Advice by Number of Data Science and Statistics Courses You Will Likely Take

Yale has lots of great classes and you can only take so many in one area, this page provides recommendations for students who can only take one, two, or three courses in statistics and data science or at least only want to commit to that many courses now.

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One Course

No calculus and no prerequisites:

  • S&DS 100 Introductory Statistics–Learn central concepts of statistics and basics of R programming language to analyze data.

  • S&DS 123 YData: An Introduction to Data Science–Build knowledge and skills in data science including programming, computation, and statistics in Python programming language. Both S&DS 100 and S&DS 123 are highly recommend introductory courses but S&DS 123 might be preferred for those looking for more programming and computation and a bit less statistics. The courses are also different in that S&DS 123 uses Python and S&DS 100 uses R. 

  • S&DS 240 An Introduction to Probability Theory. This is a “pleasures of probability” course which focuses on introducing students to fun applications of probability to thinking about the world rather than satisfying prerequisite requirements for further coursework. 

No calculus and AP Statistics (or other introductory statistics course) prerequisite:

  • S&DS 230 Data Exploration and Analysis–Learn lots of different kinds of data analysis in R programming language.

Calculus needed but no prerequisites:

  • S&DS 220 Introductory Statistics, Intensive–Learn central concepts of statistics and basics of R programming language to analyze data. Includes more data analysis and R learning than S&DS 100 but still introductory. 

  • S&DS 238 Probability and Bayesian Statistics–Intense semester of theory and practice of probability and Bayesian statistical modeling with data in R programming language.

 

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Two Courses 

Our two (and three) course recommended sequences include reference to Data Science Connector courses. Departments across Yale College offer applied courses that are labeled as Data Science Connector courses. They assume knowledge of the material in S&DS 100 or S&DS 123 and their descriptions indicate if they are taught with the R or Python programming languages. If your introductory course was in R (Python) and your Data Science connector course is in Python (R), the course will offer an adaptor module to help you adjust. If you want to take a Data Science Connector course, you can search on “Data Science Connector” on Yale Course Search. Data Science Connector courses are offered by departments in the social sciences, sciences, engineering, and humanities.

No calculus, one course in statistics/data science and one in data analysis

  • S&DS 100 or S&DS 123 and S&DS 230

No calculus, one course in statistics and one applied course

  • S&DS 100 or S&DS 123 and Data Science Connector course. 

Calculus needed, one course in statistics and one in data analysis

  • S&DS 220 or S&DS 238 and either S&DS 230 or S&DS 361 Data Analysis. S&DS 361 also requires some experience with linear algebra (e.g. MATH 222, MATH 225, or MATH 118). These courses all use R programming language.

Calculus needed, one course in statistics and one applied course

  • S&DS 220 or S&DS 238 and Data Science Connector course. Majors across Yale College offer applied courses that are labeled as Data Science Connector courses. They assume knowledge of the material in S&DS 220 or S&DS 238 (or introductory statistics courses) and their descriptions indicate if they are taught with the R or Python programming languages. S&DS 220 and S&DS 238 are taught in R. If your Data Science connector course is in Python, the course will offer an adaptor module to help you adjust. 

Calculus needed, one course in probability and one course in statistics

  • S&DS 241 Probability Theory and S&DS 242 Theory of Statistics. This is a rigorous two-course sequence in probability and statistics but does not expose the student to data analysis. 
  • S&DS 240 An Introduction to Probability Theory and S&DS 242 Theory of Statistics. Two-course sequence in probability and statistics but does not expose the student to data analysis. 
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Three Courses

No calculus, one course in statistics/data science, one in data analysis, and one applied course

  • S&DS 100 or S&DS 123 and S&DS 230 and Data Science Connector course. S&DS 230 is taught with the R programming language as is S&DS 100 but will provide a module for students who took S&DS 123 so that they can translate their Python skills to R. Similarly, some Data Science Connectors are in R and some in Python and all will offer an adaptor module to help you adjust. 

Calculus needed, one course in probability and statistics, one in data analysis, and one applied course

  • S&DS 220 or S&DS 238 and S&DS 230 or S&DS 361 and Data Science Connector course. S&DS 361 also requires some experience with linear algebra (e.g. MATH 222, MATH 225, or MATH 118). First two courses are in R but Data Science Connector can be in R or Python with an adaptor module to help students adjust.

Calculus needed, courses in probability, statistics and data analysis

  • S&DS 241 Probability Theory, S&DS 242 Theory of Statistics, and S&DS 361 Data Analysis. S&DS 361 also requires some experience with linear algebra (e.g. MATH 222, MATH 225, or MATH 118). This sequences uses the R programming language.