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 is a hybrid program, with courses available online and on campus. MATH 220-A, MATH 220-B, and STAT 202-DL are offered online in certain academic terms. See Math for Data Science schedule for details.

Students who do not have a recent academic math background are encouraged to start with MATH 202, and/or complete MATH 101 College Algebra prior to enrolling in MATH 220-A. See the full academic year course schedule for details.

Math for Data Science Tuition

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 provides students with some of the standard
mathematical models and techniques needed to make quantitative
decisions about "real-life" problems that arise in business,
economics, and the social sciences. It will cover a variety of
mathematical topics such as Matrix Theory, Linear Programming,
Counting Principles and Probability, and other topics as time
permits. At the completion of the course, students should be able
to: perform operations with matrices and apply matrix methods to
solve systems of linear equations; solve a linear programming
problem by graphical methods as well as by the simplex method; know
and use various counting principles such as the Fundamental
Principle of Counting, permutation, combinations, and basic
probability. 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. Prerequisite: a solid foundation in algebra,
trigonometry, and geometry.

Single-Variable Differential Calculus <> MATH 220-A-DL

This course covers the following: limits; differentiation;
linear approximation and related rates; extreme value theorem, mean
value theorem, and curve-sketching; optimization. Through this
course students will explore, tangle with, and ultimately master
the fundamental techniques of differential calculus, all of which
stem from the limit and all of which revolve around wielding the
derivative as a powerful tool for understanding the mathematical
and physical world. The course is conducted completely online. A
technology fee will be added to tuition. Credit not allowed for
both MATH 220-A-DL and MATH 220-A. Students who have previously
completed MATH 220-A or MATH 220-CN should not register for this
course.

Prerequisite: a solid foundation in algebra, trigonometry, and
geometry.

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 was formerly MATH 224-CN.

Single-Variable Integral Calculus <> MATH 220-B-DL

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.
This course is conducted completely online. A technology fee will
be added to tuition. Credit not allowed for both MATH 220-B-DL and
MATH 220-B. Students who have previously completed MATH 220-B or
MATH 224-CN should not register for this course.

This course covers basic concepts of linear algebra: solutions
of systems of linear equations; vectors and matrices; subspaces,
linear independence, and bases; determinants; eigenvalues and
eigenvectors; other topics and applications as time permits.
Prerequisite: MATH 230-A or MATH 230 (former), or equivalent.

As of 3/24/22, this course has been cancelled.

There is no available section.

Introduction to Statistics <> STAT 202-CN

This course is intended to familiarize students with the basics
of statistics as a baseline for academic and/or professional
application. Topics include (but are not limited to) basic
descriptive statistics, data testing, correlations, analyses of
variance, and regression analysis. The course will include
instruction on how to use Excel to help students perform
statistical analyses for future problem-solving and
decision-making. Basic knowledge of algebra is
recommended.

There is no available section.

Introduction to Statistics STAT 202-DL

This course provides an introduction to probability and
statistics theory and foundational data science applications. The
focus will be on the analysis of data using computer software, and
the approach is is conceptualâ€”the goal is for students to
understand, not to memorize. Important concepts include samples
versus populations, normal curves and the central limit theorem,
sampling distributions, standard errors, statistical inference,
correlation and regression, t-tests, analysis of variance (ANOVA),
and the chi-squared test. The replication crisis in science and how
bad statistics helped cause it will also be discussed. There are no
formal prerequisites for this course. Recommended skills include
comfort with basic algebra and some experience with spreadsheet
software, such as Microsoft Excel or Google Sheets.

This course is conducted completely online. A technology fee
will be added to tuition.