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R 프로그래밍

이 과정에서는 R로 프로그래밍하는 방법과 효과적인 데이터 분석을 위해 R을 사용하는 방법을 배웁니다. 통계 프로그래밍 환경에 필요한 소프트웨어를 설치 및 구성하는 방법과 고급 통계 언어로 구현되는 일반적인 프로그래밍 언어 개념을 설명합니다. 이 과정은 R 프로그래밍, R로 데이터 읽기, R 패키지 액세스, R 함수 작성, 디버깅, R 코드 프로파일링, R 코드 구성 및 주석 달기를 포함하는 통계 컴퓨팅의 실제 문제를 다룹니다. 통계 데이터 분석의 주제는 실제 사례를 제공합니다.

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Syllabus

1주차: 배경, 시작하기, 너트와 볼트

이번 주에는 R을 시작하는 데 필요한 기본 사항을 다룹니다. 배경 자료 강의에는 과정 역학에 대한 정보와 R 설치에 대한 일부 비디오가 포함되어 있습니다. 1주차 비디오에서는 R 및 S의 역사를 다루고 R의 기본 데이터 유형을 살펴보고, 데이터를 읽고 쓰는 기능을 설명합니다. 나열된 순서대로 비디오를 시청하는 것이 좋지만 순서대로 시청하지 않아도 스토리라인이 끊기지는 않습니다.

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학문적인 배경에 따라 통계적 컴퓨팅의 필요한 주제를 탐구합니다

교수 Jeff Leek, 박사는 통계학 및 전산학 분야의 저명한 학자입니다

현실적인 사례 연구를 통해 통계 데이터 분석을 탐구합니다

R 프로그래밍, 데이터 읽기, R 패키지 액세스, 디버깅, 프로파일링 등 통계 컴퓨팅의 실질적인 문제를 다룹니다

초보자를 위한 R 프로그래밍 기초를 제공하는 첫 주 강의를 포함합니다

실습 과제를 통해 학습 과정을 보강하는 프로그래밍 과제가 있습니다

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Reviews summary

R 프로그래밍 기초 및 데이터 분석

학생들은 이 과정이 R 프로그래밍 초보자에게 탁월한 출발점이라고 말합니다. 기본 데이터 유형, 제어 구조, 함수 등 핵심 개념을 명확하게 설명하며, 실습 위주의 프로그래밍 과제를 통해 학습한 내용을 직접 적용해 볼 수 있어 매우 유용하다고 평가합니다. 강사의 명료한 설명은 복잡한 주제를 쉽게 이해하도록 돕는 긍정적인 요소로 자주 언급됩니다. 반면, 기존 프로그래밍 경험이 있는 학습자나 고급 R 사용자에게는 다소 기초적이거나 느리게 느껴질 수 있다는 의견도 있습니다. 전반적으로 데이터 분석을 위한 탄탄한 기초를 다지는 데 이 코스가 매우 효과적이라고 합니다.

실제 코딩 연습을 통해 학습 내용을 효과적으로 적용합니다.

"프로그래밍 과제들이 실제 데이터 분석에 필요한 스킬을 익히는 데 정말 도움이 많이 되었습니다."

"손으로 직접 코딩하며 개념을 익힐 수 있었던 점이 이 과정의 가장 큰 장점이라고 생각합니다."

"이론만 배우는 것이 아니라, 다양한 실습 과제를 통해 실력을 향상시킬 수 있었습니다."

강사의 설명이 매우 명확하여 복잡한 개념도 쉽게 이해됩니다.

"강사님의 설명이 정말 깔끔하고 이해하기 쉬웠습니다. 어려운 부분도 차근차근 잘 알려주셔서 좋았어요."

"강의가 매우 체계적이고, 핵심 내용을 간결하게 전달해서 집중하기 좋았습니다."

"강사의 교수법이 탁월하여 R의 기본 원리를 완벽히 습득할 수 있었습니다."

R 프로그래밍을 처음 시작하는 학습자에게 최적화된 과정입니다.

"R을 처음 배우는 저에게 정말 좋은 시작이었습니다. 모든 것이 쉽게 설명되어 있어 따라가기 좋았어요."

"프로그래밍 경험이 전무한 상태에서 시작했는데, 개념 설명이 워낙 명확해서 기초를 튼튼하게 다질 수 있었습니다."

"R을 처음 접하는 사람들에게 이해하기 쉬운 방식으로 핵심적인 프로그래밍 개념을 알려줍니다."

고급 주제나 심화 데이터 분석은 추가 학습이 필요합니다.

"이 과정은 기본기에 충실하지만, 더 복잡한 통계 분석이나 머신러닝 적용까지는 다루지 않습니다."

"R의 기초를 닦는 데는 충분하지만, 실제 업무에 바로 적용하기 위한 고급 기술은 별도로 학습해야 합니다."

"일부 고급 R 패키지나 성능 최적화 같은 주제는 추가 자료를 찾아봐야 했습니다."

기존 프로그래밍 경험자에게는 다소 기초적일 수 있습니다.

"저는 다른 언어 경험이 있어서 초반 내용이 다소 지루했습니다. 완전히 처음인 분들에게는 좋을 것 같아요."

"R에 대한 기본적인 지식이 있다면 이 과정은 너무 기초적이라 시간을 절약하고 다른 코스를 찾아보는 게 좋습니다."

"강의 속도가 느린 편이라, 이미 R이나 프로그래밍 경험이 있다면 복습용으로만 적합할 것 같습니다."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in R 프로그래밍 with these activities:

연습문제 풀기

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R 프로그래밍 기본 사항 연습을 통해 숙련도 향상.

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제공된 연습문제 수집
문제 풀기
답안 확인 및 개선 사항 파악

간단한 R 프로젝트 시작하기

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실제 프로젝트를 수행하여 R 프로그래밍 기술 적용 및 심화.

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작은 규모의 실습 프로젝트 계획
데이터 수집 및 준비
R을 사용하여 데이터 분석 및 시각화
프로젝트 결과 보고서 작성

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Career center

Learners who complete R 프로그래밍 will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. This course provides a strong foundation in R programming, which is a widely used statistical software package. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as probability, inference, and regression analysis, which are all essential skills for statisticians.

See salaries and explore the career path for Statistician

Data Scientist

Data scientists use data to solve complex problems and make informed decisions. This course provides a strong foundation in R programming, which is a powerful tool for data analysis and visualization. By learning how to use R, you will be able to effectively analyze data, identify trends, and communicate your findings to stakeholders. Additionally, this course covers topics such as data wrangling, statistical modeling, and machine learning, which are all essential skills for data scientists.

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Data Analyst

Data analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course provides a solid foundation in R programming, which is a powerful tool for data analysis and visualization. By learning how to use R, you will be able to effectively analyze data, identify trends, and communicate your findings to stakeholders. Additionally, this course covers topics such as data wrangling, statistical modeling, and machine learning, which are all essential skills for data analysts.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine learning engineers design and develop machine learning models to solve real-world problems. This course provides a solid foundation in R programming, which is a popular language for machine learning. By learning how to use R, you will be able to build and train machine learning models, evaluate their performance, and deploy them into production. Additionally, this course covers topics such as supervised learning, unsupervised learning, and deep learning, which are all essential skills for machine learning engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data engineers design and build data pipelines to collect, store, and process data. This course provides a solid foundation in R programming, which is a popular language for data engineering. By learning how to use R, you will be able to build and maintain data pipelines, perform data transformations, and ensure data quality. Additionally, this course covers topics such as data warehousing, big data, and cloud computing, which are all essential skills for data engineers.

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Biostatistician

Biostatisticians apply statistical methods to solve problems in the field of biology. This course provides a solid foundation in R programming, which is a widely used statistical software package in the biological sciences. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as bioinformatics, genomics, and epidemiology, which are all essential skills for biostatisticians.

See salaries and explore the career path for Biostatistician

Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course provides a solid foundation in R programming, which is a widely used statistical software package in the insurance industry. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as risk management, insurance pricing, and financial modeling, which are all essential skills for actuaries.

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Public Health Analyst

Public health analysts use data to improve the health of populations. This course provides a solid foundation in R programming, which is a popular language for statistical computing and data analysis. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as epidemiology, biostatistics, and public health, which are all essential skills for public health analysts.

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Operations Research Analyst

Operations research analysts use mathematical and statistical methods to solve problems in the field of operations management. This course provides a solid foundation in R programming, which is a widely used statistical software package in the field of operations research. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as linear programming, network optimization, and simulation, which are all essential skills for operations research analysts.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical methods to analyze financial data and make investment decisions. This course provides a solid foundation in R programming, which is a popular language for quantitative analysis. By learning how to use R, you will be able to build and train financial models, evaluate their performance, and make informed investment decisions. Additionally, this course covers topics such as time series analysis, risk management, and portfolio optimization, which are all essential skills for quantitative analysts.

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Economist

Economists use economic theory and data to analyze economic issues and make policy recommendations. This course provides a solid foundation in R programming, which is a popular language for economic modeling and data analysis. By learning how to use R, you will be able to build and test economic models, analyze economic data, and communicate your findings effectively. Additionally, this course covers topics such as econometrics, macroeconomics, and microeconomics, which are all essential skills for economists.

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Financial Analyst

Financial analysts use financial data to make investment recommendations. This course provides a solid foundation in R programming, which is a popular language for financial modeling and data analysis. By learning how to use R, you will be able to build and test financial models, analyze financial data, and communicate your findings effectively. Additionally, this course covers topics such as financial statement analysis, valuation, and risk management, which are all essential skills for financial analysts.

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Market Researcher

Market researchers use data to understand consumer behavior and trends. This course provides a solid foundation in R programming, which is a popular language for market research and data analysis. By learning how to use R, you will be able to collect and analyze market data, identify trends, and communicate your findings effectively. Additionally, this course covers topics such as survey design, data analysis, and market segmentation, which are all essential skills for market researchers.

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Epidemiologist

Epidemiologists investigate the causes and distribution of disease. This course provides a solid foundation in R programming, which is a popular language for statistical computing and data analysis. By learning how to use R, you will be able to perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as biostatistics, epidemiology, and public health, which are all essential skills for epidemiologists.

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Software Engineer

Software engineers design, develop, and maintain software applications. This course provides a solid foundation in R programming, which is a popular language for statistical computing and data analysis. By learning how to use R, you will be able to develop software applications that can perform complex statistical analyses, create visualizations, and communicate your findings effectively. Additionally, this course covers topics such as object-oriented programming, software design, and testing, which are all essential skills for software engineers.

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Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in R 프로그래밍.

The R Book

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Provides a comprehensive overview of R, covering the basics of the language as well as more advanced topics. It valuable resource for both beginners and experienced R users.

R for Data Science

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Provides a practical guide to using R for data science. It covers a wide range of topics, from data import and cleaning to data visualization and modeling.

R 프로그래밍

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Reviews summary

R 프로그래밍 기초 및 데이터 분석

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