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-A Single-Variable Differential Calculus (was MATH 220)

MATH 220-B Single-Variable Integral Calculus (was MATH 224)

MATH 230-A Multivariable Differential Calculus (was MATH 230)

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-A.

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.

Single-Variable Differential Calculus <> MATH 220-A

This course covers the following: limits; differentiation;
linear approximation and related rates; extreme value theorem, mean
value theorem, and curve-sketching; optimization. This course was
formerly MATH 220-CN.

This course covers the following: definite integrals,
antiderivatives, and the fundamental theorem of calculus;
transcendental and inverse functions; areas and volumes; techniques
of integration, numerical integration, and improper integrals;
first-order linear and separable ordinary differential equations.
Prerequisite: MATH 220-A.

This course covers the following: vectors, vector functions,
partial derivatives, and optimization. Prerequisite: MATH 220-B.
This course was formerly MATH 230-CN.

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.