Find online courses made by experts from around the world.
Take your courses with you and learn anywhere, anytime.
Learn and practice realworld skills and achieve your goals.
A mathematical way to think about biology comes to life in this lavishly illustrated video book. After completing these videos, students will be better prepared to collaborate in physical sciencesbiology research. These lessons demonstrate a physical sciences perspective: training intuition by deriving equations from graphical illustrations.
"Excellent site for both basic and advanced lessons on applying mathematics to biology."
Tweeted by the U.S. National Cancer Institute's Office of Physical Sciences Oncology
Not for you? No problem.
30 day money back guarantee
Forever yours.
Lifetime access
Learn on the go.
Desktop, iOS and Android
Get rewarded.
Certificate of completion
Lecture 1  01:48  
This video outlines contents from the course 

Section 1: Algebra and precalculus (Metodo pratico de canto)  

Lecture 2  04:07  
In this and the following three videos, we will review the concept of quantity, which is represented by numbers. In this video, we review two ways in which we learned to think about numbers in elementary school. We used numbers to refer to the idea of having distinct manipulatives, and we used numbers to refer to the idea of labeling geographic locations with addresses. 

Lecture 3  04:40  
The analysis of a system of particles display BoseEinstein statistics is an example of a situation in which it is important to be aware whether we are thinking of numbers in terms of distinct manipulatives or in terms of addresses on a street. Incorrectly assuming that atomic and subatomic particles are just as distinct as the plastic counting manipulatives from kindergarten leads to overestimating the number of ways that particles can be excited out of the lowest energy state. In some situations, a system of particles that tends to occupy the lowest energy state in a way that is quantitatively consistent with thinking of numbers in terms of addresses (rather than thinking of particles as distinct manipulatives) is sometimes referred to as a BoseEinstein condensate. 

Lecture 4  02:27  
Numbers can be represented using a number line, a wedge, and placevalue representation. The application of memorized rules for performing arithmetic on numerals formatted in placevalue representation is called algorism. 

Lecture 5  01:42  
Infinity is not a number. There is no tick mark on the number line labeled "infinity." 

Lecture 6  05:42  
This slide deck presents aspects of quantitative "vocabulary" (variables) and quantitative "grammar" (functions and function composition) that will allow us to express quantitative reasoning in future slide decks. In this first of five videos, we note that it is cumbersome to describe quantitative relationships purely through the enumeration of repetitive examples involving concrete numbers. This difficult can be addressed with the assistance of abstract "placeholder," "standin" symbols. A variable is a symbol that stands in for a number at once arbitrary, yet specific and particular. Using variables, we can communicate quantitative relationships concisely. 

Lecture 7  04:45  
Functions are basic buildingblock sentences of mathematical reasoning. A function relates input values in a domain to output values in a codomain, and these associations can be depicted using plots. While different disciplines use slightly different definitions of a function, an essential stipulation familiar to scientists and mathematicians from a variety of fields is that a function associates each input value with precisely one output value. 

Lecture 8  02:33  
Functions can be combined by using the output of one function as the input for another function. The resulting object is a composite function, which is one way to combine mathematical ideas to derive mathematical conclusions. 

Lecture 9  02:55  
When two functions are called each other's inverses, they can be composed. The overall composite function has the property that the value entered as an input is returned as an output. The plot of the composition of inverse functions is the diagonal line y = x. 

Lecture 10  05:15  
When we try to think of an inverse of the squaring function, we encounter two difficulties. One problem is that the reflection of the parabola y = x^2 is, in many places, doublevalued, and, thus, not a function. Second, this plot does not explore negative input values. When we attempt to address this second difficulty, we develop the idea of the imaginary root i, which, when squared, gives 1. Knowledge of the imaginary root because will help us to study oscillatory dynamics in a later slide deck. 

Lecture 11  06:04  
Graphical and analytic understanding of solving the quadratic equation Plotting quadratic functions Completing the square 

Lecture 12  05:59  
The geometry routinely used by physical scientists on a daytoday basis is only a small portion of the typical high school course. Useful concepts include the notion of a flat space (as opposed to a curved space), as well as the Pythagorean theorem. 

Lecture 13  03:04  
The unit circle is a circle of radius one centered at the origin of the xycoordinate plane. The location of a point on a circle is specified by the angle θ it sweeps counterclockwise from the x axis. The location of a point is also specified using its corresponding x and ycoordinates, which, in this context, are referred to as cos(θ) and sin(θ), respectively. 

Lecture 14  05:13  
Using the Pythagorean theorem to relate the lengths of sides of triangles drawn in the context of a circle, we estimate π. We also provide a mnemonic for memorizing π to 6 digits. This allows us to understand that the tick marks on the horizontal axis of the function plots from the previous video correspond to numerical values. 

Lecture 15  06:11  
Even though sine and cosine are fundamentally defined as functions that provide the y and xcoordinates, respectively, of points on the unit circle, sine and cosine are also regarded as "trigonometric" functions, which describe the geometry of right triangles. We practice applying this perspective as we derive two examples of identities involving sine and cosine. 

Lecture 16  04:05  
Introduction to Greekletter Sigma summation notation Gauss summation trick, which is used when counting the number of pairwise interactions in a population of components 

Lecture 17  04:29  
When adding more and more numbers to a running total, the running total becomes arbitrarily close to a finite number. Another possibility is that the running total becomes arbitrarily large. As the examples in this video show, both of these outcomes are possible even if we are assured that individual terms in a sum are becoming successively smaller. 

Lecture 18  03:20  
How many ways can we arrange n distinct objects in n slots? The answer is n (n  1) (n  2) . . . 3 * 2 * 1. Because this kind of calculation appears often in the study of probabilities, we give it a symbol called the factorial: n! = n (n  1) (n 2) . . . 3 * 2 * 1. 

Lecture 19  06:59  
When we counted the number of ways to arrange objects in a row in the last video, we assumed that all the objects were distinct from each other. In this video, we relax this assumption and obtain the famous (L + N)! / (L! N!) formula for counting the number of ways to arrange L indistinguishable objects and N indistinguishable objects together in a row. This is also the formula for counting the number of combinations of L objects drawn from a container of L + N objects.  
Lecture 20  05:05  
The formula for counting combinations from the previous video helps us to write an expression for the binomial quantity (x + y)^p. The resulting sum can contain many terms. However, in some applications, only a small number of terms is necessary for approximate calculations. 

Section 2: Calculus (Arie antiche)  
Lecture 21  07:38  
Informally, when we say that the limit of a function as x approaches a is L, we mean that as x becomes arbitrarily close to a, the function becomes arbitrarily close to L. This idea is made more precise using the εδ definition. 

Lecture 22  03:57  
When we say that the limit of a function at a value of x = a is infinity, we mean that as x becomes arbitrarily close to a, the value of the function becomes arbitrarily large. 

Lecture 23  03:30  
When we say that a function has a limit of L "at" infinity, we mean that as x becomes arbitrarily large, the function becomes arbitrarily close to L. 

Lecture 24  03:32  
When we say that a function has an infinite limit "at" infinity, we mean that as x becomes arbitrarily large, the function becomes arbitrarily large. 

Lecture 25  02:40  
An example of a situation in which a function can fail to have a limit at a value of x = a is when the function jumps discontinuously in height at that value of x. One example of a situation in which a function can fail to have a limit at infinity is an oscillatory function that fails to approach a particular value of y = L because it keeps swinging with sustained amplitude up and down through y = L. 

Lecture 26  06:59  
In this video, the outline for using the epsilondelta definition to prove that the limit of a function has a particular value y = L at x = a has two main parts. First, we determine what range of y values the function takes when x is restricted to intervals on either side of the value x = a of interest. Then, we ask whether we can narrow these intervals sufficiently to ensure that the range of y values taken by the function is contained within a range of y values of interest centered at y = L. When we conclude that this can be done for any finite range of such y values, we conclude that the limit of interest exists. 

Lecture 27  08:47  
The goal of this and the next 4 videos is to formalize an idea of "slope" and then to build a cribsheet of rules for studying the slopes of some example functions. In this video, we define the derivative, caution against interpreting differentials as numbers, and remark that derivatives do not always exist. It is important to become familiar with derivatives because they provide a basic vocabulary for talking about dynamical systems in the natural sciences (including in biology). 

Lecture 28  07:23  
Power law functions can serve as ingredients for more complicated structures because, as we will later learn, many more complicated functions can be approximated as sums of power laws. For this reason, it is important to learn the power rule for calculating the slopes of such expressions. The power rule is written d(x^n)/dx = nx^(n1).  
Lecture 29  03:54  
One way to combine basic functions in more sophisticated structures is to nest functions sequentially within each other. This is called "composition of functions." The chain rule is used to study the slopes of composite functions. The rule is written d(g(f(x))/dx = dg(f(x))/df(x) df(x)/dx or d(g(f))/dx = dg/df df/dx and memorized using the mnemonic, "derivative of a function with an outside and an inside is derivative of the outside times derivative of the inside."  
Lecture 30  04:25  
Another way to put basic functions together to make more sophisticated structures is to write their expressions next to each other as a product. In this video, we derive the product rule, which is used in such situations. The product rule is written d(fg)/dx = (df/dx)g + f(dg/dx) and memorized by reciting "derivative of the first, times the second, plus first times derivative of the second." 

Lecture 31  08:27  
The derivative of sine is cosine, and the derivative of cosine is negative sine. This backandforth relationship (with a negative sign in one of the equations) is a hallmark of dynamical systems that might support oscillations. Thus, this pattern, which you will derive in this video, is important to keep in mind when you later study biological oscillations. 

Lecture 32  06:14  
When a function depends on multiple independent variables, the "partial" symbol is reserved to denote slopes calculated by jiggling one independent variable at a time 

Lecture 33  06:54  
This set of four videos introduces power series representations. Using a power series representation is like using decimal representation. Both techniques organize the description of the target object at levels of increasing refinement. In this first video, we show that the second derivative corresponds to the curvature of a plot. In this way, we strengthen intuition that higherorder derivatives can also have geometric interpretations. 

Lecture 34  13:51  
We imitate a function by combining the descriptions of its geometric properties as embodied in its value and the values of its higher derivatives at an expansion point.  
Lecture 35  05:06  
We obtain a power series representation for the sine function expanded about the point θ = 0.  
Lecture 36  04:50  
Using the first three terms of the power series representation for sine we obtained in the previous video, we iteratively approximate π to four decimal places.  
Lecture 37  10:02  
In these four videos, we develop a familiar with integration that will later be useful for deducing functions of time (e.g. number of copies of a molecule as a function of time) using rates of change (e.g. the first derivative of the number of copies of a molecule with respect to time). In this first video, we develop the concept of the definite integral in terms of the area under a curve. 

Lecture 38  03:52  
We demonstrate that differentiation undoes integration. This is called the first fundamental theorem of calculus.  
Lecture 39  09:19  
We demonstrate that integration undoes differentiation. This is called the second fundamental theorem of calculus. This theorem allows us to construct a table of integrals using differentiation rules we previously learned.  
Lecture 40  06:28  
Sometimes, superficial differences can make it seem that a listing in an integration table does not match the integral we want to study. We develop a change of variables (also called a "usubstitution") rule that can sometimes help us to identify a match between an integral we want to study and a listing in a table.  
Lecture 41  05:48  
Two wrongs make a right Tear two differentials apart as though they retained meaning in isolation Slap on the smooth S integral sign as though it were a unit of meaning itself, even without a differential You get the same integral expression you would obtain longhand using usubstitution or "change of variables" in integrals 

Lecture 42  07:10  
Compounding interest with arbitrarily small compounding periods Power series representation of exp(x) 

Lecture 43  02:06  
exp(0) = 1 

Lecture 44  06:05  
(exp(x))^p = exp(px) 

Lecture 45  05:57  
exp(x)exp(y) = exp(x+y) 

Lecture 46  02:20  
Mnemonic for memorizing e = 2.718281828459045... 

Lecture 47  05:06  
The natural logarithm is the inverse of the exponential 

Lecture 48  03:46  
The indefinite integral of 1/x is ln(x) + C 

Section 3: Calculus for biology (Handel)  
Lecture 49  05:15  
Concepts of stochasticity underlie many of the models of dynamic systems explored in quantitative biology. We describe some of these ideas in this and the following three videos. In this video, we state that systems exhibiting deterministic dynamics can sample a messy variety of waiting times between chemical reaction events even when the motions of component parts are periodic. Particularly, this can happen when the periods of motion of individual parts are incommensurate (pairs of periods form ratios that are irrational). 

Lecture 50  04:29  
In a deterministic system with complicated interactions, small differences in initial conditions can quickly avalanche into qualitative differences in dynamics. Since initial conditions can only be measured with finite certainty, the dynamics of such systems are, for practical purposes, unpredictable after short times. 

Lecture 51  05:03  
In the previous two videos, deterministic systems displayed dynamics with aspects associated with stochasticity. In contrast, some systems not only mimic some aspects associated with stochasticity, but, instead, display indeterminism at a fundamental level. For example, when a collection of completely identical systems later displays heterogeneous outcomes, the systems are fundamentally indeterministic. They have no initial properties that can be used to discern which individual system will display which particular outcome. 

Lecture 52  04:46  
Markov models are often used when developing mathematical models of systems which partially or more fully display aspects associated with stochasticity (depending on how fully a system displays aspects associated with stochasticity, the use of a Markov model might need to be recognized as a conceptual approximation). Icons that can represent the use of such models include spinning wheels of fortune and rolling dice. 

Lecture 53  08:57  
In this and the following three videos, we present a canonical worked problem that is presented in introductory systems biology coursework. For an example of this mathematical lesson, see Alon, Ch. 2.4, pp. 1821. In this video, we animate a time sequence of translation and degradation events that cause the number of copies of a protein of interest in a cell to change over time. 

Lecture 54  09:50  
We derive a differential equation approximating the timerate of change of the number of copies of protein in the cell modeled by the animation in the previous video. This differential equation reads, dx/dt = β  αx. We depict aspects of this differential equation with a flowchart. It is important to remember that this differential equation does not represent all aspects of the stochastic dynamics in the toy model presented in the previous video. 

Lecture 55  04:12  
We sketch a slope field corresponding to the differential equation derived in the previous video. We use this slope field to draw a qualitative curve describing how the number of copies of protein is expected to rise over time, when starting from an initial value of zero. 

Lecture 56  11:02  
We obtain an analytic solution for the relationship between the number of copies of protein and time for the differential equation qualitatively investigated in the previous video. We find that the rise time, T1/2, is ln(2) divided by the degradation rate coefficient, α. The fact that the rise time is independent of the translation rate β is sometimes used as a pedagogical example of the importance of quantitative reasoning for gaining insights into biological dynamics that would be difficult to develop through naturallanguage and vaguelystructured notional reasoning alone. 

Lecture 57  13:02  
Using a collision picture to understand why reaction rates look like polynomials of reactant concentrations 

Lecture 58  11:58  
Cooperativity of a simple (oversimplified) kind 

Lecture 59  06:32  
How Hill functions, considered in combination with linear degradation, can support bistability 

Lecture 60  15:35  
This video introduces collisional population dynamics and tabular game theory (comparative statics). The particular game in this example is the prisoner's dilemma. In this game survival of the relatively most fit occurs simultaneously with decrease in overall fitness. For a printable tutorial explaining how evolutionary game theoretic differential equations can be applied to analyze population dynamics, please refer to doi:10.1098/rsfs.2014.0037. 

Lecture 61  12:32  


Lecture 62  19:20  
In the previous slide deck, we noted similarities between population dynamics and business transaction payoff pictures. In this and the next video, we provide deeper understanding of these connections. In this video, we derive the population dynamics equations in such a way that it is natural to say that cells being modeled repeatedly play games and are subject to game outcomes. For a printable tutorial describing interpretations that can be associated with evolutionary game theoretic differential equations, please see doi:10.1098/rsfs.2014.0038. 

Lecture 63  10:58  
Repeated simple interactions in a population of robotic replicators can achieve results seemingly related to results obtained from sophisticated computations. The use of population dynamics and business transaction payoff matrix analyses from the previous slide deck to obtain this understanding is an example of quantitative reasoning.  
Section 4: Probability and statistics (Debussy)  
Lecture 64  05:54  
The first of five videos on introductory statistics, this module introduces probability distributions and averages. The average (also called "arithmetic mean") quantitatively expresses the notion of a central tendency among the results of an experiment. 

Lecture 65  03:43  
The average of a sum is the sum of the averages. The average of a constant multiplied against a function is the constant multiplied by the average of the function. The average of a constant is the constant itself. 

Lecture 66  05:33  
The variance of a function is the average of the square of the function. For the purposes of theoretic calculations, it might be useful to express the variance using the "insideout" computation formula described in this video. 

Lecture 67  06:26  
Two variables are said to be statistically independent if the outcome of an experiment tracked by one variable does not affect the relative likelihoods of different outcomes of the experiment tracked by the other variable. The twovariable probability distribution factorizes into two probability distribution functions. 

Lecture 68  07:20  
The covariance of statistically independent variables is zero. The variance of a sum of statistically independent variables equals the sum of the variances of the variables. This identity is often used to derive uncertainty propagation formulas. 

Lecture 69  03:49  
This slide deck provides examples of how hypotheses about probabilistic processes can be used to discuss probability distributions and obtain theoretical values for averages and variances. In this first video, we describe the Bernoulli trial, which corresponds to the experiment in which a coin is flipped to determine on which of two sides it lands. 

Lecture 70  07:18  
In this second video in this slide deck, we discuss the binomial distribution. This distribution describes the probability of getting x heads out of N coin tosses (Bernoulli trials), each individually having probability p of success. 

Lecture 71  08:13  
In the Poisson limit, we take a series of [independent] Bernoulli trials (giving rise to a binomial distribution) and allow the number of coin flips N to increase without bound while allowing the chance p of success on a particular coin flip to decrease without bound in such a compensatory fashion that the average number of successes ("heads") is unchanged. Because the likelihood of "heads" on any given toss decreases without bound, this limit is called the limit of rare events. 

Lecture 72  11:22  
To study the combinatorics involved in an example where the central limit theorem applies, we will need to work with the factorials of large numbers. Stirling's approximation is an approximation for n! for large n. In this video, we motivate this approximation by comparing the expression for ln(n!) with an integral of the natural log function. 

Lecture 73  06:37  
The central limit theorem states that a Gaussian probability distribution arises when describing an overall variable that is a sum of a large number of independently randomly fluctuating variables, no small number of which dominate the fluctuations of the overall variable. 

Lecture 74  09:01  
In some situations, when the number of coin tosses is large, Stirling's approximation can be applied to factorials that appear in the expression for the binomial distribution. The resulting expression is basically an exponential function of a quadratic function with a negative leading coefficient. This is the hallmark of a Gaussian distribution. 

Lecture 75  03:50  
For a Gaussian distribution, roughly twothirds of the probability is found within the first standard deviation. 

Lecture 76  08:25  
Because equipment in physics experiments is highlyengineered, individual device contributions to measurement fluctuations might be "small." The overall fluctuations in the final measured quantity might be well approximated using a firstorder Taylor expansion in terms of individual device fluctuations. Fluctuations in measurements are thus sums over random variables, and thus, potentially Gaussian distributed. 

Lecture 77  10:35  
The levels of molecules in biological systems can approximate "temporary" steadystate values that equal products of rate coefficients and reactant concentrations. Since logarithms convert products into sums, the logarithms of the levels of some biological molecules can be normally distributed. Hence, the levels of the biological molecules are lognormally distributed. 

Section 5: Uncertainty propagation  
Lecture 78  07:56  
Quadrature formula is a result of Taylor expanding functions of multiple fluctuating variables, assuming that fluctuations are independent, and then applying the identity "variances of sums are sums of variances"  
Lecture 79  09:21  
Standard deviation vs. sample standard deviation Mean vs. sample mean Standard deviation of the mean vs. standard error of the mean 

Lecture 80  05:27  
Origin of the famous factor of sqrt(n), which is the ratio by which the standard deviation of the distribution of the sample means is smaller than the standard deviation of the distribution of the measurements (parent distribution)  
Lecture 81  03:23  
Are error bars nonoverlapping, barely touching, or tightly overlapping? What pvalue do people associate with the situation in which error bars barely touch?  
Lecture 82  06:47  
"I quantitated staining intensity for 1 million cells from 5 patients, everything I measure is statistically significant!" It is quite possible that you need to use n = 5, instead of 5 million, for the √ n factor in the standard error. 

Lecture 83  08:18  
In order to identify theoretical curves that closely imitate a set of experimental data, it is necessary to be able to quantify to what extent a set of data and a curve look similar. To address this need, we present the definition of the quantity chisquared. For a given number of measurements, a smaller chisquared indicates a closer match between the data and the curve of interest. In other words, a smaller chisquared corresponds to a situation in which it looks more as though the data "came from" Gaussian distributions centered on the curve. The average chisquared value across a number of experiments, each involving M measurements, is M. 

Lecture 84  13:37  
We slightly modify the definition of chisquared developed in the previous video for the situation in which a "correct" curve has not been theoretically determined beforehand. We choose a "best guess" curve with corresponding best guess values of fitting parameters by minimizing chisquared, which corresponds to maximizing likelihood. 

Lecture 85  03:44  
Using the concepts developed in the preceding two videos, we present a checklist of steps necessary for performing fitting of mathematical curves to data with error bars. These steps include checking whether the reduced chisquared value is in the neighborhood of unity and inspecting a plot of normalized residuals to check for systematic patterns. This algorithm is appropriate for general education undergraduate "teaching laboratory" courses. 

Lecture 86  5 pages  
The purposes of this exercise are (1) to practice sample variance curve fitting in MatLab and (2) to understand that the timesequence according to which data are acquired can affect the apparent size of error bars. This exercise is a PDF, instead of a video, so that you can refer to a printout next to your computer as you work through the commands described.  
Section 6: Computation of stochastic dynamics  
Lecture 87  07:49  
Dynamics of population fractions 

Lecture 88  16:29  


Lecture 89  08:13  
Model: RNA polymerase makes many (usually unsuccessful) independent attempts to initiate transcription and mRNA strands degrade after a precise lifetime Outcome: mRNA copy numbers are Poisson distributed 

Lecture 90  14:07  
Model: RNA polymerase makes many (usually unsuccessful) independent attempts to initiate transcription. Once a mRNA strand is produced, it begins to make independent (usually many unsuccessful) attempts to be degraded. Outcome: As in part a, mRNA copy numbers are Poisson distributed 

Section 7: Linear algebra (Gradus ad Parnassum)  
Lecture 91  05:29  
Motivating example: Modeling dynamics of web startup company customer base  
Lecture 92  09:42  
Vectors, vector spaces, and coordinate systems  
Lecture 93  13:25  
Linear operators, matrix representation, matrix multiplication  
Lecture 94  09:24  
Using eigenvalueeigenvector analysis to solve for the dynamics of the demographics of the webstartup customer base. First, we qualitatively describe the longterm behavior of the system in paying/nonpaying customer population space. 

Lecture 95  16:48  
Now that we have qualitatively described the longterm behavior of the paying and nonpaying customer populations in this model, we obtain mathematical descriptions using eigenvectoreigenvalue analysis. 

Lecture 96  12:58  
Simple quasispecies eigendemographics and eigenrates based on Bull, Meyers, and Lachmann, "Quasispecies made simple," PLoS Comp Biol, 1(6):e61 (2005) Relative dominance in a population is determined, not merely by "fitness" alone, but also depends on the degree to which individuals "breed true." 

Lecture 97  04:24  
Euler's formula: Expanding the exponential function in terms of sine and cosine Complex exponentials in the complex plane Euler's identity exp(iπ) = 1 

Lecture 98  04:51  
In this and the next video, we develop a familiarity with the representation of vector rotations using rotation matrices. This understanding is helpful for identify dynamical systems that support oscillations in physics, engineering, and biology. A rotation operator rotates a vector by an angle without changing the length of the vector. A rotation matrix represents the action of a rotation operator on a vector. 

Lecture 99  08:43  
How can we determine whether a dynamical system can be represented using something that looks like a rotation matrix? Rotation matrices have complex eigenvalues. We can determine whether a dynamical system supports rotational motion by determining whether the matrix representing the system's dynamics has complex eigenvalues. 

Section 8: Differential equations  
Lecture 100  10:16  
CAUTION: I'm not familiar enough with numerical integration to know whether the particular example of the method for stepsize adaptation in the video is used generally (or at all) in commonly available software packages. The purpose of the example was to show that it is possible to generate an error estimate (a) without knowledge of the actual solution and (b) by comparing the solutions from two numerical integration algorithms. 
David's illustrations have been published in Science, Physical Review Letters, Molecular Pharmaceutics, Biosensors and Bioelectronics, and the Proceedings of the National Academy of Sciences.
University of California, San Francisco
Postdoc, 20102012
Tlsty Lab
Harvey Mudd College
BS, Physics, 2005
Advisor: Robert J. Cave
Hours of video content
Course Enrollments
Students
Simply Great
A really great course with understandable material and a good instructor. Worth learning from!
Por favor escribe el título de tu calificacion aquí
Por favor escribe tu crítica aquí
A mathematical way to think about biology
I am very interested in the course but as a biologist I had little maths during my education years. I only have school level maths & it is now very rusty. The lectures are very fast with little to no explanations, if you get struck up. May be others are more familiar with maths required here so it is easier for them. From my point of view the course could be divided into two or three parts & more examples should have been included to make it more intuitive. The language used is really hard to understand. It is very technical.
Great Course.
I love this guy. He's giving me a terrifec mathematical backgound and overview that I can handle.
FAB!! Should be part of EVERY Biology curriculum!