Description
Theoretical foundations to data science using open source software. Empirical distribution functions, point estimation, interval estimation, tests of hypotheses, maximum likelihood and method of moments. Formal tools are developed, and concepts are demonstrated using simulation. Abstract concepts are made concrete through visualization and numerical computation.
Prerequisites
- Prerequisite(s): MATH 2007 (or MATH 1005 or MATH 2052 ); and DATA 2500 ; and DATA 1519 (or STAT 2509 or STAT 2559 ).
Conditions and arrangements
- Precludes additional credit for Precludes additional credit for STAT 3508 , STAT 3558 .
- Prerequisite(s): MATH 2007 (or MATH 1005 or MATH 2052 ); and DATA 2500 ; and DATA 1519 (or STAT 2509 or STAT 2559 ).
- Lectures three hours a week, laboratory one hour a week.
Reference text in its original language
Theoretical foundations to data science using open source software. Empirical distribution functions, point estimation, interval estimation, tests of hypotheses, maximum likelihood and method of moments. Formal tools are developed, and concepts are demonstrated using simulation. Abstract concepts are made concrete through visualization and numerical computation.
- Prerequisite(s): MATH 2007 (or MATH 1005 or MATH 2052 ); and DATA 2500 ; and DATA 1519 (or STAT 2509 or STAT 2559 ).
- Precludes additional credit for Precludes additional credit for STAT 3508 , STAT 3558 .
- Lectures three hours a week, laboratory one hour a week.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/undergrad/courses/STAT/