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DATA SCIENCE PROGRAMMING II

COMPSCI 320
과목 설명

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.

선수과목

COMPSCI 220 (or COMP SCI 301 prior to Spring 2020), COMPSCI 300 , COMPSCI 319 , graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone Certificate

충족 요건
학점

4

개설 시기

Not Applicable

평점
3.82

9.69% 과거 데이터 대비

수료율
99.56%

2.98% 과거 데이터 대비

A 비율
80.7%

51.52% 과거 데이터 대비

학급 규모
456

29.33% 과거 데이터 대비

2026 Summr Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

COMPUTING IN DATA SCIENCE AND STATISTICS

STAT 606

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.

DATA SCIENCE & ENGINEERING

ECE 204

A hands-on introduction to Data Science using the Python programming language. Data-centric and computational thinking. Describe, analyze, and make predictions using data from real-world phenomena. Programming in Python. Importing, manipulating, summarizing, and visualizing data of various types. Notions of bias, fairness, and ethics in data science.

DATA SCIENCE PROGRAMMING I FOR RESEARCH

COMPSCI 319

Introduction to Data Science programming using Python. In addition to a survey of programming basics (control flow and data structures), web scraping, database queries, and tabular analysis will be introduced. Projects will emphasize analyzing real datasets in a variety of forms and visual communication using plotting tools. Similar toCOMP SCI 220but the pedagogical style of the projects will be adapted to graduate students in fields other than computer science and data science. No previous programming experience required.

THEORY & ALGORITHMS FOR DATA SCIENCE

COMPSCI 541

Theoretical methods for data science. Topics include: review of probability background, concentration inequalities, geometry of high dimensional random variables, parametric and non-parametric estimation, selected topics from optimization (optimality conditions; deterministic and stochastic gradient descent), PAC learning, sample complexity and algorithms for linear classification and regression, and property/distribution testing. Uses Python programming language.

DATA SCIENCE PROGRAMMING I

COMPSCI 220

Introduction to Data Science programming using Python. No previous programming experience required. Emphasis on analyzing real datasets in a variety of forms and visual communication.