponents and Radicals; Exponential Functions
7	2.6, 3.1	Logarithmic Functions; Simple Interest
8	3.3, 7.5	Compound & Continuous Compound Interest; Future Value of an Annuity and Sinking Funds
9	3.4, 7.5	Present Value of an Annuity and Amortization
10	4.1, 4.2	Systems of Linear Equations; Basic Operations on Matrices
11	4.5, 4.6, 5.1	Linear Programming; Matrix Equations; Linear Inequalities; Systems of Linear Inequalities
12	5.2, 5.3, 7.4	Linear Programming
13	7.3, 7.4	Sets; Basic Counting Principles; Permutations and Combinations
14	8.1, 8.2	Sample Spaces, Events, and Probability; Union, Intersection, and Complements
15	8.3, 8.5	Conditional Probability & Intersection; Random Variables, Probability Distribution and Expected Value
16	Review, Test	Final Review and Assessment

Grading and Assessment

• Tests: 80% (6 exams, not multiple choice, administered via Blackboard)

• Homework: 10% (MyLab assignments, online platform)

• Take-Home Quizzes: 10% (uploaded before class, not accepted late)

• Grading Scale:

  • A: 90–100

  • B: 80–89

  • C: 70–79

  • D: 60–69

  • F: <60

• Where to Find Grades: Grades will be posted on Blackboard.

Course Policies and Expectations

• Attendance: Regular and punctual attendance is expected. Participation in class and group work is required.

• Homework: MyLab assignments must be completed online. Late work is penalized 10% per day. At least two lowest homework grades and one lowest quiz grade will be dropped.

• Quizzes: Take-home quizzes must be uploaded before class; no late submissions accepted.

• Make-Up Policy: No make-up tests. Only one retest is allowed at the end of the semester for a missed test.

• Calculator Policy: Scientific calculators are allowed; graphing calculators are not required. No cell phones or unapproved devices during exams.

• Academic Integrity: Cheating or use of unauthorized resources (including unapproved AI tools) is prohibited and will be penalized.

• AI Policy: Generative AI may be used for homework and independent learning, but not on major assessments unless explicitly permitted.

Support and Resources

• Instructional Associates: Available for tutoring specific to the course.

• Learning Labs: Tutoring in math and other subjects, both in-person and online.

• Academic Coaching: Support for study strategies and learning processes.

• Student Services: Academic, financial, personal, and technology support available.

Key College Algebra Topics Covered

Linear Equations and Inequalities

• Definition: Equations and inequalities involving linear expressions in one or more variables.

• Example: $2x + 3 = 7$; $x - 5 > 2$

• Application: Used to model and solve real-world problems involving constant rates of change.

Functions and Their Properties

• Definition: A function is a relation in which each input has exactly one output.

• Notation: $f(x)$ denotes the value of the function $f$ at $x$.

• Types: Linear, quadratic, polynomial, rational, exponential, and logarithmic functions.

• Example: $f(x) = x^2 + 3x - 4$

Polynomials and Factoring

• Definition: A polynomial is an expression consisting of variables and coefficients, involving only non-negative integer powers of variables.

• Factoring: Writing a polynomial as a product of its factors.

• Example: $x^2 - 9 = (x - 3)(x + 3)$

Quadratic Equations and Functions

• Standard Form: $ax^2 + bx + c = 0$

• Quadratic Formula: $x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$

• Applications: Modeling projectile motion, area problems, and more.

Rational Expressions and Equations

• Definition: Expressions or equations involving ratios of polynomials.

• Example: $\frac{x+2}{x-1}$

• Key Point: Always consider restrictions on the variable (denominator cannot be zero).

Exponential and Logarithmic Functions

• Exponential Function: $f(x) = a^x$, where $a > 0$ and $a \neq 1$

• Logarithmic Function: $f(x) = \log_a x$, the inverse of the exponential function

• Applications: Compound interest, population growth, radioactive decay

Systems of Equations and Matrices

• Definition: A set of two or more equations with the same variables.

• Matrix Operations: Addition, subtraction, multiplication, and finding inverses.

• Application: Solving real-world problems involving multiple constraints.

Linear Programming

• Definition: A method to achieve the best outcome in a mathematical model with linear relationships.

• Key Steps: Define variables, write constraints, write the objective function, graph feasible region, find optimal solution.

Counting Principles, Probability, and Statistics

• Counting Principles: Fundamental principle of counting, permutations, and combinations.

• Probability: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}$

• Random Variables and Expected Value: Used to model and analyze random processes.

Important Dates

• Last day for 70% refund: Sept 15 (Mon)

• Last day to withdraw: Nov 20 (Thu)

• Holidays: See syllabus for specific dates

Additional info:

• Some content and policies have been inferred and expanded for clarity and completeness.

• For full details, refer to the official course syllabus and college policies.