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 parttime 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 oncampus only in the Autumn and Spring.
The example program is intended to provide a progression of coursework with courses offered online and oncampus.
Core courses
Must be taken for a letter grade.
Course Name & Title  Curriculum component 
Quarter 
Format 
Alt.  Prerequisites  Replace with 

STATS 116 Theory of Probability 
Core: Probability  Summer  Online  Autumn, Spring: Oncampus  Calculus and familiarity with infinite series, or equivalent 
STATS 217 & 218 (Oncampus) (when replacing STATS 116  a second course in probability or stochastics is needed.) 
STATS 203(V) Introduction to Regression Models and Analysis of Variance 
Core: Applied Statistics  Summer  Online 
Spring 2022: Oncampus 
postcalculus mathematical statistics course, e.g., STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra. 
STATS 305A (Oncampus) or STATS 191 (Online) 
STATS 217 Introduction to Stochastic Processes I 
Core: Stochastic Processes  Winter, Summer  Online  Winter: Oncampus  a postcalculus 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: Oncampus) 
STATS 200 
Core: Statistical Theory  Autumn (subject to change) 
Online (last offered online: Aut 2021) 
Autumn, Winter: Oncampus  Prerequisite: STATS 116 
Choose one:

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 oncampus only.
Must be taken for a letter grade.
Course Name & Title  Curriculum component 
Quarter 
Format 
Alt.  Prerequisites  Replace with 

STATS 202 Data Mining and Analysis 
Statistics Depth  Autumn, Summer  Online  Autumn: oncampus  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 
STATS 214 
Statistics Depth  Autumn  Online  Autumn: oncampus  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).  
STATS 216V Introduction to Statistical Learning 
Statistics Depth  Winter, Summer  Online  Winter: oncampus  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 (oncampus) 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 
STATS 240P Statistical Methods in Finance 
Statistics Depth  Autumn  Online  N/A  STATS 200 or equivalent.  N/A 
STATS 243P Risk Analytics and Management in Finance and Insurance 
Statistics Depth  Winter  Online  N/A  STATS 240 or equivalent.  N/A 
STATS 248 Causal Inference in Clinical Trials and Observational Study (II) 
Statistics Depth  Spring  Online  Spring: oncampus  Working knowledge of statistics and R.  N/A 
STATS 270 
Statistics Core or Statistics Depth  Spring  Online  Spring: oncampus  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 
STATS 315B Modern Applied Statistics: Data Mining 
Statistics Depth  Spring  Online  N/A  STATS 202 or STATS 216  N/A 
STATS 376A Information Theory  Statistics Depth  Online  N/A  STATS 116  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, leastsquares, 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
Oncampus
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 proofwriting. (MATH 104 offers a more applicationoriented treatment.) Prerequisites: MATH 51
Autumn, Winter, Spring
Oncampus
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 proofwriting. Prerequisite: 21.
Autumn, Spring
Oncampus
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
Oncampus
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.
Autumn
Oncampus
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. Leastsquares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interiorpoint 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.
Winter, Summer
Programming
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.
Electives
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: divideandconquer, dynamic programming, greedy algorithms, randomization. Algorithms for fundamental graph problems: minimumcost 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 objectoriented 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
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.
Autumn