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Jeff Leek, PhD,
Brian Caffo, PhD,
and
Roger D. Peng, PhD
이 과정에서는 데이터 과학자의 도구 상자에 있는 메인 도구와 아이디어를 소개합니다. 본 과정은 데이터 분석가와 데이터 과학자가 작업하는 데이터, 질문 및 도구의 개요에 대해 설명합니다. 이 과정에는 두 가지 구성 요소가 있습니다. 첫 번째는 데이터를 실행 가능한 지식으로 바꾸는 아이디어에 대한 개념적 소개입니다. 두 번째는 버전 관리, 마크다운, git, GitHub, R 및 RStudio와 같은 프로그램에서 사용할 도구에 대한 실용적인 소개입니다.
Register for this course and see more details by visiting:
OpenCourser.com/course/qlypvd/deiteo
What's inside
Syllabus
데이터 과학 기초
이 모듈에서는 데이터 과학과 데이터 자체를 소개하고 정의합니다. 또한 문제가 발생했을 때 데이터 과학자가 도움을 받기위해 사용하는 일부 리소스에 대해서도 살펴봅시다.
R 및 RStudio
이 모듈에서는 R 및 RStudio를 시작하고 실행하는 데 도움을 줍니다. 그 과정에서 두 가지 모두에 대한 몇 가지 기본 사항과 데이터 과학자가 이를 사용하는 이유를 학습합니다.
Read more
Syllabus
데이터 과학 기초
이 모듈에서는 데이터 과학과 데이터 자체를 소개하고 정의합니다. 또한 문제가 발생했을 때 데이터 과학자가 도움을 받기위해 사용하는 일부 리소스에 대해서도 살펴봅시다.
R 및 RStudio
이 모듈에서는 R 및 RStudio를 시작하고 실행하는 데 도움을 줍니다. 그 과정에서 두 가지 모두에 대한 몇 가지 기본 사항과 데이터 과학자가 이를 사용하는 이유를 학습합니다.
Read more
버전 관리 및 GitHub
이 모듈에서는 버전 관리와 데이터 과학자에게 버전 관리가 중요한 이유를 학습합니다. 또한 Git 및 GitHub를 사용하여 데이터 과학 프로젝트에서 버전 관리를 매니징하는 방법을 배우게 됩니다.
R Markdown, Scientific Thinking 및 Big Data
이 마지막 모듈에서는 R Markdown을 사용하는 방법을 배우고 모든 성공적인 데이터 과학자에게 매우 중요한 세 가지 개념인 좋은 질문하기, 실험적 설계, 빅 데이터에 대해 소개합니다.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
適合한 대상: 데이터 분석 및 데이터 과학 분야의 초보자
데이터 과학자에게 필수적인 도구와 아이디어 소개
버전 관리, git, R, RStudio와 같은 실무적 도구 사용법 학습
강사진으로 유명한 전문가 포함
Accredited institutes, bootcamps, 실습과 유사한 실습 제공
Save this course
Save 데이터 과학자의 도구 상자 to your list so you can find it easily later:
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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 데이터 과학자의 도구 상자 with these
activities:
Review R and RStudio
Show steps
Reviewing R and RStudio will help you refresh your understanding of the basic concepts, data structures, and functions used in the course.
Browse courses on
R
Show steps
-
Install R and RStudio
-
Review the basics of R syntax
-
Practice loading and manipulating data in R
Join a study group for the course
Show steps
Joining a study group can help you learn from others, get help with difficult concepts, and stay motivated.
Show steps
-
Find a study group
-
Attend study group meetings regularly
-
Participate in discussions and ask questions
Follow tutorials on data cleaning and wrangling
Show steps
Following tutorials on data cleaning and wrangling will help you develop the skills necessary to prepare data for analysis.
Browse courses on
Data Cleaning
Show steps
-
Find a tutorial on data cleaning and wrangling
-
Follow the tutorial step-by-step
-
Practice the techniques on your own dataset
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on data analysis and visualization
Show steps
Solving practice problems will help you develop your analytical skills and ability to communicate your findings visually.
Browse courses on
Data Analysis
Show steps
-
Find a set of practice problems
-
Solve the problems on your own
-
Check your answers and learn from your mistakes
Create a data visualization project
Show steps
Creating a data visualization project will help you learn how to effectively communicate data insights to others.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and a visualization type
-
Prepare the data for visualization
-
Create the visualization
-
Write a report or presentation explaining your findings
Mentor a new student in the course
Show steps
Mentoring a new student can help you reinforce your understanding of the course material and develop your leadership skills.
Show steps
-
Find a new student to mentor
-
Meet with your mentee regularly
-
Provide guidance and support
Contribute to an open-source data science project
Show steps
Contributing to an open-source data science project can help you gain experience with real-world data science tools and techniques.
Browse courses on
Collaborative Learning
Show steps
-
Find an open-source data science project
-
Identify a way to contribute
-
Make your contribution
Review R and RStudio
Show steps
Reviewing R and RStudio will help you refresh your understanding of the basic concepts, data structures, and functions used in the course.
Browse courses on
R
Show steps
Show steps
Show steps
- Install R and RStudio
- Review the basics of R syntax
- Practice loading and manipulating data in R
Join a study group for the course
Show steps
Joining a study group can help you learn from others, get help with difficult concepts, and stay motivated.
Show steps
Show steps
Show steps
- Find a study group
- Attend study group meetings regularly
- Participate in discussions and ask questions
Follow tutorials on data cleaning and wrangling
Show steps
Following tutorials on data cleaning and wrangling will help you develop the skills necessary to prepare data for analysis.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find a tutorial on data cleaning and wrangling
- Follow the tutorial step-by-step
- Practice the techniques on your own dataset
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on data analysis and visualization
Show steps
Solving practice problems will help you develop your analytical skills and ability to communicate your findings visually.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a set of practice problems
- Solve the problems on your own
- Check your answers and learn from your mistakes
Create a data visualization project
Show steps
Creating a data visualization project will help you learn how to effectively communicate data insights to others.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and a visualization type
- Prepare the data for visualization
- Create the visualization
- Write a report or presentation explaining your findings
Mentor a new student in the course
Show steps
Mentoring a new student can help you reinforce your understanding of the course material and develop your leadership skills.
Show steps
Show steps
Show steps
- Find a new student to mentor
- Meet with your mentee regularly
- Provide guidance and support
Contribute to an open-source data science project
Show steps
Contributing to an open-source data science project can help you gain experience with real-world data science tools and techniques.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Find an open-source data science project
- Identify a way to contribute
- Make your contribution
Career center
Learners who complete 데이터 과학자의 도구 상자 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist's primary role is to help make business decisions using data. They work with a company's raw data and use techniques from computer science, statistics, and mathematics to build models to help maximize a business's success. This course may be useful for Data Scientists as it can help them enhance their skills in data management, modeling, and data visualization.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and maintaining machine learning models. They work with a variety of machine learning algorithms and techniques to create models that can solve a variety of business problems. This course may be useful for Machine Learning Engineers as it can help them develop their data analysis skills, which will allow them to build and maintain better machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to help improve the efficiency of business operations. They work with a variety of data sources and techniques to create models that can help businesses optimize their processes. This course may be useful for Operations Research Analysts as it can help them develop their data analysis skills, which will allow them to build and maintain better models.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They help businesses make informed decisions by providing insights into data trends and patterns. This course may be useful for Statisticians as it can help them develop skills in data management, modeling, and data visualization.
Data Architect
Data Architect
Data Architects are responsible for designing and building the data architecture for an organization. They work with a variety of data sources and technologies to create a data architecture that meets the needs of the organization. This course may be useful for Data Architects as it can help them develop their data analysis skills, which will allow them to design and build better data architectures.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to find meaningful insights. They are able to use their findings to create useful reports that can be used to improve a business's decision making. This course may be useful for Data Analysts as it can help them develop skills in data management, modeling, and data visualization.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis. They work with a variety of technologies, including databases, data warehouses, and cloud computing, to ensure that data is available to data scientists and data analysts in a timely and efficient manner. This course may be useful for Data Engineers as it can help them develop skills in data management and data visualization.
Actuary
Actuary
Actuaries use data to help assess and manage risks. They work with a variety of data sources and techniques to create models that can help organizations make informed decisions about risk. This course may be useful for Actuaries as it can help them develop their data analysis skills, which will allow them to build and maintain better models.
Financial Analyst
Financial Analyst
Financial Analysts use data to help make investment recommendations. They are responsible for analyzing financial data, such as company reports, economic indicators, and market trends, and making recommendations on how to invest money. This course may be useful for Financial Analysts as it can help them develop skills in data management, modeling, and data visualization.
Business Analyst
Business Analyst
Business Analysts work at the intersection of business and IT. They are responsible for bridging the gap between the two divisions by translating business requirements into technical requirements. This course can help Business Analysts as it provides them with a more thorough understanding of data analysis, which will allow them to better interact with Data Scientists and Data Analysts.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to an organization. They work with a variety of data sources and techniques to create models that can help organizations mitigate risks. This course may be useful for Risk Analysts as it can help them develop their data analysis skills, which will allow them to identify and assess risks more effectively.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers, to ensure that products meet the needs of customers. This course may be helpful for Product Managers as it can help them develop their data analysis skills, which will allow them to make better decisions about product development.
Research Analyst
Research Analyst
Research Analysts are responsible for gathering and analyzing data to provide insights to clients. They work with a variety of data sources and techniques to create reports and presentations that can help clients make informed decisions. This course may be useful for Research Analysts as it can help them develop their data analysis skills, which will allow them to provide better insights to clients.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course may be helpful for Software Engineers as it can help them develop their data analysis skills, which will allow them to design and develop better software systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to help make investment decisions. They are responsible for analyzing financial data, such as company reports, economic indicators, and market trends, and making recommendations on how to invest money. This course may be useful for Quantitative Analysts as it can help them develop skills in data management, modeling, and data visualization.
For more career information including salaries, visit:
OpenCourser.com/course/qlypvd/deiteo
Reading list
We've selected 11 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
데이터 과학자의 도구 상자.
Save
이 책은 데이터 과학 및 통계 분야의 강력한 도구인 R 언어에 대한 포괄적인 소개를 제공합니다. 데이터 관리, 시각화, 통계 모델링을 위한 R의 기능을 다룹니다.
이 책은 통계적 방법론의 종합적인 개요를 제공합니다. 데이터 분석, 예측 모델링, 통계적 추론에 대한 고급 주제를 다룹니다.
이 책은 머신 러닝과 통계 모델링의 기본 개념을 다룹니다. 데이터 과학자에게 필수적인 주제인 회귀, 분류, 클러스터링에 대해 설명합니다.
Save
이 책은 데이터 집약적 애플리케이션 설계 및 구축에 대한 고급 가이드를 제공합니다. 분산 시스템, 데이터베이스, 빅 데이터 처리의 아키텍처 및 설계 고려 사항을 다룹니다.
Save
이 책은 딥 러닝의 이론적 기초와 응용을 다룹니다. 딥 러닝 모델의 구축, 훈련, 평가에 대해 설명합니다.
이 책은 Apache Spark를 사용한 데이터 집약적 분석을 다룹니다. 대규모 데이터 세트 처리, 기계 학습 모델 훈련, 스트리밍 데이터 분석에 대한 지침을 제공합니다.
Save
이 책은 R Markdown의 포괄적인 안내서를 제공합니다. R Markdown 문서 작성, 동적 보고서 생성, 웹 애플리케이션 구축에 대해 설명합니다.
이 책은 데이터 과학 프로젝트를 구축하기 위한 실용적인 안내서를 제공합니다. 데이터 수집, 처리, 분석, 시각화의 전체 프로세스를 다룹니다.
이 책은 비즈니스 컨텍스트에서 데이터 과학을 적용하는 데 초점을 맞춥니다. 데이터 분석을 사용하여 비즈니스 문제를 해결하고 의사 결정을 개선하는 방법을 설명합니다.
Save
이 책은 빅 데이터 분석의 비즈니스 및 기술적 측면을 다룹니다. 빅 데이터 프로젝트 구현, 데이터 거버넌스, 분석 결과 해석에 대한 가이드를 제공합니다.
이 책은 데이터 과학의 수학적 기초를 다룹니다. 확률, 통계, 선형 대수, 최적화와 같은 주제를 설명합니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qlypvd/deiteo
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Effort
1-4시간/주
Via
Coursera
Institution
Johns Hopkins University
Instructors
Jeff Leek, PhD
Brian Caffo, PhD
Roger D. Peng, PhD
Language
한국어
Transcripts
English
Español
Français
Português
Deutsch
Italiano
العربية
Pусский
tiếng Việt
Good to know
適合한 대상: 데이터 분석 및 데이터 과학 분야의 초보자
데이터 과학자에게 필수적인 도구와 아이디어 소개
버전 관리, git, R, RStudio와 같은 실무적 도구 사용법 학습
강사진으로 유명한 전문가 포함
Accredited institutes, bootcamps, 실습과 유사한 실습 제공
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