Statistical Physics & Thermodynamics from Beginner to Expert from Udemy

This course is for everyone who wants to learn about statistical physics.

A bit of college mathematics (basic derivatives) is all you need to know.

Understanding the motion of a single object is possible using the laws of classical mechanics. However, when we want to consider billions of particles at the same time, we need a new method: Statistical physics. The theory behind this approach is fascinating due to its simplicity. Still, it allows to correctly predict the laws of thermodynamics.

You are kindly invited to join this carefully prepared course in which we derive the following concepts from scratch. I will present examples and have prepared quizzes and exercises for all topics.

Optional tutorial of the essential basics (2 hours)

Laws of classical mechanics
Statistics & stochastics

Theory of statistical physics (3 hours)

Isolated, closed and open systems (micro canonical, canonical and grand canonical ensembles)
Probability density, partition function and average values

Applications and examples (6 hours)

Entropy, temperature and the laws of thermodynamics
Thermodynamic properties of gases
Phase transitions

At the end of the course there is even an optional section in which we simulate a phase transition using python. This is state of the art research.

Why me?

My name is Börge Göbel and I am a postdoc working as a scientist on theoretical magnetism. Therefore, I use statistical physics very often but I have not forgotten the time when I learned about this theory and still remember the problems that I and other students had. I have refined my advisor skills as a tutor of Bachelor, Master and PhD students in theoretical physics and have other successful courses here on Udemy.

I hope you are excited and I kindly welcome you to our course.

What's inside

Learning objectives

Basics: tutorial of classical mechanics and statistics
Theory: statistical physics of isolated, closed & open systems

Application: thermodynamics with many examples
Advanced project: phase transitions based on statistical physics and monte carlo algorithms

Basics: tutorial of classical mechanics and statistics
Theory: statistical physics of isolated, closed & open systems
Application: thermodynamics with many examples
Advanced project: phase transitions based on statistical physics and monte carlo algorithms

Syllabus

Introduction & Physical background

Overview of the course

Classical mechanics background

Newton's laws of motion

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Presents the theory behind statistical physics in a way that emphasizes its simplicity, which may appeal to learners who are intimidated by complex physics topics

Includes an optional section on simulating a phase transition using Python, which aligns with state-of-the-art research practices and could be valuable for those in the field

Requires only a bit of college mathematics (basic derivatives), making it accessible to a wider audience with a foundational understanding of calculus

Covers isolated, closed, and open systems, which are fundamental concepts in statistical physics and essential for understanding different thermodynamic scenarios

Includes optional tutorials on classical mechanics and statistics, which may be helpful for learners who need a refresher on these foundational topics

Uses examples of stochastics that involve dice and urn problems, which may not be applicable to all learners and may require additional knowledge

Reviews summary

Solid statistical physics & thermodynamics foundation

According to students, this course provides a solid foundation in statistical physics and thermodynamics. Learners particularly appreciate the instructor's ability to explain complex theoretical concepts clearly, making seemingly difficult topics understandable. The course covers a wide range of material, from foundational classical mechanics and statistics to advanced topics like phase transitions and optional quantum statistics. While many find the lectures engaging and well-structured, some beginners report the pace can be challenging, suggesting a prior background in university-level physics and mathematics is beneficial despite the course's 'beginner to expert' title. The course includes helpful examples and exercises, though opinions vary on their difficulty and alignment with the lecture material. Overall, it's considered a strong theoretical treatment suitable for serious students and professionals.

Valuable deeper dives for interested learners.

"The optional Python simulation section was a great addition, very practical."

"Appreciated the quantum statistics optional section; it added valuable depth."

"These optional parts show the instructor's commitment to advanced topics."

Covers a broad range of key areas.

"Loved the breadth of topics covered, from basic ensembles to phase transitions and even quantum optional."

"The syllabus promises a lot and delivers; it's a very complete overview of the field."

"It provides a solid theoretical base that seems applicable to further study or professional work."

Complex theories are broken down effectively.

"The instructor does a fantastic job of explaining very complex ideas in a way that makes them accessible."

"I really liked how the theoretical concepts were derived step-by-step, making it easier to follow."

"The lectures helped me finally understand foundational concepts in stat mech I struggled with before."

Exercises sometimes misaligned or too simple.

"Some exercises felt a bit too basic compared to the lecture complexity."

"I found some quizzes didn't fully test understanding of the derivations shown."

"Could benefit from more challenging problems that reinforce the core theoretical points."

Moves quickly, potentially challenging for beginners.

"For someone truly starting from scratch, the pace might be a bit too fast, especially later in the course."

"Although it says 'beginner to expert', I feel it's better suited if you have some university physics under your belt."

"Sometimes felt rushed going through derivations without enough time to absorb."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistical Physics & Thermodynamics from Beginner to Expert with these activities:

Review Classical Mechanics

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Reinforce your understanding of classical mechanics, which is essential for grasping the concepts of statistical physics.

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Review the fundamental laws of motion.
Practice solving problems related to energy conservation.
Familiarize yourself with Hamiltonian mechanics.

Brush Up on Statistics and Probability

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Strengthen your knowledge of probability distributions and statistical concepts, which are crucial for understanding statistical physics.

Browse courses on Binomial Distribution

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Review probability distributions like binomial, normal, and Poisson.
Practice calculating expectation values.
Solve problems related to probability and statistics.

Read 'Statistical Physics' by Landau and Lifshitz

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Supplement your learning with a deep dive into a classic textbook on statistical physics.

View Statistical Physics on Amazon

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Obtain a copy of 'Statistical Physics' by Landau and Lifshitz.
Read the chapters relevant to the course topics.
Work through the example problems.

Four other activities

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Show all seven activities

Participate in Study Groups

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Collaborate with peers to discuss course concepts and solve problems together.

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Form a study group with other students.
Schedule regular meetings to discuss course material.
Work through practice problems together.
Explain concepts to each other.

Create Explanatory Blog Posts

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Solidify your understanding by explaining key concepts in your own words through blog posts.

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Choose a specific topic from the course.
Research the topic thoroughly.
Write a clear and concise blog post explaining the concept.
Include examples and illustrations.
Publish your blog post online.

Simulate a Thermodynamic System

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Apply your knowledge by simulating a thermodynamic system using Python or another programming language.

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Choose a thermodynamic system to simulate (e.g., ideal gas, Ising model).
Write a program to simulate the system's behavior.
Analyze the results of your simulation.
Compare your results to theoretical predictions.

Explore 'Thermodynamics and an Introduction to Thermostatistics' by Callen

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Deepen your understanding of thermodynamics with a well-regarded textbook.

View THERMODYNAMICS & AN INTRO. TO THERMOSTATISTICS on Amazon

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Obtain a copy of 'Thermodynamics and an Introduction to Thermostatistics' by Callen.
Read the chapters relevant to the course topics.
Work through the example problems.

Career center

Learners who complete Statistical Physics & Thermodynamics from Beginner to Expert will develop knowledge and skills that may be useful to these careers:

Professor Physics

The physics professor teaches physics courses at the college or university level and conducts research in a specialized area of physics. Statistical mechanics and thermodynamics are core subjects in physics curricula. By taking this course, one can solidify their understanding of the fundamentals, gain new perspectives on teaching these subjects, and potentially develop new research directions. The course's examples, exercises, and advanced Python project can serve as valuable teaching resources. This field often requires advanced study.

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Research Scientist

A research scientist designs and conducts experiments to investigate phenomena, test hypotheses, and develop new knowledge. Many research areas, particularly in materials science or condensed matter physics, rely heavily on statistical mechanics and thermodynamics. Success as a research scientist hinges on understanding the statistical behavior of large systems. This course can help prepare one for such work, covering topics like entropy, temperature, and the various laws of thermodynamics, all essential for advanced research projects. The advanced project simulating a phase transition using Python is particularly valuable, as it reflects current research practices.

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Materials Scientist

A materials scientist researches and develops new materials with specific mechanical, electrical, or chemical properties. Expertise in statistical physics and thermodynamics is important for understanding the behavior of materials at a microscopic level. This course explores phase transitions, key to understanding material properties and behavior under different conditions. Specifically, the course touches upon how to model and simulate phenomena that are key to novel materials development. The course also covers quantum statistics, which is critical for understanding the electronic and thermal properties of materials.

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Data Scientist

A data scientist analyzes complex data sets to extract meaningful insights and develop predictive models. This career often requires a strong foundation in statistical mechanics and thermodynamics. A course covering the statistical treatment of large systems, such as gases or interacting particles can help build a foundation in the concepts needed to understand and model complex data patterns. In particular, the course's treatment of probability density, partition functions, and average values can be directly applied in the development of statistical models used in machine learning and data analysis.

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Thermodynamic Engineer

The thermodynamic engineer designs and optimizes systems involving heat transfer and energy conversion, such as power plants or refrigeration systems. This specific engineering requires a deep understanding of thermodynamics. The course's coverage of the laws of thermodynamics, thermodynamic properties of gases, and thermodynamic cycles is directly applicable to this work. Examples and exercises focusing on real-world applications discussed in the course are important for problem-solving.

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Financial Engineer

The financial engineer, also known as a quantitative analyst, develops and implements complex financial models. Statistical physics provides tools for modeling complex systems, which can be applied to financial markets. The course's treatment of statistical ensembles and probability distributions can be valuable for modeling market behavior and risk. Knowledge of statistical mechanics can provide a competitive edge in understanding and predicting market dynamics. This role often requires advanced study.

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Quantitative Analyst

The quantitative analyst role applies mathematical and statistical methods to financial and risk management problems. Those with a background in statistical physics are well-suited for this role. Skills from such a background can be applied to model market behavior. This course, with its focus on statistical approaches to complex systems addresses the same skill set. The exploration of statistical physics from isolated to open systems, including microcanonical, canonical, and grand canonical ensembles, offers a framework for tackling problems related to financial modeling.

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Simulation Engineer

The simulation engineer develops and uses computer models to simulate physical systems and processes. Statistical physics and thermodynamics provide fundamental frameworks for many of these simulations. One can benefit from this course, which covers key principles relevant to such simulations. Especially useful is the content on the grand canonical ensemble, which helps when simulating open systems. Also, the course project simulating phase transitions using Python provides hands-on experience. This field often requires advanced study.

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Energy Analyst

The energy analyst examines energy production, distribution, and consumption patterns to identify opportunities for efficiency and sustainability. Effective analysis often requires a solid understanding of thermodynamics and energy systems. A course dedicated to thermodynamics empowers one to model and optimize energy-related processes, from power generation to energy storage. The discussion of thermodynamic cycles and real gases can give one a toolset for analyzing energy systems.

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Biophysicist

A biophysicist studies biological systems using the principles of physics. This requires a strong grounding in statistical mechanics and thermodynamics to understand the behavior of biological molecules and systems. This course may assist in learning these fundamentals. Topics like entropy, free energy, and statistical ensembles, covered in this course, are particularly relevant to understanding protein folding, molecular dynamics, and other biophysical processes. This field often requires advanced study.

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Nuclear Engineer

The nuclear engineer designs, develops, and maintains nuclear power plants and other nuclear technologies. Understanding the thermodynamics of nuclear reactions and reactor systems is important in this role. A course that teaches thermodynamics can provide fundamental knowledge relevant to understanding energy transport and efficiency in nuclear systems. Discussions of statistical physics can help one model the behavior of particles within reactor cores.

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Climate Modeler

A climate modeler develops and uses computer models to simulate Earth's climate system. This person often needs a solid grounding in statistical physics and thermodynamics to accurately represent the complex interactions within the climate. This course can prove useful, as it teaches foundational principles in statistical physics. Especially relevant is its explanation of open and closed systems, and how to model them. The material on gases and phase transitions may further assist understanding atmospheric processes.

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Econometrician

The econometrician applies statistical methods to analyze economic data and test economic theories. Knowledge of statistical physics can provide a unique perspective, especially when modeling complex economic systems. Taking this course may be beneficial, as skills in statistical mechanics are useful for modeling emergent behavior in large, interconnected systems, such as markets or economies. The course's treatment of probability distributions and statistical ensembles can be applied to economic modeling.

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Software Developer

A software developer designs, develops, and tests software applications. While seemingly unrelated, statistical physics concepts find use in areas like Monte Carlo simulations or optimization algorithms. The training in statistical physics afforded by such a course may assist in such development. The project where one simulates a phase transition using Python may be particularly useful, as it offers practical experience in applying statistical physics principles to computational problems. This may be useful in certain areas of quantitative finance.

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Acoustic Engineer

An acoustic engineer deals with sound and vibration. Statistical mechanics can be applied to model the behavior of sound waves in complex environments. While this course may seem distantly related, the concepts of statistical distributions and energy transfer discussed can provide new perspectives on acoustic phenomena. Gaining general understanding of how energy propagates through media is important in this role.

See salaries and explore the career path for Acoustic Engineer

Statistical Physics & Thermodynamics from Beginner to Expert

What's inside

Learning objectives

Syllabus

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Reviews summary

Solid statistical physics & thermodynamics foundation

Activities

Career center

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