Doing Math with Python from Udemy

With this course, we'll go through the nice book "Doing Math with Python" (authored by Amit Saha) in full detail, demo to you every touched mathematical 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.

After the course, you'll be able write programes to take numbers and formulas as input, do the tedious calculations needed, and then spit out the solution or draw a graph.

You'll have chances to follow several programming challenges, you'll find those programs are powerful calculators for solving math problems. You'll also find the solutions to equations, calculate the correlation between sets of data, and determine the maximum value of a function, among other tasks. You'll also take some practice to simulate real-life events, such as projectile motions, a coin toss, or a die roll.

As mentioned by the book author, I do hope "you'll find that this new context for 'doing math' makes learning both programming and math more exciting, fun, and rewarding."

So, what you're still waitomg fpr? Let's start now and good luck.

Note: the source code are available in both Github and Gitee repository.

What's inside

Learning objectives

Using python, be able to explore high school level mathematical topics
Learn how to programming data with graphs using python
Learn how to describing data with statistics
Grasp algebra and symbolic math with sympy in python

Learn how to play with sets and probablility
Learn hwo to drawing geometric shaps and fractals
Using python to solve certain calculus problems

Using python, be able to explore high school level mathematical topics
Learn how to programming data with graphs using python
Learn how to describing data with statistics
Grasp algebra and symbolic math with sympy in python
Learn how to play with sets and probablility
Learn hwo to drawing geometric shaps and fractals
Using python to solve certain calculus problems

Syllabus

Introduction

Opening and Introduction

Chapter 1: Working with Numbers

01-01 Basic Mathematical Operations

Career center

Learners who complete Doing Math with Python will develop knowledge and skills that may be useful to these careers:

Computational Scientist

A Computational Scientist develops and applies advanced computational methods, algorithms, and simulations to address complex scientific and engineering challenges across diverse disciplines. This role often requires an advanced degree. The "Doing Math with Python" course is an exceptional fit for an aspiring Computational Scientist. It helps build a comprehensive foundation in applying programming to mathematics, including symbolic algebra with SymPy, solving calculus problems like derivatives and integrals, and creating sophisticated simulations. The experience of writing programs to handle numerical inputs, perform calculations, and visualize results directly mirrors the core work of a Computational Scientist in modeling and exploring scientific phenomena.

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Data Scientist

A Data Scientist synthesizes expertise in statistics, computer science, and domain knowledge to extract insights from complex datasets and build predictive models. This multifaceted career demands strong programming and analytical capabilities. For an aspiring Data Scientist, the "Doing Math with Python" course helps build a comprehensive foundation, encompassing data visualization, descriptive statistics like correlation and percentiles, symbolic algebra, and probability. The chance to programmatically solve mathematical challenges and simulate events, combined with skills in reading and manipulating data from files, directly prepares learners to tackle real-world data science problems and develop robust analytical solutions. This role often requires an advanced degree.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys algorithms that enable computers to learn from data, driving innovations in artificial intelligence. This demanding role requires a robust background in mathematics, statistics, and programming. The "Doing Math with Python" course is profoundly relevant for a Machine Learning Engineer, covering essential topics such as descriptive statistics, probability, algebra with SymPy for symbolic manipulation, and solving calculus problems including derivatives and gradient descent. The ability to program data with graphs and handle numerical operations in Python helps build the analytical and computational skills critical for developing and optimizing machine learning models. This career often requires an advanced degree.

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Data Analyst

A Data Analyst is crucial for translating raw data into actionable insights, vital for strategic decision-making across industries. This role involves collecting, cleaning, analyzing, and visualizing data to identify trends and patterns. The "Doing Math with Python" course helps build a foundation in descriptive statistics, including calculating mean, median, mode, and correlation, which are core tasks in this career. Learners also gain practical experience in data visualization using Matplotlib and reading data from files, directly preparing them to create compelling reports and dashboards. Taking this course equips aspiring Data Analysts with the programming skills to automate analyses and efficiently handle numerical data, making it an excellent choice for entering this field.

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Quantitative Analyst

A Quantitative Analyst, often called a "Quant," applies advanced mathematical and statistical methods to solve complex problems, typically in finance or risk management. This role involves building sophisticated models, performing statistical analysis, and developing algorithms. The "Doing Math with Python" course provides a strong conceptual understanding and practical experience in areas like statistics, probability, algebraic equation solving using SymPy, and calculus, including derivatives and integrals. The ability to simulate dice games and other probabilistic events, combined with Python programming for numerical tasks, is directly applicable. This course helps build the programming and mathematical modeling foundation essential for a rigorous Quantitative Analyst career, which often requires an advanced degree.

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Operations Research Analyst

An Operations Research Analyst uses advanced analytical methods, including mathematical modeling, statistics, and optimization, to help organizations make better decisions and solve complex logistical and strategic problems. This career frequently requires an advanced degree. The "Doing Math with Python" course is highly relevant for an Operations Research Analyst, as it covers solving algebraic equations, statistical analysis, and probability. Crucially, the course includes implementing gradient descent and finding maxima and minima of functions—optimization techniques fundamental to operations research. The ability to simulate systems and programmatically handle complex calculations helps build the core quantitative skills needed in this field.

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Physics Programmer

A Physics Programmer develops software that simulates physical phenomena, often within scientific research, engineering, or even the entertainment industry for games and visual effects. The "Doing Math with Python" course offers exceptionally relevant skills for a Physics Programmer. Learners directly engage in programming projectile motion, describing the motion of a simple pendulum, and animating figures based on physical laws. They delve into calculus for finding limits, derivatives, and integrals of functions, crucial for understanding dynamic systems. This foundation in applying mathematical concepts programmatically helps build the expertise needed to create accurate and compelling physics simulations.

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Actuary

An Actuary assesses and manages financial risk, predominantly in the insurance and pension industries, by applying robust mathematical, statistical, and financial theories to analyze future uncertain events. This role generally requires an advanced degree and professional certifications. For an Actuary, the "Doing Math with Python" course is highly pertinent, covering core concepts in statistics, such as calculating mean, median, mode, and correlation, along with extensive work in probability. The course includes generating random numbers and simulating events, which are crucial for risk modeling. Learning to programmatically solve mathematical problems and work with probability density functions helps build foundational analytical skills essential for this profession.

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Junior Software Engineer Scientific Applications

A Junior Software Engineer Scientific Applications develops and maintains software solutions specifically designed for scientific or quantitative analysis. This role involves translating complex mathematical models and algorithms into functional code. The "Doing Math with Python" course is highly relevant, providing hands-on experience in writing programs for mathematical operations, solving algebraic equations with SymPy, and implementing calculus problems. Learners also practice simulating real-life events like projectile motion and applying formulas. This course helps develop the foundational programming skills in Python necessary to build powerful calculators for scientific problems and contribute to robust scientific software development efforts.

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Simulation Engineer

A Simulation Engineer develops and utilizes computer models to replicate and analyze the behavior of real-world systems or processes, aiding in design optimization and forecasting. This role requires strong programming and mathematical foundational skills. The "Doing Math with Python" course provides direct experience with simulating real-life events, such as projectile motions and dice games. Learners also gain proficiency in plotting with formulas, animating figures, and applying calculus to describe motion, which are all pertinent to building dynamic models. Understanding how to programmatically solve mathematical problems and visualize data can significantly prepare one for the complex modeling challenges a Simulation Engineer faces.

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Statistician

A Statistician designs studies, collects, analyzes, and interprets numerical data to identify patterns and draw conclusions, influencing decisions in various sectors from healthcare to market research. This course is invaluable for an aspiring Statistician as it covers core statistical concepts such as finding the mean, median, mode, measuring dispersion, calculating correlation, and creating frequency tables. Learners gain practical skills in describing data with statistics using Python, reading data from files, and creating scatter plots to visualize relationships. This specialized training in applying statistical methods programmatically helps build efficiency and depth in data analysis, which is fundamental to the field. A career as a Statistician generally requires an advanced degree.

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Financial Modeler

A Financial Modeler constructs quantitative frameworks and computational tools to analyze financial performance, forecast future trends, and evaluate investment opportunities. This role relies heavily on mathematical rigor and programming acumen. The "Doing Math with Python" course is highly applicable for a Financial Modeler, as it covers solving algebraic equations, performing statistical calculations like mean and correlation, and working with probability concepts. The ability to write programs that take numbers and formulas as input to perform tedious calculations and generate visual output, such as plotting expenses, directly translates to building sophisticated financial models and scenario analyses.

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Biophysicist

A Biophysicist explores biological systems using principles and methods traditionally employed in physics, often involving complex mathematical modeling, computational simulations, and data analysis. This role generally requires an advanced degree. While "Doing Math with Python" focuses on general mathematical principles, the course provides a foundational toolkit that may be helpful for a Biophysicist. It covers data visualization, descriptive statistics, solving algebraic equations, and applying calculus principles. The ability to programmatically model phenomena and analyze data helps build vital computational problem-solving skills, which are increasingly important for quantitatively studying biological processes and creating predictive models.

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Geomatics Engineer

A Geomatics Engineer works with geospatial data, applying mathematical, computational, and surveying techniques to map, measure, and manage the Earth's surface and features. For a Geomatics Engineer, the "Doing Math with Python" course may be useful by helping build a foundation in geometry and data visualization. Learners draw geometric shapes, work with Cartesian coordinates, and program data with graphs using Matplotlib, skills that are relevant for representing spatial information. The ability to manipulate units of measurement and solve algebraic equations programmatically can also be pertinent for various calculations in geographic information systems and spatial analysis.

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Technical Artist Game Development

A Technical Artist Game Development acts as a crucial bridge between artistic vision and technical implementation in game production, often creating tools and systems. For a Technical Artist Game Development, the "Doing Math with Python" course may be useful as it helps build a foundational understanding of geometry and animation. Learners work with drawing geometric shapes, animating figures, and applying transformations of points in a plane, directly relevant to visual effects and procedural content generation. The course's focus on programming mathematical concepts and simulating real-life events like projectile motion also helps develop skills applicable to game physics and interactive experiences.

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