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.
For courses offered online at Stanford, whether individually or as part of a certificate program, please visit the Stanford Online 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 during the year are available online (distance learning) administered 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.
Must be taken for a letter grade.
|Course Name & Title||Curriculum component||
Theory of Probability
|Core: Probability||Summer||Online||Autumn, Spring: On-campus||Calculus and familiarity with infinite series, or equivalent||
STATS 217 & 218 (On-campus)
(when replacing STATS 116 - a second course in probability or stochastics is needed.)
Introduction to Regression Models and Analysis of Variance
|Core: Applied Statistics||Winter||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||Winter, Summer||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)|
|Core: Statistical Theory||Winter (subject to change)||
|Autumn, Winter: On-campus||Prerequisite: STATS 116||
Statistics Depth courses
The following is a list of Statistics courses which are periodically available online via SCPD. All other courses offered by the Statistics Department are taught on-campus only.
Must be taken for a letter grade.
|Course Name & Title||Curriculum component||
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|
|Statistics Depth||TBD||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||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 220/320 (CS 339N, NBIO 220)||
Machine Learning Methods for Neural Data Analysis
|Winter||Online||N/A||Students should be comfortable with basic probability (STATS 116) and statistics (at the level of STATS 200). This course will place a heavy emphasis on implementing models and algorithms, so coding proficiency is required.||N/A|
|STATS 229 Machine Learning (same as CS 229)||Statistics Depth||Autumn, Spring||Online||N/A||Linear algebra, and basic probability and statistics.||N/A|
Statistical Methods in Finance
|Statistics Depth||last offered 2021||Online||N/A||STATS 200 or equivalent.||N/A|
Risk Analytics and Management in Finance and Insurance
|Statistics Depth||last offered 2022||Online||N/A||STATS 240 or equivalent.||N/A|
Causal Inference in Clinical Trials and Observational Study (II)
|Statistics Depth||last offered 2022||Online||Spring: on-campus||Working knowledge of statistics and R.||N/A|
|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 STATS 216||N/A|
Linear Algebra requirement
Students who have had linear algebra in their prior education may take a more advanced mathematics course (e.g. CME 364A, CME 302), or other mathematics 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.
The following courses in Mathematics are not available online.
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.
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.
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 EE 263, basic probability.
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.
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
Artificial 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, 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.
Deep Learning (CS 230)
Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.
Information Theory (EE 276)
Project-based course about how to measure, represent, and communicate information effectively. Why bits have become the universal currency for information exchange. How information theory bears on the design and operation of modern-day systems such as smartphones and the Internet. The role of entropy and mutual information in data compression, communication, and inference. Practical compressors and error correcting codes. The information theoretic way of thinking. Relations and applications to probability, statistics, machine learning, biological and artificial neural networks, genomics, quantum information, and blockchains.
Prerequisite: a first undergraduate course in probability.