COMPUTING IN DATA SCIENCE AND STATISTICS

Name: COMPUTING IN DATA SCIENCE AND STATISTICS
Availability: OutOfStock
Rating: 4.3 (102 reviews)

STAT 606

과목 설명

A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.

선수과목

Declared in Statistics MS or Statistics VISP (undergraduate)

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

3.35

-2.15% 과거 데이터 대비

수료율

94.12%

-3.03% 과거 데이터 대비

A 비율

35.29%

-12.2% 과거 데이터 대비

학급 규모

-50% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

DATA SCIENCE COMPUTING PROJECT

STAT 605

The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs utilized include Linux, R, distributed computing, powerful editor(s), git/github, and other related tools. Work in the class will be done in teams to research, develop, write, and make presentations related to a variety of data analysis projects.

STATISTICAL DATA VISUALIZATION

STAT 436

Techniques for visualization within data science workflows. Topics include data preparation; exploratory data analysis; spatial, tabular, and graph structured data; dimensionality reduction; model visualization and interpretability; interactive queries and navigation.

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.

DATA SCIENCE PROGRAMMING II

COMPSCI 320

Intermediate approach to Data Science programming using Python. Experience with basic tabular analysis in Python is assumed. Learn to implement data structures (e.g., graphs) to efficiently represent datasets. Software-engineering tools such as version control and Python virtual environments will be introduced, with an emphasis on reproducibility of analysis. Tracing and A/B testing will be introduced as techniques for generating meaningful datasets. Introduces basic classification, clustering, optimization, and simulation techniques. Plotting and visual communication will be emphasized throughout the course.

DATA SCIENCE COMPUTING PROJECT

STAT 405

The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs used include Linux, R, distributed computing, text editor(s), git/github, and other related tools. Work in teams to research, develop, write, and make presentations related to a variety of data analysis projects.