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Statistics HCP: online course offerings

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 on-campus only in the Autumn and Spring.

Core courses

Must be taken for a letter grade.

Course Name & Title Curriculum component

Quarter
Offered

Format

Alt. Prerequisites Replace with
STATS 116
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

Winter: On-campus

post-calculus 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 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)
  • Students who have not taken a course in probability may consider taking the pre-requisite 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 on-campus only.

Must be taken for a letter grade.

 

Course Name & Title Curriculum component

Quarter
offered

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
Data-driven 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   On-campus    
MATH 113 Linear Algebra and Matrix Theory   On-campus    
MATH 115 Functions of a Real Variable   On-campus    
MATH 171 Fundamental Concepts of Analysis   On-campus    
CME 302 Numerical Linear Algebra Autumn On-campus    
EE 364A Convex Optimization   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.

SCPD course offerings in CS

 

 

 

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.
Weekly progress reports; 1-3 units [determined by the number of readings and amount of work assigned]

May be repeated for credit.