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Summer Course in Statistics: Data Mining and Analysis

Stanford University Summer Session
8 weeks
Full time
Summer / Short course

Program description

Summer Course in Statistics: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining.

Course Details

  • Intensive Study: Data Science
  • Online Format: Synchronous - This course is taught in real-time, and students are expected to attend virtual sessions at specific times during the week.

Admission requirements

  • Be at least 18 years of age by the start of the study abroad program
  • Have graduated from high school or secondary school or equivalent
  • Be able to verify fluency in the English language, using one of the methods described in the English Language Proficiency below
  • Internet-Based Test of English as a Foreign Language (iBT TOEFL) Minimum score of 95 for the iBT TOEFL, International English Language Testing System: Academic (IELTS: Academic) Minimum overall band score of 7.0, or Cambridge Assessment English Minimum score of 180 on the Cambridge English Advanced, CEFR Level C1.

This program is suitable for undergrad/grad students. 

  • Introductory courses in statistics or probability (e.g., STATS 60), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105). Probability at the level of STATS 116 and familiarity with Linear Algebra

Does this course require proof of English proficiency?

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Program content

Topics: decision trees, association rules, clustering, case based methods, and data visualization.

Tuition fees

The total fees for this program is USD 3,525.


By the end of the quarter, students will:

  • Understand the distinction between superised and unsupervised learning and be able to identify appropriate tool to answe ifferent research questions.
  • Become familiar with basic unsupervised procedures including clustering and pinicipal components analysis.
  • Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminat analysis, K-nearest neighbors, splines, gneralized additive models, tree based methos, and support vector machines.
  • Gain a practical appreciation of the bias-variance traddeoff and apply model selection methods based on cross validation and booststrapping to a prediction challenge.
  • Analze a real dataset of moderate size using R.
  • Develop the computational skills for date wrangling, collaboration, an reproducible research
  • Be exposed to other topics in machine learning, such as missing data, non-linear dimensionality redction techniques, web-based data visualizations, anomaly detection, and representation learning.

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About this institute

Stanford Summer Session

Stanford University Summer Session invites visiting undergraduate, graduate, and high school students to experience Stanford University during its fourth academic quarter. During the eight-week course, Summer Session students are enrolled in the same courses as matriculated Stanford students, and earn...

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Contact info

Stanford University Summer Session

415 Broadway
Academy Hall, Floor 2, 8853
94063 Redwood City California
United States of America

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