DATA SCIENCE MODELING II

Name: DATA SCIENCE MODELING II
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Rating: 3.8 (2627 reviews)

STAT 340

과목 설명

Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation. Features the analysis of real-world data sets and the communication of findings in a clear and reproducible manner within a project setting.

선수과목

(MATH 211 , 217, or MATH 221 ) and STAT 240

충족 요건

ISYE 649 , BMI/STAT 620 , GENBUS 656 , ISYE 445 , ACTSCI 652 , ACTSCI 650 , STAT 349 , STAT 351 , ISYE 562 , COMPSCI/STAT 471 , ECON 570 , STAT 575 , ACTSCI 654 , STAT 456 , ACTSCI 657 , ACTSCI 300 , ACTSCI 655 , ECON 400 , ME 536 , STAT 433 , COMPSCI 566 , STAT 461 , CIVENGR 516 , STAT 443 , COMPSCI 541 , ISYE 625 , ACTSCI 640 , STAT 411 , ECON 695 , and STAT 421

학점

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미보고

평점

2.94

-3.98% 과거 데이터 대비

수료율

94.69%

-1.8% 과거 데이터 대비

A 비율

27.21%

-8.7% 과거 데이터 대비

학급 규모

452

54.85% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

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유사 과목

DATA SCIENCE WITH R

STAT 433

Perform Data Science as an iterative (back and forth) process of four different types of activities (data collection, data wrangling, data analysis, communication). Traverse through the five requisite stances (scientist, coder, mathematician, methodologist, skeptic). Develop and hone a broad set of computational tools in R (but not the broadest) and a broad set of statistical/machine learning tools (but not the broadest). Focus on doing these with agility to make the coding "transparent" and serve the large goals of the project.

INTRODUCTION TO STATISTICS FOR SCIENCE AND ENGINEERING

STAT 324

Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.

R FOR STATISTICS I

STAT 303

An understanding of the commonly used statistical language R. Topics will include using R to manipulate data and perform exploratory data analysis.

DATA SCIENCE MODELING I

STAT 240

Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.

R FOR STATISTICS II

STAT 304

Provides an understanding of the commonly used statistical language R. Topics will include writing conditional expressions, loops, and functions; manipulating data matrices and arrays; extracting data from text; and making high level visualizations of data.