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 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 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.

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.

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.

This course provides an introduction to probability and
statistics theory. Assignments and projects help develop studentsâ€™
analytic and critical thinking skills and challenge them to apply
statistical analysis with real world data. The course contains
three parts: methods of data collection, techniques for data
organization and analysis, and techniques for interpreting data
using statistical methodologies. Students will learn not only how
to appropriately collect and analyze data, but how to draw
conclusions from their data for use in decision-making. The course
emphasizes use of Microsoft Excel for graphing and data analysis in
homework assignments. Students will also collect and analyze a data
set of personal interest for the final project. A final paper will
also be prepared. Microsoft Excel and PowerPoint techniques
relevant to the final project will be taught in class, however, a
basic understanding of these applications is expected.

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