Both the M.S. and Ph.D. in Data Science programs have four prerequisites. Both require linear or matrix algebra, statistics, and programming. However, the MSDS requires Single Variable Calculus (Calculus I/II) and the Ph.D. requires Multivariable Calculus (Calculus I/II/III).
If you completed the prerequisites under a different title (e.g., instead of Calculus III, the course is listed as Mathematics IV on your transcript), you must submit a course description and/or syllabus of the course to confirm the completion of the prerequisites.
Competitive applicants have their prerequisites completed or in progress at the time of application. Proof of completion will be required for any incomplete prerequisites if an applicant is admitted and accepts their offer of admission, and all prerequisites must be completed by the deadline in your offer letter.
Refer to the Transfer Credit Analyzer as a resource when reviewing your prerequisites. If your course/institution is not on the analyzer, email sdsadmissions@virginia.edu for assistance.
MSDS - Single Variable Calculus (Calculus II)
Single Variable Calculus must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a two-course sequence (Calculus I and Calculus II). This may be satisfied with our Calculus for Data Scientists Boot Camp. Other certificates, open courses, and boot camps will not satisfy the requirement.
Note to enrolling students: If it has been several years since you completed this prerequisite, we can provide you with access to the boot camp as a refresher prior to your first term; reach out to sdsadmissions@virginia.edu following matriculation.
UVA equivalents: MATH 1220, MATH 1320, or APMA 1110; AP BC Calculus with a score of 4 or 5; or VCCS: MTH 262 or MTH 264 . Note that AP AB Calculus, IB Mathematics, and CLEP Calculus will not meet the prerequisite.
Must contain the following key topics:
Functions (precalculus)
- Basic properties of functions: domain and range, compositions
- Function families: Polynomial, exponential, logarithmic, trigonometric, etc.
- Continuity and discontinuity
- Limits and infinitesimals
- Horizontal and vertical asymptotes
Derivatives
- Definition of the derivative
- Differentiability
- Techniques of differentiation: the power rule, the quotient rule, product rules, etc.
- Taylor series
- Applications of derivatives (e.g., in physics)
- Significance of derivatives: maxima, minima, points of inflections, mean value theorem
Integrals
- Definition of the integral
- Indefinite and definite integrals
- Integration by substitution, integration by parts, improper integrals
- Fundamental Theorem of Calculus
Ph.D. only - Multivariable Calculus (Calculus III)
Multivariable Calculus must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a three-course sequence (Calculus I, Calculus II, Calculus III). Certificates, open courses, and boot camps will not satisfy the requirement.
UVA equivalent: MATH 2310 or MATH 2315; or APMA 2120.
Linear or Matrix Algebra
Linear Algebra must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) linear or matrix algebra. This may also be met by certificate through the UVA School of Data Science's Linear Algebra for Data Scientists Bootcamp. Other certificates, open courses, and boot camps will not satisfy the requirement.
UVA equivalents: DS 3005, STAT 3110, MATH 3350, MATH 3351, MATH 4210, PHYS 3340, or APMA 3080; or successful completion of our Linear Algebra for Data Scientists Bootcamp.
To satisfy the Linear or Matrix Algebra prerequisite, your course must contain the following key topics:
Vectors
- Definition of a vector
- Vector spaces (e.g., Euclidian space, Rn)
- Vector bases
- Vector addition and scalar multiplication
- Dot or inner product
- Orthogonality
- Norm of a vector
- Vector geometry (distance, angles, and projections)
- Cross product
Matrices
- Definition of a matrix
- Dimension and rank
- Determinants
- Elementary row operations (e.g., inverses)
- Matrix addition and scalar multiplication
- Matrix multiplication
- Transpose of a matrix
- Square matrices and properties
- Symmetric matrices
- Orthogonal matrices
- Block matrices
- Systems of linear equations and linear transformations
- Triangular and echelon forms
- Factorization and decomposition
- Eigenvalues and eigenvectors
Statistics
Statistics must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts in probability and statistical inference. AP Statistics with a score of 4 or 5 is also acceptable. Certificates, open courses, and boot camps will not satisfy the requirement.
UVA equivalents: STAT 2020, STAT 2120, STAT 3120; APMA 3110 or APMA 3120; or AP Statistics with a score of 4 or 5; or VCCS: MTH 155 or MTH 245. Note that a probability-only course, such as APMA 3100, will not meet the prerequisite on its own, however, you may have other coursework that will satisfy the remaining content area; please email sdsadmissions@virginia.edu for more information.
To satisfy the Statistics prerequisite, your course must contain the following key topics:
Probability
- Basic set theory
- Basic combinatorics: combinations and permutations
- Kolmogorov’s three axioms of probability
- Concept of an event
- Mutually exclusive events
- Concept of sample space
- Concept of a trial or observation
- Independence and dependence
- Conditional probability
- Bayes Theorem
- Concept of a probability distribution
- The normal distribution
- Random variable
Statistics
- Difference between population and sample
- Difference between statistic and parameter
- Relative Frequency
- Measures of centrality: mean, mode, and median
- Measures of dispersion: variance and standard deviation
- Types of data: categorical vs. numeric data; continuous vs discrete numeric data; varieties of categorical data
- Elementary distributions: Gaussian (Normal), Binomial (Bernoulli), and Poisson
- Sampling Distributions and the Central Limit Theorem
- Confidence and confidence intervals
- Statistical significance
- Bias
- Correlation
- Student’s T-Test
- Z-scores
- Sample size determination
- Significance testing
- Hypothesis tests
- Effect size
- P-values
- Chi-square tests
- Model
- Error
- Regression
- Least squares error
Programming
Programming experience can be demonstrated by completion of a course in computer science from an accredited college or university or substantial experience working with a programming language (such as Python, R, Matlab, C++, or Java). We will ask you to detail this experience in your application.
UVA equivalents: DS 1002; CS 1110, 1111, or 1112; CS 1113 or PHYS 1655; or AP Computer Science A with a score of 4 or 5; or IB HL Computer Science with a score of 5, 6,or 7; or VCCS: CS 221; or ITP 120, 132, or 150.
Note that all incoming MSDS students will be required to take a test to demonstrate their proficiency in Python and R prior to the start of classes; refer to our FAQs for more information.
Prerequisite FAQs
Do all prerequisites need to be completed before I apply?
No, though be sure to detail how and when you plan to complete any remaining prerequisites on your application. All prerequisites must be completed by the deadline in your offer letter, which is typically a few weeks prior to the start of your first term. Prerequisites may NOT be completed concurrently with the program.
The most competitive applicants have all their prerequisites completed or nearly complete at the time of application; however, those planning to do the boot camps may choose to wait until they matriculate, which is fine. After paying their tuition deposit, enrolling students missing the calculus or linear algebra prerequisites will receive free access to our Calculus for Data Scientists Boot Camp and/or UVA School of Data Science's Linear Algebra for Data Scientists Bootcamp.
Is there a time limit on prerequisites?
No. However, if it has been several years since you completed the course, you are strongly encouraged to refresh yourself on the key topics prior to the start of classes. Enrolling students in need of refreshing on calculus or linear algebra may request access to our Calculus for Data Scientists Boot Camp and/or UVA School of Data Science's Linear Algebra for Data Scientists Bootcamp.
Programming knowledge/experience (Python, Java, or C++) should be recent and up to date. Note that all incoming MSDS students will be required to take a test to demonstrate their proficiency in Python and R prior to the start of classes; refer to our FAQs for more information.
How can I complete the prerequisites?
Single Variable Calculus must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a two-course sequence (Calculus I and Calculus II). This may be satisfied with our Calculus for Data Scientists Boot Camp. Other certificates, open courses, and boot camps will not satisfy the requirement.
Multivariable Calculus (for PhD only) must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts through multivariable calculus and functions in more than one dimension. In the U.S., this is typically a three-course sequence (Calculus I, Calculus II, Calculus III). Certificates, open courses, and boot camps will not satisfy the requirement. Multivariable calculus is NOT required for the MSDS program.
Linear Algebra must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) linear or matrix algebra. This may also be met by certificate through the UVA School of Data Science's Linear Algebra for Data Scientists Bootcamp. Other certificates, open courses, and boot camps will not satisfy the requirement.
Statistics (for MSDS and PhD) must be completed at an accredited institution, for a grade, either in-person or online, and at a college level; a course or courses that cover(s) concepts in probability and statistical inference. AP Statistics with a score of 4 or 5 is also acceptable. Certificates, open courses, and boot camps will not satisfy the requirement.
Programming experience can be demonstrated by completion of a course in computer science from an accredited college or university or substantial experience working with a programming language (such as Python, R, Matlab, C++, or Java). We will ask you to detail this experience in your application.
Note that all incoming MSDS students will be required to take a test to demonstrate their proficiency in Python and R prior to the start of classes; refer to our FAQs for more information.
My school called the class something else (e.g., "Mathematics III" instead of "Calculus II"). What do I need for my application to prove I've met the prerequisite?
- Enter the course and institution information in the application.
- Upload a course description or syllabus from your institution.
We will evaluate your materials after you apply to determine whether you've met the prerequisite. If we determine that the course doesn't meet the criteria for the prerequisite, we will include completion of that course as a condition of your admission.