This course was about 2/3 review from high school chemistry and 1/3 new material. Overall, it was a straightforward course; follow along the notes and do practice questions until you're familiar with the concepts to do well in this course. Dr. Taha's notes were completely hand-written when I took CHEM 101, so be prepared to write. As for the lab portion, it was pretty chill since you have tons of time to complete the lab. To do well in the lab, review the techniques you'll be doing before the lab, and take good notes, as you likely won't be able to use your phone to access the lab manual during the lab itself. Also, please, please, please do NOT wash your lab coat at home (it will likely be contaminated with chemicals you do not want mixing with your everyday clothes); either take it to an industrial cleaner or buy a new one when your lab coat becomes too dirty. The class average was 2.7/4.0. As for the grade cutoffs, in general, intro chem tends to be the following: A+ ≥ 92%, A ≥ 86%, A- ≥ 81%, B+ ≥ 77%, B ≥ 70%, B- ≥ 66%, C+ ≥ 60%, C ≥ 55%, C- ≥ 51%, D+ ≥ 48%, D ≥ 46%, and F < 46%. The course average tends to be around 66.5%. However, these numbers may change from year to year depending on your class.

The 4 major units in this course included (i) atoms, (ii) molecules, (iii) gases, liquids, and solids, and (iv) main-group elements. The new material (i.e., likely not covered in high school chemistry) includes molecular orbital theory, crystalline solids, advanced materials, phase diagrams, and packing efficiency.

MATH 114 felt like a more difficult version of high school calculus. My top tips to succeed are to do the practice problems and work through assigned problems in the textbook. Also, do the practice exams to get familiar with the types of questions that you may be asked. You will most likely fail this course if all you do is read through the solutions. The class GPA was 2.5/4.0.

The tpics covered include a review of geometry, limits and continuity, derivatives, applications of derivatives, graph sketching, and antiderivatives and integrals.

Erik was a fantastic prof with extremely difficult exams. Both the midterm and final exam had a class average of 52%. Nobody got 100% on the midterm until Erik added 8% to everybody's marks. To do well in this course, do the practice problems and practice exams. The class average was 2.7/4.0.

This was the course description in the syllabus: "Algebra-based course primarily for students in life, environmental, and medical sciences. It guides the student through two distinct types of motion: motion of matter (particles) and wave motion. Vectors, forces, bodies in equilibrium, review of kinematics and basic dynamics; conservation of momentum and energy; circular motion; vibrations; elastic waves in matter; sound; wave optics; black body radiation, photons, de Broglie waves. Examples relevant in environmental, life, and medical sciences will be emphasized."

This was the easiest course I've taken at UAlberta. Most of the concepts are straightforward and the tests were extremely easy. To do well, read the textbook and follow along the class notes. The class GPA was 2.8/4.0

The topics in this course include: intro to psychology, research methods, stats in psychology, genes, evolution, neural basics, sensation, perception, learning, memory, motivation, emotion, and consciousness.

This course felt like a comprehensive review of high school biology with more in-depth explanations of various metabolic processes in plant and animal cells. The way I studied for this course was to annotate the PowerPoint notes while the prof taught, then use flashcards and mindmaps to consolidate the knowledge. The textbook is a very helpful reference text for BIOL 107. The class GPA was 2.6/4.0.

The topics covered are pretty standard: macromolecules, biological membranes, eukaryotic and prokaryotic organelles, biological reactions, photosynthesis, glycolysis, Krebs cycle, ATP synthesis, fermentation, bacterial growth, cell division, DNA, genes, mRNA, proteins, operons, and transcription regulation in eukaryotes.

Dr. Grant was such a great prof! Her teaching is excellent, her notes are fantastic, and she gives out candy on the last day of class! Also, Dr. Grant is like the Terminator for marking exams; she marks all of them herself in a single day. Effective study techniques are to first understand key concepts—such as nucleophilic and electrophilic sites, protic and aprotic solvents, etc.—then practice as many questions as possible. Although a certain degree of memorization is necessary (like memorizing solvents for different reactions), this course is best learned through practice (all exams are 100% written). The class GPA was 2.9/4.0.

This course covers the following topics: molecular structure, families of carbon compounds, IR spectroscopy, acids and bases, nomenclature, conformational analysis, stereochemistry, substitution reactions, alkenes and alkynes, alcohols and ethers, conjugated systems, and aromatic compounds.

PHYS 126 is an algebra-based course for life sciences students that extends many concepts you may have learned in high school. Dr. Wheelock was a decent lecturer with good notes and fair exams. However, the exam difficulty is very uniform between questions and the exams themselves are only out of 15 marks for 15 multiple choice questions, so the class distribution is pretty tight; try not to make careless mistakes. The class GPA was 2.7/4.0.

The following units are taught: fluids, electric charges / forces / fields, electric potential, electric current, DC circuits, magnetism, magnetic flux, Faraday's law of induction, AC circuits, nuclear physics, and nuclear radiation. All of the topics presented are very surface level due to the course being algebra-based and not calculus-based, but it will provide all of the physics knowledge you need (alongside PHYS 124) to succeed on the MCAT.

If you have the choice, pick Rosana! She is one of the best stats lecturers at UAlberta, and her class section had the highest average across all of the STAT sections (the exams are consolidated). The lab sections are not mandatory to attend and are there as TA help sessions. You will complete labs in groups of 3-4 chosen for you in this course. Depending on your luck, you may need to do all of the work or share the work in the labs. My tips for succeeding are to know how to answer the questions in the practice exams and labs, as they are very similar in content to the exams. The class average was 2.7/4.0.

This course teaches the following topics: gathering data, descriptive statistics, probability and probability models, sampling distributions, inference for proportions, chi-square tests, inference for means (t-tests), ANOVA, and simple linear regression. Many of these topics show up over and over again in research settings (and whenever dealing with data), so make sure you understand the main idea behind each statistical test.

This course is a much more in depth version of the genetics you learned in high school. In my opinion, BIOL 207 was a lot harder than BIOL 107, as it was easier to make mistakes and the concepts involved more moving parts. To be honest, it was the large lab component that saved my mark in this course. For inspiration, I received low 70s on all of my midterms (class average was high 60s) but somehow pulled through with an A. Although Dr. Locke is now retired, his exams were extremely similar to his practice exams. My top tips for doing well are to understand how to apply each of the concepts presented (e.g., restriction enzymes, transposons, Western blots) and to work through the exercises in the OGL textbook. The class GPA was 2.8/4.0.

This course was divided into mulitple "themes." The are as follows: central dogma, gene structure and expression, mutations, chromosomes and meiosis, chromosome genetics, cloning DNA and genes, DNA variation, evolution, and ethics.

This is the more exciting continuation of CHEM 261! If you enjoyed working through mechanisms and knowing how to synthesize various compounds, this is the course for you. Dr. Clive's exams were fair and 100% written response, and they were extremely similar to his past exams. The midterm average was 66%. Although the lecture portion is fair, the labs are extremely hectic. In my first lab, nobody finished on time. In the rest of the labs, my yields were never above 30% (but that's more a me problem than anything else). The class GPA was 2.9/4.0.

There are a ton of concepts covered in this coruse. You start with a review of NMR and mass spectroscopy, and you will be expected to draw a chemical compound based on the signals from both techniques. You then delve into conjugated unsaturated systems, aromatic compounds, and reactions of aromatic compounds. The next topic is about nucleophilic addition to carbonyl groups (e.g., aldehydes and ketones) and carbohydrates. You then learn about carboxylic acids and derivatives alongside nucleophilic addition-elimination at the acyl carbon. You also learn about lipids. The final few topics are about reactions at the alpha-carbon of carbonyl compounds (e.g., enols and enolates), condensation and conjugate addition reactions of carbonyl compounds, amines, and amino acids and proteins.

I found CMPUT 274 to be a transformative course. I learned so much about programming and problem-solving. This course is extremely fast-paced; I spent many hours learning and doing assignments. This course alongside CMPUT 275 were the heaviest courses I've taken at university. This class is divided evenly between first-year honors CS and second-year computer engineers, and it is tailored towards people with some knowledge of programming (though I had 0 knowledge of programming when I did this course). To succeed in this course, approach your learning in a hands-on manner. Although some memorization is required, the main point of this class is to have you become proficient at programming and problem-solving. The class GPA was 2.9/4.0.

This course is half Python and half C++. In the first half of the course, you will learn the basics of programming (variables, statements, conditionals, loops). You will then learn functions, recursion, arrays, data structures, and testing and debugging. These concepts will be taught using Python. The first major assignment in my year was to implement the Huffman compression and decompression algorithm. In the second half of the course, you will learn how to program in C++ and apply it to more advanced algorithms. In particular, the second major assignment involved implementing the RSA cryptography algorithm to communicate between a computer and an Arduino. You will also learn about binary search and various sorting algorithms.

PHYSL 212 is the honors version of human physiology at the University of Alberta. All of our exams are written and we have a final term paper, whereas PHYSL 210 only has multiple choice exams. I really enjoyed this fast-paced course as it covered many interesting body systems. Also, Dr. Mitchell was a funny and good prof. Note that there is a different lecturer for each body system block. My top tips for succeeding in this course would be to use flashcards (e.g., Anki, Quizlet) and draw mindmaps. The textbook was not very helpful for this course. The class GPA was 3.5/4.0.

The blocks in this course were presented as follows: cell physiology (Dr. Mitchell), nervous system (the controversial Dr. Nguyen), autonomic nervous system (Dr. Barton), blood (Dr. Barton), immune system (Stepheny Zani), musculoskeletal system (Dr. Mitchell), cardiovascular system (Dr. Mitchell), and fetal circulation (Nayara Lopes).

This course was more an introductory neuroscience class than a psychology class. Claire is a great prof; she talks quickly, so make sure to write fast. Her exams are extremely fair. You can also do the assignments in groups. As long as you follow the class and textbook, you'll be fine. There are hard grade cutoffs in this course which vary depending on the year. The class GPA was 3.3/4.0.

Doing PHYSl 212 alongside this course makes the nervous system unit of this course extremely easy. The topics in this ourse are as follows: intro to biopsychology, anatomy of the nervous system, neural conduction and synaptic transmission, biopsych methods, visual system, sensory systems, and sensorimotor system.

Get ready for some English comprehension in Dr. Parrish's multiple choice palooza! The most difficult part about this course was not memorizing all 20 essential amino acids, but interpreting the multiple choice answers. A typical question is like (a) glycine, (b) arginine, (c) lysine, (d) all of the above, (e) none of the above, (f) a and b but not c, (g) b and c but not a. Although there is a textbook for this course, it's very dense and not very helpful. Khan Academy or the Organic Chemistry Tutor on YouTube are higher yield resources.

BIOCH 200 covers a wide range of topics. You will learn the following units in order: biomolecules, water, nucleotides and nucleic acids, protein structure and function, enzymes, biological membranes, metabolism, aerobic and catabolism of glucose. If you want to learn about metabolic pathways, you will need to take higher level BIOCH courses.

Like CMPUT 274, I found this course to be transformative. This course focuses purely on algorithms and data structures in C++ (you will use C++ to program an Arduino). The best way to succeed here is to do a ton of practice, whether you use LeetCode, Kattis, HackerRank, etc. YouTube videos about specific algorithms may also be helpful. Perhaps the most stressful aspect about CMPUT 275 are the morning problems. In my year, morning problems were graded, 30-minute coding challenges that you needed to complete at the end of every class. The silver lining is that they are worth so little that you can get 0% on all of them and still pass the course with an A+. Be warned that CMPUT 275 is extremely fast-paced and will likely take up a huge portion of your time, so make sure to plan accordingly.

In terms of concepts, you will learn the following classes of algorithms: brute force, divide and conquer, graph algorithms, and dynamic programming. Of note are the graph theory concepts like depth search first, breadth search first, and Dijkstra's algorithm. These graph concepts were used in the implementation of a Google Maps-esque project with an Arduino and a touchscreen display. You will also learn and implement common data structures such as linked lists, hash tables, binary trees, min/max heaps, etc. In terms of systems concepts, you will learn hardware basic (circuit design, input, output), programming pipeline (compile, link, execute), makefiles, memory hierarchy, and caching. In my year, we also had a final project where we proposed, implemented, and presented a project.

Enver was a pretty great prof with good notes and clear explanations. The major issues with this course were the exams—they were way too long for the time allotted, as almost nobody finished the midterm on time. One YouTube series that was of great help was 3Blue1Brown's essense of linear algebra as they give a fantastic gemoetrical perspective into the concepts you learn. Linear algebra concepts come back in the majority of your future CS, math, and engineering courses, so make sure to do well here.

As with most introductions to linear algebra, you start by learning how to solve systems of linear equations with row and column operations. You then transition into vectors in n-space, matrix algebra, determinants, and linear transformations. The first part is likely the most intuitive portion of MATH 125. The second half of MATH 125 goes into more abstract topics such as eigenvalues and eigenvectors, complex numbers, dot product, cross product, orthogonality, and diagonalization. Since the second half of the course is more conceptually difficult, try to do as well as possible on pre-midterm material.

Really cool and low-stress introduction to neuroscience. Each week, different lecturers come in to present on various topics ranging from psychiatry to neurosurgery. The course grade breakdown consists of two midterms worth 20% each and a final exam worth 60%. The first midterm will be the easiest exam you will ever write, as the class average is usually 90%+. Don't let this lull you into a false sense of security. The second exam is much more difficult relatively speaking, and the class average will be in the low 70s. The final exam is pretty reasonable and is on a difficulty level between the two midterms. All of the exams are multiple choice.

The following lectures were presented in my year: overview and history of the brain, neuroanatomy, memory disorders, clinical and research techniques for investigating the human brain, a brief introduction to machine learning on psychiatry, developmental genetics of the human brain, infections of the nervous system, pain, movement disorder, intellectual disability, brain circulation and stroke, autism spectrum disorder, headaches, neuropathology, surgery of epilepsy, dementia and memory, epilepsy, sleep, multiple sclerosis, the addicted brain, spinal cord injury, intro to psychiatry, and schizophrenia.

This course was a fantastic continuation of PHYSl 212. As with PHYSl 212, all of the exams are written and you will also have to write a term paper. I found the most effective way of learning was to use flashcards (Anki) and draw mindmaps to solidify the concepts.

The blocks in this course were as follows: respiratory system (Dr. Mitchell and Dr. Beaudin), renal system (Dr. Mitchell and Stepheny Zani), gastrointestinal system (Dr. Clugston), endocrine system (Dr. Barton), and reproductive system (Dr. Hamza).

This course is largely a review of high school chemistry with a few extra special cases. Dr. Gedik is a gem of a prof as she is good at explaining concepts, provides very wholesome review sessions, and responds to emails quickly. Overall a very relaxed course compared to the rest of first-year engineering. The class GPA was 2.3/4.0.

The five units you will learn in this course are (i) kinetics, (ii) equilibrium, (iii) coordination chemistry, (iv) thermodynamics, and (v) electrochemistry. Perhaps the only difference from high school chemistry is the coordination chemistry unit and a small portion of the equilibrium unit.

I still have nightmares about the assignments and final exam from this course :<, so be warned. ENGG 130 is your standard introduction to statics, i.e., F_net = 0. In this course, you will apply a bunch of seemingly random linear algebra concepts such as cross product, triple scalar product, determinants, and the such to solve really wacky 3D equilibrium problems. Don't worry about the math too much here; you will learn what all of it means in your linear algebra course (MATH 102) in the following semester. As I foreshadowed, this course is extremely computationally tedious (e.g., solving a system of equations with 7 unknowns). Each assignment may take a few hours of work just to chug through the algebra and geometry. To do well in this course, be EXTREMELY careful with your numbers and vectors, as one wrong number or sign can result in a catastrophically wrong answer. Also, try to do well on the assignments as the exams are quite difficult and will likely drag down your overall grade. Final word of advice: this course has consolidated exams across all sections, so no matter what prof you have, you will have the same exams. That means you can attend any prof's class and still do well. Also, check out Jeff Hanson on YouTube; he is hands-down the single factor that saved my grade in this class. The class GPA was 2.2/4.0.

As with a standard statics class, you will learn about particle equilibrium, moments, equilibrium in 2D and 3D, truss analysis, bending moment diagrams, dry friction, centroids, and moments of inertia. The first part of the course involves a ton of algebra and geometry. The second part of the course involves calculus and geometry.

This course is loved by some, hated by others. If you have Dr. Moore, get ready for a ton of calculus and seemingly random derivations using math you will learn in your second-year math courses. Don't worry if you don't understand the derivations; focus on the key concepts and how to use the derived equations. There will be a ton of equations, so playing formula bingo will likely be detrimental to your course grade. The class GPA was 2.5/4.0.

This course begins with oscillatory motion and the differential equations which govern such motion. You then will generalize simple harmonic oscillations into waves; you will be introduced to the wave equation, a partial differential equation which governs how waves propagate through space. These concepts will be applied to acoustic waves (e.g., bulk modulus, phase velocity, group velocity), which will lead to the power carried by waves. After this unit, you will cover optics. You will finish the course by considering light waves, which is arguably the most difficult part of this course because there are so many concepts that are similar yet very different to solve.

EN PH 131 is the continuation of ENGG 130, but now F_net ≠ 0! Also, this course on dynamics is thankfully a lot less tedious algebra-wise than statics. To do well in this course, make sure you understand all of the examples in presented class and do a bunch of problems from the Hibbeler textbook. You'll also want to pay special attention in MATH 101 in the differential equations unit as you will likely use separation of variables quite a bit. The class GPA was 2.7/4.0.

In this course, you will learn about particle kinematics and dynamics: rectilinear particle kinematics, particle dynamics, work and energy, linear momentum and impulse. You will then learn about kinematics and dynamics of a rigid body.

This math course is quite controversial in terms of student enjoyment. Although technically an extension of calculus, this course takes a very different approach in terms of the rigour involved in the derivations and the way concepts are presented. At times, it will feel like the concepts are disconnected, as if you are learning a bag of tricks to solve ODEs and PDEs; in a sense, this is true. Differential equations are pretty fundamental in engineering, so make sure you learn the material in this course well. The class GPA was 2.4/4.0, though this math course was graded much more nicely in terms of part marks than the other calculus courses.

The concepts presented in MATH 201 are pretty standard: you'll learn about techniques for solving first- and second-order ODEs, series solutions to ordinary differential equations (ODEs), Laplace transform as a method to solve ODEs, Fourier series, and separation of variables for partial differential equations (PDEs). For PDEs, you will learn how to solve the wave equation and the heat equation with many different boundary conditions.

This course was a standard introduction to multivariable calculus. Tips for doing well include perusing Paul's Online Math Notes and grinding through as many practice exams as possible. Important concepts in this course include the chain rule for partial derivatives, line integrals, surface integrals, divergence theorem, Green's theorem, and Stokes' theorem (especially if you're an EE/CompE/EngPhys student as electromagnetism is written in the language of vector calculus). The class GPA was 1.7/4.0 (yikes!).

This course essentially finishes the Stewart calculus textbook. You will start by learning about partial derivatives (e.g., chain rule, Lagrange multipliers, gradient), move on to multiple integrals (in multiple coordinate systems), and finish with the grand finale of vector calculus. The vector calculus unit is arguably the most important as you learn line integrals, surface integrals, curl / div / grad, divergence theorem, Green's theorem, and Stokes' theorem. The course final is consolidated across all sections, so all students will face an equally difficult exam during finals season!

This course can count as either a complementary studies elective or impact of technology on society (ITS) elective, but not both. If you're hesitant about a 300-level SOC course, don't worry; all of the concepts are easy to understand. In my opinion, SOC 366 was a fantastic introduction to historical and contemporary work perspectives with a nice nod to Canadian industries. You also learn about engineering industries, as this course is restricted to engineering students. Per Nicole's syllabus, "this course will challenge you to understand the role of engineers in driving industrial and social transformation, and provide insights into how such broad change impacts engineering work itself." Nicole's exams are very fair, and they are part multiple choice, part written. The only thing is that you should get ready to read ~50 pages of the textbook each week, as the exam will contain content from the text. Honestly, the best way to do well is to take good notes as the exams are open book. The class GPA was 3.4/4.0.

This course will begin with an introduction to the "old" and "new" economy. You will then transition to management, innovation and failure, unions and industrial relations, labour markets, inequality, and diversity at work. You will finish by exploring households, family, care, the meaning of work, alternative approaches, and the future of work (especially related to technological change). Many of the concepts are directly relevant to our everyday lives, so I would wholeheartedly recommend this course to any interested engineering student.

This is another course where students either hate or love it. STAT 235 is a fast-paced introduction to statistics for engineers. My top tips for doing well is to create a killer formula sheet, practice as many assignment and practice exam questions as possible, and develop a strategy for recognizing when different tests or probability distributions are used. In my year, the stat labs were optional attendance to any open section. I would recommend attending as the TAs pretty much just tell you the answers to the labs. This class had hard grade cutoffs. The class average for this course was 1.7/4.0 (again, yikes!).

This course is broken into two major parts: probability and statistical tests. In the probability section, you will learn about the axioms of probability, random variables, and discrete and continuous probability distributions. The statistical test portion include t-tests, linear regression, ANOVA, and inferences for proportions. Linear regression will be a HUGE focus in this course, as it is extremely important in many future courses (and likely in your future career). As such, it will also be a major focus in the exams, so ensure you know it inside and out.

ECE 202 is an extension of the circuits concepts you learned in high school. Other than to derive maximum average power and capacitor and inductor phasor relationships, there is minimal calculus in this course, i.e., ECE 202 is all about algebra and problem solving. Xihua was one of the best profs I had: good pace, efficient, tons of examples, good homework practice, and excellent summary sheets. The class average was 2.9/4.0.

You will start off by reviewing Kirchhoff's laws (KVL and KCL) then quickly build upon them with more powerful analysis techniques with DC circuits. The major techniques include loop analysis, mesh analysis, superposition, source transformation, Thevenin theorem, and Norton theorem. The latter part of this course introduces AC circuit analysis in the frequency domain; all of the DC techniques apply to the AC case. The course finishes with an introduction to complex power, magnetic coupling, and transformers.

Very interesting topics and extremely non-traditional teaching style with Duncan Elliot. Duncan provides so many bonus marks on his test that in my year 10+ students got over 100% on the final exam. The labs were quite fun, though get ready to self-learn the basics of VHDL because the course and lab section don't go at the same pace. Overall, other than occasionally super long lab assignments, this course was pretty low stress for high reward. The class average was 2.8/4.0.

The course introduces Boolean algebra, logic gates, Karnaugh maps, MUX/DEMUX, adders, subtractors, programmable logic devices, FPGAs, finite state machines, and everything else in a standard introduction to digital logic course.

This class presented a straightforward approach to the first and second laws of thermodynamics. The majority of the problems in this course involved setting up energy and mass balance equations then plugging in values from thermodynamic tables. There are short assignments due each week, which are a great source of practice (and free marks). The class GPA for this course was a 2.7/4.0. No matter which prof you have for CH E 243, you will be in good hands.

The first half of the course presented fundamental tools for energy and mass analysis alongside applications to thermodynamic devices (e.g., throttler, diffuser, turbine). The second half of the course focused on the efficiency and entropy of these devices and thermodynamic cycles. This course covered the Otto, Diesel, Brayton, refrigeration, and Rankine cycles.

Circuits II is a continuation of circuits I. All of the network theorems and analysis techniques (minus magnetic coupling and complex power) apply in this course, so make sure you know them by heart. In my opinion, the best way to do well in this course is to grind practice problems and sample exams. Top tips: know Norton, Thevenin, mesh, and loop analysis; make a nice summary sheet on bode plots and op-amps; do a bunch of practice questions; and you'll be golden. The class GPA for this course was 3.1/4.0.

The first part of this course focuses on analyzing first- and second-order circuits (i.e., using differential equations). Don't worry if you're rusty at solving ODEs—this course presents many shortcuts to solving them. The second part of this course analyzes circuits in the frequency domain. Here, we rely on phasors and eventually the Laplace transform. Bode plots are also introduced to analyze the frequency response of circuits. The third part of this course studies operational amplifiers and diodes, which play a key role in larger integrated circuits.

If you liked Newtonian mechanics, you'll love this course! This was one of the most difficult classes I've taken, courtesy of Kirk, but also one of the most satisfying and beautiful courses I've had the pleasure of studying. Kirk is extremely passionate about teaching physics and has very high expectations for his students. He also programmed a physics simulator on the Apple App Store, which is frequently used in class to show some cool simulations. The class GPA for this course was 2.8/4.0.

The most difficult part about this course is setting up the equations and being extremely diligent with algrebra/calculus. Each assignment took multiple hours to complete, so beware. Nonetheless, you will prove interesting problems in the assignments, such as: (i) Is there a contradiction in Netwon's third law for two charges approaching each other from orthogonal angles? (ii) What is the angular frequency of a spring on a rotating system? (iii) Why do planets orbit the sun such that the line between the planet and the sun sweeps out equal areas in equal times? (iv) How do we generate symmetry? To do well in this course, understand the fundamental theorems well, be careful with your computations, and practice, practice, practice!

The first half of this course reviews many concepts from first year physics but takes them one step further; you'll learn Newtonian mechanics in polar and cylindrical coordiantes, intrinsic coordinates, velocity-dependent forces (e.g., drag), linear and angular momentum, and energy in the context of vector calculus. Then, you will analyze oscillating systems: damped oscillations, coupled oscillators, and small oscillations. The second half of this course is where things get spicy. You first prove the Euler-Lagrange equations and other topics in calculus of variations (e.g., geodesic problems, Brachistochrone problem) then use the Euler-Lagrange equation in the context of Lagrangian mechanics. Lagrangian mechanics is a powerful extension of Newton's second law in scalar form, and you will be able to solve increasingly complex problems such as the double pendulum problem in an algorithmic manner. Lagrangian mechanics leads to one of the most beautiful theorems about symmetry and conserved quantities: Noether's theorem. Towards the end of the course, you will learn about two-body central force problems, an extension of Lagrangian mechanics, which plays a critical role in orbital mechanics and quantum mechanics. Finally, you will cover the basics of Hamiltonian mechanics.

Ever watched Intestellar or wondered what Einstein did? Well now you can learn all about it! This course was a whirlwind introduction to special relativity, quantum mechanics, nuclear physics, and particle physics. JP is a pretty chill and funny prof, so you're in good hands in this course (though I'm not a fan of the PowerPoint slides you learn from). The special relativity unit is arguably the wackiest, where you learn about time dilation, length contraction, and relativistic mechanics. You will also learn that E = mc² is simply a special case of the more general energy-momentum relationship, E² = m²c⁴ + p²c² (this relationship will be derived by integrating the derivative of relativistic momentum with respect to position). After special relativity, you'll learn how light and charge became quantized and then delve into wave-particle duality alongside the Heisenberg uncertainty principle. You will then derive the Schrodinger equation and see how it leads to many crazy phenomena like quantum tunnelling and the EPR paradox. You will finish the course by deriving the quantum numbers (which form the basis for modern chemistry) and see the necessity for relativistic quantum mechanics. The class GPA for this course was 2.6/4.0.

Honestly, the best way to do well in this course is to take advantage of the extremely easy quizzes as the midterm and final will likely drag down your mark. For the special relativity unit, understand the meaning behind every equation, as plug and chug will not work here. For quantum mechanics, learn how to solve the Schrodinger equation and how to interpret and use the equations you derive from it. For most students, special relativity is a lot more difficult to understand than the quantum mechanics unit.