Math for Data Science
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.
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*Course availability varies year to year. See Math for Data Science schedule for details.
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.
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.
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.
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.
| 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.
View MATH 202-CN Sections |
| 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. View MATH 220-A Sections |
| 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. View MATH 220-A-DL Sections |
| Single-Variable Integral Calculus <> MATH 220-B | 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. View MATH 220-B Sections |
| 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. View MATH 220-B-DL Sections |
| Linear Algebra <> MATH 240-CN | 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. 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 conceptual—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. View STAT 202-DL Sections |
