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Course Criteria
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1.00 - 9.00 Credits
This course provides graduate students with continued support and advisement. It is usually taken following completion of all coursework required for the degree. Repeatable for credit. Pass/Fail only.
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3.00 Credits
Descriptive and inferential statistical methods. Emphasis on conceptual understanding and statistical thinking. Examples presented from many different areas. Prerequisite/Restriction: One of the following within the last year or three consecutive semesters (including summer); ACT Math score of 19 or higher; SAT Math score of 460 or higher; AP Calculus AB score of at least 3; Grade of C- or better in MATH 0995; Grade of C- or better in MATH 1050 or MATH 1100; or satisfactory score on the Math Placement Exam.
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5.00 Credits
Intro to statistics with an emphasis on conceptual understanding and statistical reasoning. Foundational algebra, types of studies, summarizing data, probability, hypothesis testing. NOTE: This course does NOT meet the prerequisites for MATH 1050. Prerequisite/Restriction: One of the following within the last year or three consecutive semesters (including summer): ACT Math score of 16 or higher; SAT Math score of 400 or higher; or Grade of C- or better in MATH 0950 or satisfactory score on the MATH Placement Exam.
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3.00 Credits
This course introduces the analysis and visualization of tabular data using high-level computer programming. It emphasizes conceptual understanding of the data science lifecycle, which includes data collection, sampling and sample bias, exploratory data analysis and visualization, and hypothesis testing and prediction. Prerequisite(s)/Restriction(s): One of the following within the last year or three consecutive semesters (including summer): ACT Math exam with a score of 19 or higher, or equivalent SAT Math score AP Calculus AB exam with a score of 3 or higher MATH 0995 or ?MATH 1010 with a grade of C- or better in the chosen option (a passing grade of P is acceptable) MATH 1050 or MATH 1100 with a grade of C- or better in the chosen option (a passing grade of P is acceptable) Satisfactory score on the ALEKS Math Placement Exam
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4.00 Credits
Introduction to statistical concepts, graphical techniques, probability, distributions, estimation, one and two sample testing, chi-square tests, and simple linear regression, one-way ANOVA. Prerequisite/Restriction: One of the following within the last year or three consecutive semesters (including summer); ACT Math score of at least 25; SAT Math score of at least 580; Grade of C- or better in MATH 1050 or MATH 1100; or satisfactory score on the Math Placement Exam.
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1.00 - 6.00 Credits
Lower-division internship/cooperative work experience in statistics.
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4.00 Credits
Descriptive and inferential statistics, probability, sampling, estimation, tests of hypotheses, linear regression and correlation, chi-square tests, analysis of variance, and multiple regression. Prerequisite/Restriction: One of the following within the last year or three consecutive semesters (including summer); ACT Math score of at least 25; SAT Math score of at least 580; Grade of C- or better in MATH 1050 or MATH 1100; or satisfactory score on the Math Placement Exam.
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1.00 - 3.00 Credits
Prerequisite/Restriction: Prior arrangement with specific instructor. Repeatable for credit. Semester(s) Traditionally Offered: Fall, Spring, Summer
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3.00 Credits
Introduction to statistical concepts, graphical techniques, discrete and continuous distributions, parameter estimation, hypothesis testing, and chi-square tests. Prerequisite/Restriction: C- or better in MATH 1100 or MATH 1210.
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3.00 Credits
This course introduces data science concepts with a focus on statistics and working with real data. Topics include discrete and continuous distributions, parameter estimation, hypothesis testing, and chi-square tests. Students work with data that aligns with the field of their respective section. Prerequisite(s): C- or better in MATH 1100 or MATH 1210 Repeatable for credit: No Grade Mode: Standard
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