Doing Math With Python
Algebra with Python, Algebra 1, Algebra 2, Geometry, Trigonometry, PreCalculus, Calculus
This course’s goal is to bring together three topics near to my heart—programming, math, and science. What does that mean exactly? Within these pages, we’ll programmatically explore high school–level topics, like manipulating units of measurement; examining projectile motion; calculating mean, median, and mode; determining linear correlation; solving algebraic equations; describing the motion of a simple pendulum; simulating dice games; creating geometric shapes; and finding the limits, derivatives, and integrals of functions. These are familiar topics for many, but instead of using pen and paper, we’ll use our computer to explore them. We’ll write programs that will take numbers and formulas as input, do the tedious calculations needed, and then spit out the solution or draw a graph. Some of these programs are powerful calculators for solving math problems. They find the solutions to equations, calculate the correlation between sets of data, and determine the maximum value of a function, among other tasks. In other programs, we’ll simulate reallife events, such as projectile motion, a coin toss, or a die roll. Using programs to simulate such events gives us an easy way to analyze and learn more about them.
You’ll also find topics that would be extremely difficult to explore without programs. For example, drawing fractals by hand is tedious at best and close to impossible at worst. With a program, all we need to do is run a for loop with the relevant operation in the body of the loop.
I think you’ll find that this new context for “doing math” makes learning both programming and math more exciting, fun, and rewarding.
• Chapter 1, Working with Numbers, starts off with basic mathematical operations and gradually moves on to topics requiring a higher level of math knowhow.
• Chapter 4, Algebra and Symbolic Math with SymPy, introduces symbolic math using the SymPy library. It begins with the basics of representing and manipulating algebraic expressions before introducing more complicated matters, such as solving equations.
Your Instructor
Eric Chou, Ph.D.
He obtained his MS and PhD degree from the University of Southern California, Los Angeles, CA, USA. His technical fields is focused on smart sensory information processing, machine learning, optimization theory, communication and VLSI design.Currently, He is an adjunct faculty member in the Online M.S. Computer Science/Data Science Programs at Lewis University, IL. He is also running a startup company.
He love computational research and its application to real world. i have involved in many largescale computer/communication product research development in many worldleading company such as HP, Micrel, and many startups. I also involved in founding a startup company. I also enjoy sharing my ideas through teaching. I hold many US and international patents in technical fields such as software development, mobile computing, IC design and communication receiver design. I am also a certified coach in Taekwondo in both UAST and AAU.
Course Curriculum

StartLecture A: Numbers (Part 1: Introduction) (57:42)

StartLecture A: Numbers (Part 2: Number Sets) (47:31)

StartLecture A: Numbers (Part 3 First Program) (64:46)

StartLecture A: Numbers (Part 4: Irrational and Complex) (61:44)

StartChapter 1: Homework

StartLecture B: Data Types Part 1: Overview() (28:48)

StartLecture B: Data Types (Part 2: Integer Division) (74:51)

StartExtraMaterial: Modular Arithmetic

StartUnit 1 Lecture B: Number Systems (Part 3: Number Systems) (66:14)

StartUnit 1 Lecture B: Data Types (Part 4: String) (56:58)

StartChapter 2 Homework

StartLecture C: Operators (Part 1: Integer Division) (64:59)

StartLecture C: Operations (Part 2: Operators) (54:44)

StartChapter 3; Homework

StartLecture D: Python Program Structure (58:12)

StartChapter 4 Homework