Advice by Goals

For this goal, we recommend that students take at least one introduction to statistics or data science course and either a course in data analysis or a Data Science Connector course that applies introductory-level statistics and data science skills to a substantive area (or better both). 

Data Science Connector courses can be from any department or program but they are taught using either Python or R (as in S&DS 123–uses Python–and S&DS 100–uses R) and connect the statistics and data science skills learned in those classes to substantive questions. If the Data Science Connector course you are interested in uses the programming language not emphasized in your introductory course, an adaptor module “Python for R Users” or “R for Python Users” will be offered 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.

Some recommended pathways with no calculus needed:

• S&DS 100 -> S&DS 230

• S&DS 100 -> Data Science Connector 

• S&DS 100 -> S&DS 230 -> Data Science Connector

• S&DS 123 -> S&DS 230

• S&DS 123 -> Data Science Connector 

• S&DS 123 -> S&DS 230 -> Data Science Connector

• GLBL 121 -> GLBL 122

Some recommended pathways with calculus needed:

• S&DS 220 -> S&DS 230

• S&DS 220 -> Data Science Connector 

• S&DS 220 -> S&DS 230 -> Data Science Connector

• S&DS 238 -> S&DS 230

• S&DS 238 -> Data Science Connector

• S&DS 238 -> S&DS 230 -> Data Science Connector

• S&DS 238 -> S&DS 361

• S&DS 238 -> S&DS 361 -> Data Science Connector

• ECON 117 -> ECON 123 (ECON 117 has ECON 108, 110, or 115 as a prerequisite)

• ECON 135 -> ECON 136 (ECON 135 has ECON 108, 110, or 115 and MATH 118 or MATH 120 and MATH 222 or MATH 120 and MATH 225 as prerequisites) 

Which introductory course is for me? 

We think all of these introductory courses are great ways to acquire skills needed to use quantitative evidence in making decisions. Just take one! In deciding which one might be best for you, consider: whether you want the pathway to include calculus in the presentation; what programming or statistical software you want to learn about (S&DS 100, 220, and 238 use R, S&DS  123 uses Python, GLBL 121 and Econ 117 use Stata); whether you want relatively more material on statistics or on programming and computation (S&DS 123 spends more time on programming and computation and less on statistics); how much time you have for the course (S&DS 100 and S&DS 220 are both introductory statistics courses taught with R but 220 is a bit more intensive with more data analysis and R work); and whether you want the course to have a disciplinary perspective (e.g. GLBL 121 for Global Affairs and the various ECON courses for Economics).

Yale College requires that all students take at least two courses in quantitative reasoning. This is Yale College’s “QR” requirement. You can satisfy this requirement partially with one course in statistics and data science or fully with two courses in statistics and data science. For our recommendations for one course or two-course sequences, see Advice By the Number of Data Science and Statistics Courses You Will Likely Take .

For this goal, we recommend that students take at least one introduction to statistics or data science course and at least one course in data analysis.

Some recommended pathways with no calculus needed:

• S&DS 100 -> S&DS 230

• S&DS 123 -> S&DS 230

• GLBL 121 -> GLBL 122

Some recommended pathways with calculus needed:

• S&DS 220 -> S&DS 230

• S&DS 238 -> S&DS 230

• S&DS 238 -> S&DS 361

• ECON 117 -> ECON 123 (ECON 117 has ECON 108, 110, or 115 as a prerequisite)

• ECON 135 -> ECON 136 (ECON 135 has ECON 108, 110, or 115 and MATH 118 or MATH 120 and MATH 222 or MATH 120 and MATH 225 as prerequisites) 

We recommend S&DS 265 Introductory Machine Learning for this goal. To take this course, you need to first take two of the following courses: S&DS 230, S&DS 238, S&DS 240, S&DS 241, and S&DS 242. In addition, you need previous programming experience (e.g. R, Matlab, Python, C++). Python experience is preferred (see CPSC 100 or CPSC 110 for a Yale course).

There are a number of ways to get started toward Yale’s Certificate in Data Science. For students with limited or no background, we recommend taking S&DS 123 YData: An Introduction to Data Science as a prerequisite. After this course or an equivalent introductory course, students can start taking courses toward fulfilling the Certificate requirements.  

For students with more limited programming and mathematical backgrounds, recommended sequences after S&DS 123 include one course in Probability and Statistical Theory and one course in Statistical Methodology and Data Analysis such as:

S&DS 230 or S&DS 240->S&DS 230 or S&DS 240 (in either order)

For students with some background in statistics and programming and seeking a treatment of this material that uses calculus, recommended sequences vary depending on interests:

S&DS 230 -> S&DS 238 A more data analysis emphasis in starting the Certificate.

S&DS 241 -> S&DS 242 A more theoretical start to the Certificate.

The Certificate in Programming prepares current Yale undergraduates to program computers in support of work in any area of study. While the Certificate does not provide the same grounding in theory and systems that the computer science majors do, it does provide a short path to programming literacy that can be completed in a span of four terms. The prerequisite for the Certificate is an introductory programming course (CPSC 100, CPSC 110, CPSC 112 or CPSC S115), successful completion of the AP Computer Science course, or equivalent programming experience. You can find more information on the DUS of CS Website.

Students interested in computer science and engineering fields should develop a strong quantitative background in the following areas:  

• multivariate calculus (e.g. MATH 120) 

• linear algebra (e.g. MATH 222, 225) 

• programming (e.g. CPSC 100, 110, 112) 

• probability (e.g. S&DS 238, 241)

• discrete math (e.g. CPSC 202, MATH 244) 

• data analysis (e.g. S&DS 230) 

• algorithms (e.g. CPSC 365, 366)

• and machine learning/AI (e.g. S&DS 365, CPSC 370)

For a list of all majors with statistics and data science course requirements and a brief description of the requirements and other course recommendations for the major, see Advice by Intended Major.