Statistics HCP: online course offerings

The Honors Cooperative Program (HCP), through the Stanford Center for Professional Development (SCPD), allows professionals to pursue graduate study on a part-time basis. HCP students are fully matriculated graduate students of Stanford University with all privileges, rights and responsibilities.

HCP applicants are subject to the same admission requirements as other applicants, although application deadlines may differ.

Read more on the Stanford Center for Professional Development website.

Note that while the majority of this degree can be completed online, this depends heavily on your program plan, area of focus, and the course offerings for any given academic quarter. You may find it helpful to compare the degree requirements with SCPD’s typical course offerings to see how much of this degree can be completed online.

Online course offerings in the Department of Statistics

A portion of statistics courses are offered online through Stanford Center for Professional Development (SCPD).

Courses offered multiple quarters are not necessarily offered online in each quarter. For instance, STATS 116 is offered online in the Summer, but on-campus only in the Autumn and Spring.


The example program is intended to provide a progression of coursework with courses offered online and on-campus.



Core courses

Must be taken for a letter grade.


Course Name & Title Curriculum component



Alt. Prerequisites Replace with
Theory of Probability
Core: Probability Summer Online Autumn, Spring: On-campus Calculus and familiarity with infinite series, or equivalent STATS 217 & 218* (On-campus)
STATS 203(V)
Introduction to Regression Models and Analysis of Variance
Core: Applied Statistics Summer Online

Spring 2022: On-campus

post-calculus mathematical statistics course, e.g. STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.

STATS 305A (On-campus)


STATS 191 (Online)

Introduction to Stochastic Processes I
Core: Stochastic Processes not offered Summer 2020 Online Winter: On-campus a post-calculus introductory probability course (e.g. STATS 116) If replacing both STATS 116 & 217 - two courses in stochastic processes or probability are required (e.g.  STATS 218 & 219: On-campus)


Introduction to Statistical Inference

Core: Statistical Theory Autumn Online Autumn, Winter: On-campus Prerequisite: STATS 116 STATS 300A or 300B or 300C; or STATS 270



Statistics Depth courses

The following is a list of Statistics courses available online (SCPD). All other courses taught by the Statistics department are on-campus only.

Must be taken for a letter grade.


Course Name & Title Curriculum component



Alt. Prerequisites Replace with
Data Mining and Analysis
Statistics Depth Autumn, Summer Online Autumn: on-campus Introductory courses in statistics or probability (e.g., Stats 60 or equivalent), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105). STATS 216/V


Machine Learning Theory (same as CS229M)

Statistics Depth Autumn Online Autumn: on-campus Linear algebra ( MATH 51 or CS 205), probability theory ( STATS 116, MATH 151 or CS 109), and machine learning ( CS 229, STATS 229, or STATS 315A).  
Introduction to Statistical Learning
Statistics Depth Winter, Summer Online Winter: on-campus Introductory courses in statistics or probability (e.g., Stats 60 or equivalent), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105). STATS 315A (on-campus) or STATS 202
STATS 229 Machine Learning (same as CS229) Statistics Depth Autumn, Spring Online N/A linear algebra, and basic probability and statistics. N/A
Statistical Methods in Finance
Statistics Depth Autumn Online N/A STATS 200 or equivalent. N/A


Risk Analytics and Management in Finance and Insurance

Statistics Depth Winter Online N/A Prerequisite: STATS 240 or equivalent. N/A


Clinical Trial Design in the Age of Precision Medicine and Health

Statistics Depth Spring Online Spring: on-campus
Working knowledge of statistics and R.


A Course in Bayesian Statistics

Statistics Core or Statistics Depth Spring Online Spring: on-campus Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended; Stats 200 or equivalent statistical theory course desirable. STATS 200
Modern Applied Statistics: Data Mining
Statistics Depth Spring Online N/A STATS 202 or 216 N/A
STATS 376A Information Theory Statistics Depth Winter Online N/A STATS 116 N/A



Linear Algebra requirement

Students who have had linear algebra may take a more advanced mathematics course, such as from the following, or other math course with program advisor's approval. Must be taken for a letter grade.


Select one from the following. Substitution of more advanced courses in Mathematics, that provide similar skills, may be made with consent of the advisor. All must be taken for a letter grade, with the exception of courses offered satisfactory/no credit only.

Applied Matrix Theory (MATH 104)

Linear algebra for applications in science and engineering: orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, least-squares, the condition number of a matrix, algorithms for solving linear systems. MATH 113 offers a more theoretical treatment of linear algebra. MATH 104 and ENGR 108 cover complementary topics in applied linear algebra. The focus of MATH 104 is on algorithms and concepts; the focus of ENGR 108 is on a few linear algebra concepts, and many applications. Prerequisites: MATH 51 and programming experience on par with CS 106.

Autumn, Winter, Spring, Summer  


Linear Algebra and Matrix Theory (MATH 113)

Algebraic properties of matrices and their interpretation in geometric terms. The relationship between the algebraic and geometric points of view and matters fundamental to the study and solution of linear equations. Topics: linear equations, vector spaces, linear dependence, bases and coordinate systems; linear transformations and matrices; similarity; eigenvectors and eigenvalues; diagonalization. Includes an introduction to proof-writing. ( Math 104 offers a more application-oriented treatment.) Prerequisites: Math 51

Autumn, Winter, Spring


Functions of a Real Variable (MATH 115)
The development of real analysis in Euclidean space: sequences and series, limits, continuous functions, derivatives, integrals. Basic point set topology. Includes introduction to proof-writing. Prerequisite: 21.


Autumn, Spring


Fundamental Concepts of Analysis (MATH 171)
Recommended for Mathematics majors and required of honors Mathematics majors. Similar to 115 but altered content and more theoretical orientation. Properties of Riemann integrals, continuous functions and convergence in metric spaces; compact metric spaces, basic point set topology. Prerequisite: 61CM or 61DM or 115 or consent of the instructor.


Autumn, Spring


Numerical Linear Algebra (CME 302)
Solution of linear systems, accuracy, stability, LU, Cholesky, QR, least squares problems, singular value decomposition, eigenvalue computation, iterative methods, Krylov subspace, Lanczos and Arnoldi processes, conjugate gradient, GMRES, direct methods for sparse matrices. Prerequisites: CME 108, MATH 114, MATH 104.




Convex Optimization I (EE 364A/(CME 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.


Winter, Summer


One course in programming:

CS 106A/B/X, CS 107, CS 140 - 181, or other course with the faculty advisor's approval.

Students who have these skills may elect a more advanced CS course.

Must be taken for a letter grade.

SCPD course offerings in CS


Suggested electives offered via SCPD. May be taken satisfactory/no credit only.

Design and Analysis of Algorithms (CS 161)

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching, amortized analysis, stable matchings and approximation algorithms. Prerequisite: 103 or 103B; 109 or STATS 116.


Autumn, Winter, Summer


Artifical Intelligence: Principles and Techniques (CS 221)

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.


Autumn, Summer


For courses offered online at Stanford, whether individually or as part of a certificate program, please visit the Stanford Online.