For students and professionals seeking to build math and analysis proficiency, the Math for Data Science post-baccalaureate certificate program is designed to strengthen their quantitative background for graduate school or to enhance their data analysis skills for their careers. Consisting of courses in applied mathematics, statistics, and calculus, the program provides students with a quantitative foundation for data analysis—a critical skillset that is applicable to a wide range of industries.

Math for Data Science post-baccalaureate certificate students will:

Apply mathematical concepts to real-world research and business situations

Demonstrate quantitative reasoning skills to solve complex problems

Analyze data using a variety of mathematical and statistical methods

Required Courses

Four from the following:

MATH 202 Finite Mathematics

MATH 220 Differential Calculus

MATH 224 Integral Calculus

MATH 230 Differential Calculus of Multivariable

MATH 240 Linear Algebra

STAT 202 Introduction to Statistics

Note: It is recommended that students who do not have a recent academic math background begin with MATH 202, and/or complete MATH 101 College Algebra or MATH 113 Precalculus prior to enrolling in MATH 220.

Post-baccalaureate students at Northwestern's School of Professional Studies pay per course. For more information about financial obligations and tuition, please visit the Tuition page.

Admission for Math for Data Science

In addition to completing an online application, you'll also need to submit a few supplemental materials. A list of requirements for admission including application deadlines and tips on how to apply can be found at the Admission page.

Math for Data Science Registration Information

Whether you're a first-time registrant or current and returning student, all students register using our online student registration and records systems. Important information about registering for courses at SPS, including registration timelines and adding or dropping courses in which you are already enrolled, can be found on the Registration Information page.

Find out more about the Math for Data Science Program

Program Courses:

Course Detail

Finite Mathematics <> MATH 202-CN

This course serves as a foundation of mathematical knowledge
targeting data analysis. Topics will be chosen from set theory,
combinatorics (the art of counting), finite probability, elementary
linear algebra and its applications to linear optimization
problems. Among other things, the course will focus on practical
applications of these mathematical tools to real-life situations,
such as analyzing survey data, probability tests, supply and demand
linear functions and equilibrium prices in economy, minimizing
linear cost functions and maximizing linear profit functions in
business. Upon completing the course, students will be able to
transform real-world tasks into mathematical problems, manipulate
(systems of) linear equations and optimizations, and solve counting
problems in a systematic way. Prerequisite: none.

Differential Calculus of One Variable Functions <> MATH 220-CN

This course covers definition of a function; trigonometric,
exponential, logarithmic and inverse functions; graphs, limits,
continuity; derivative of a function; product, quotient and chain
rule; implicit differentiation; linear approximation and
differentials; related rates; mean value theorems; curve plotting;
optimization problems; Newton's method; and antiderivatives.
Prerequisite: three years of high school math, including
trigonometry, or MATH 113.

This course is focused on definite integrals and the fundamental
theorem of calculus. Techniques of integration including
integration by parts, trigonometric integrals, trigonometric
substitutions, partial fractions, numerical integration, and
improper integrals are covered. Topics also include: applications
of integration (computation of volumes, arc length, average value
of functions, the mean value theorem for integration, work and
probability), sequences and series (the integral and comparison
tests, power series, ratio test, introduction to Taylor's formula
and Taylor series, using series to solve differential equations).
Prerequisite: MATH 220 or equivalent.

Differential Calculus of Multivariable Functions <> MATH 230-CN

This course will extend the methods of single-variable calculus
to functions of many variables, i.e. it will develop techniques to
obtain local linear approximations of functions (of multiple
variables) in order to analyze and optimize quantities. Specific
topics include: vectors, dot and cross products, equations of lines
and planes, polar, cylindrical and spherical coordinates,
differentiation of vector functions, velocity and acceleration, arc
length, parametric surfaces, functions of several variables,
partial derivatives, tangent plane and linear approximations, chain
rule for partial derivatives, directional derivative and gradient,
max-min problems for functions of several variables, Lagrange
multipliers. Prerequisite: MATH 224 or equivalent.

This course is an introduction to the field of linear algebra,
which is of fundamental importance throughout mathematics and its
applications. We start with Gaussian elimination, a systematic way
to solve linear systems of equations. This leads to a natural
introduction of vectors and matrices. From here, we dive into the
abstract concepts of vector spaces, subspaces, linear independence,
bases, and linear transformations. Geometric interpretations of
linear transformations in Euclidean n-spaces will be discussed
through the introduction of determinants, eigenvalues, and
eigenvectors. This course focuses on problem-solving techniques in
mathematics. A significant part of the lecture time will be devoted
to small group discussions for problem solving. Prerequisite:
MATH 230 or equivalent.

This course provides an introduction to the basic concepts of
statistics. Throughout the course, students will learn to:
summarize data using graphs and tables; explain/calculate
descriptive statistics, confidence intervals, correlation,
regression, and probability; and explain tests of significance and
data-production including sampling and experiments. Basic
knowledge of algebra is recommended.