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Program Overview

Math for Data Science

Math for Data Science Certificate Program

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.



About the Math for Data Science Program

Math for Data Science Goals and Courses

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 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.

View MATH 202-CN Sections
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.

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Integral Calculus <> MATH 224-CN

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.

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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.

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Linear Algebra <> MATH 240-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.

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Introduction to Statistics STAT 202-CN

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.

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