# Prerequisites

To be eligible to apply for any of the UBC Master of Data Science programs, you must meet the prerequisite requirements as outlined below.

The prerequisites can be taken at UBC or at an academic equivalent post-secondary institution. You must have a minimum B+ (76% at UBC) average in your prerequisite courses.

Below each prerequisite course, you will find the learning objectives of each that will assist in making the MDS prerequisites more specific and precise. Please note that a course from another university does not have to have matching learning objectives, but there should be significant overlap between the content of the prerequisite course and what is listed below.

You can also use the UBC Transfer Credit Search Tool to search courses from your institution and how they transfer to UBC. The results shown are for reference only and are subject to change.

At the time of application, you must have proof of completion or enrolment in the following prerequisite courses.

• One course in Programming (3 credits):
• Similar to UBC-V: CPSC 103, CPSC 110, APSC 160  / UBC-O: COSC 111, COSC 123, DATA 301

• Learning objectives:
• Write and execute code in a development environment such as Visual Studio Code, Jupyter Lab, etc.
• Compare and contrast different data types such as integers, strings, etc.
• Use branching logic with if statements or the equivalent.
• Implement repetition using either loops or recursion.
• Write comments to document code.

• One course in Probability and/or Statistics (3 credits):
• Similar to UBC-V: STAT 200, STAT 241/251, STAT 302 / UBC-O: STAT 121, STAT 124, STAT 230

• Learning Objectives for a Probability Course:
• Differentiate between permutations and combinations and apply them to counting problems.
• Define, interpret, and compute the probability mass function (PMF) or probability density function (PDF) of a random variable.
• Link, apply and compute the cumulative distribution function (CDF) to either the PDF or PMF.
• Classify common probability distributions in random variables such as the Normal, Exponential, Uniform, Binomial, Poisson, etc., as either discrete or continuous.
• Define and compute the expected value of a random variable with a given probability distribution.
• Define and compute the variance of a random variable with a given probability distribution.
• Apply the basic properties of the expected value and variance.
• Apply basic probabilistic reasoning to real-world problems.

• Learning Objectives for a Statistics Course:
• Compute and interpret summary descriptive statistics, such as mean and variance.
• Define and contrast location summary statistics such as mean, median, and mode.
• Differentiate and interpret common visualizations such as scatter plots, bar charts, histograms or box plots depending on the type of variable.
• Draw descriptive conclusions on data relationships depending on a plot analysis.
• Define and contrast basic statistical concepts such as a random sample, estimator, estimate and population.
• Explain the concept of a sampling distribution of a parameter estimator.
• Define and differentiate point and interval estimates.
• Interpret confidence intervals of a parameter estimate.
• Explain how sample size affects sampling distributions, estimates and confidence intervals.
• Define and apply the concept of basic hypothesis testing.

AND

• One course in Calculus (3 credits) OR Linear Algebra (3 credits):
• Calculus: Similar to UBC-V: MATH 100 / UBC-O: MATH 100

• Learning Objectives for a Calculus Course:
• Compute limits of basic univariate functions.
• Differentiate basic univariate functions.
• Integrate basic univariate functions.
• Apply calculus to real-world problems.
• Given a graph of a univariate function, draw its derivate.
• Given a graph of a univariate function, draw its integral.
• Given the units of a function's input and output (such as a position in metres vs. time), specify the function's derivative or integral units.

• Linear Algebra: Similar to UBC-V: MATH 221 / UBC-O: MATH 221

• Learning Objectives for a Linear Algebra Course:
• Express a system of linear equations in matrix form.
• In simple cases, perform matrix operations such as matrix addition, matrix-vector multiplication, and matrix-matrix multiplication.
• Assess whether a set of vectors are linearly independent.
• Explain the connection between linear transformations, such as rotations, and matrices.
• Assess whether a matrix is invertible based on its determinant, eigenvalues, or other relevant properties.

NOTE: Completion of a course in both calculus AND linear algebra is strongly recommended.

Note: Online MOOCs/Coursera courses and adult education/continuing studies courses are not accepted but distance education courses are.

For students interested in the MDS in Computational Linguistics, there is, in addition to the technical background required for all MDS students, the expectation that candidates will have a degree and/or other significant experience relevant to language (i.e. a major or minor in linguistics). Candidates should outline this language background in their letter of intent.

Part of the MDS program consists of foundational material in Computer Science and Statistics. If you hold an undergraduate degree in one of these areas, please review our course offerings before making the decision to apply for the program.