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
Enroll in online courses 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.
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) 
STATS 203(V) Introduction to Regression Models and Analysis of Variance 
Core: Applied Statistics  Summer  Online 
Winter: Oncampus 
postcalculus mathematical statistics course, e.g. STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra. 

STATS 217 Introduction to Stochastic Processes I 
Core: Stochastic Processes  not offered Summer 2020  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) 
 Students who have not taken a course in probability may consider taking the prerequisite course (STATS 116) prior to starting the program. Otherwise, students should plan to take STATS 116 autumn quarter followed by STATS 200 in winter quarter.
 *Students who replace STATS 116 with STATS 217 must take a second course in stochastic processes or probability.
 Enrollment in STATS 116 after successful completion of STATS 217, 218 and/or 219 does not count towards degree requirements, including as an elective.
Additional Statistics courses
The following is a list of Statistics courses available online (SCPD). All other courses taught by the Statistics department are 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 
Additional Stats  Autumn, Summer  Online  N/A  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 216V Introduction to Statistical Learning 
Additional Stats  Winter, Summer  Online  N/A  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 202 
STATS 229 Machine Learning (same as CS229)  Additional Stats  Autumn, Spring  Online  N/A  linear algebra, and basic probability and statistics.  
STATS 237P Investment Portfolios, Derivative Securities, and Risk Measures 
Additional Stats  Summer  Online  N/A  STATS 116 or equivalent.  
STATS 240P Statistical Methods in Finance 
Additional Stats  Autumn  Online  N/A  STATS 200 or equivalent.  
STATS 241P Datadriven Financial Econometrics 
Additional Stats  Summer  Online  N/A  STATS 240 or equivalent.  
STATS 245P Data, Models and Applications to Healthcare Analytics 
Additional Stats  Summer  Online  N/A  STATS 202 or 216, or CS 229  
STATS 290 Computing for Data Science 
Additional Stats  Winter  Online  N/A  Programming experience including familiarity with R; computing at least at the level of CS 106; statistics at the level of STATS 110 or 141.  
STATS 315B Modern Applied Statistics: Data Mining 
Additional Stats  Spring  Online  N/A  STATS 202 or 216  
STATS 376A Information Theory  Additional Stats  Winter  Online  N/A  STATS 116 
 The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment.
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 adviser's approval. Must be taken for a letter grade.
Course Name & Title  Quarter Offered 
format  ALT.  Prerequisites 

Select one from the following. Substitution of more advanced courses in Mathematics, that provide similar skills, may be made with consent of the adviser. All must be taken for a letter grade, with the exception of courses offered satisfactory/no credit only.  
MATH 104 Applied Matrix Theory  Autumn, Winter, Spring, Summer  Oncampus  
MATH 113 Linear Algebra and Matrix Theory  Autumn, Winter, Spring, Summer  Oncampus  
MATH 115 Functions of a Real Variable  Autumn, Spring  Oncampus  
MATH 171 Fundamental Concepts of Analysis  Autumn, Spring, Summer  Oncampus  
CME 302 Numerical Linear Algebra  Autumn  Oncampus  
EE 364A Convex Optimization  Winter  Online 
Programming
One course in programming:
CS 106A/B/X, CS 107, CS 140  181, or other course with the faculty adviser's approval.
Students who have these skills may elect a more advanced CS course.
Must be taken for a letter grade.
Electives
Course Name & Title  Quarter Offered 
format  Prerequisites 

Suggested electives offered via SCPD. May be taken satisfactory/no credit only.  
CME 364A Convex Optimization I (EE 364A)  Winter  Online  linear algebra such as EE263, basic probability 
CS 161 Design and Analysis of Algorithms  Winter  Online  CS103; CS109 or STATS116 
CS 221 Artifical Intelligence: Principles and Techniques  Autumn  Online  CS103, CS106B or CS106X, CS107 and CS109 (algorithms, probability and programming experience) 
CS 229 Machine Learning  Autumn, Spring  Online  Linear algebra, basic probability and statistics. 
CS 230 Deep Learning  Winter  Online  Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications); CS 229 may be taken concurrently 
CS 236 Deep Generative Models  Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.  
STATS 299 Independent Study  Autumn, Winter, Spring, Summer  via email or by appointment 
Study or research program under the supervision of a Statistics faculty member. Proposals must include the topic of study as well as the deliverables for the course, i.e, what one will produce and submit for a grade. Research is an original analysis or inquiry situated within a scholarly discipline. May be repeated for credit. 