Statistics for Data Science
University of Pennsylvania · PA · ESE5420
Statistics for Data Science is a subject at University of Pennsylvania, PA. It covers topics like data-science, statistical-computing, statistical-learning, statistics. It is part of 22 degrees. The information below is compiled from public course material — approximate, not official.
University of Pennsylvania
ESE5420
3.00
Topics covered
Degrees that include Statistics for Data Science
- Applied Economics and Data Science, MEDS
- Artificial Intelligence, BSE
- Artificial Intelligence, Certificate
- Artificial Intelligence, MSE
- Bioengineering, BSE
- Bioengineering, MSE
- Biomedical Science, BA
- Biomedical Science, BAS
- Biotechnology, MBIOT
- Computer & Information Technology, MCIT
- Computer Science, MASCS
- Data Science and Artificial Intelligence, MSE
- Data Science, Certificate
- Data Science, MSE
- Electrical and Systems Engineering, PhD
- Electrical Engineering, MSE
- Energy and Sustainability Engineering, MSE
- Quantitative Finance, MSQF
- Scientific Computing, MSE
- Statistics and Data Science, MA
- Systems Engineering, MSE
- Technology and Innovation, MSE
Frequently asked questions
What is Statistics for Data Science about?
Statistics for Data Science covers topics like data-science, statistical-computing, statistical-learning, statistics.
Which degrees include Statistics for Data Science?
Statistics for Data Science is part of 22 degrees, including Applied Economics and Data Science, MEDS, Artificial Intelligence, BSE, Artificial Intelligence, Certificate, Artificial Intelligence, MSE, Bioengineering, BSE.
How many credit points is Statistics for Data Science?
Statistics for Data Science at University of Pennsylvania is typically around 3.00 credit points. Approximate — confirm with the university.
Where is Statistics for Data Science taught?
Statistics for Data Science is taught at University of Pennsylvania, PA.
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Sources: University of Pennsylvania public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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