This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.
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About this Course
최근 조회 34,654회
학습자 경력 결과
29%
이 강좌를 수료한 후 새로운 경력 시작하기
30%
이 강좌를 통해 확실한 경력상 이점 얻기
10%
급여 인상 또는 승진하기
배울 내용
Differentiate between various types of data pulls
Describe the basic data analysis iteration
Explore datasets to determine if data is appropriate for a project
Use statistical findings to create convincing data analysis presentations
귀하가 습득할 기술
Data AnalysisCommunicationInterpretationExploratory Data Analysis
학습자 경력 결과
29%
이 강좌를 수료한 후 새로운 경력 시작하기
30%
이 강좌를 통해 확실한 경력상 이점 얻기
10%
급여 인상 또는 승진하기
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 6시간 필요
영어
자막: 영어, 일본어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 6시간 필요
Managing Data Analysis
Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the lecture. I'm excited to have you in the class and look forward to your contributions to the learning community. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!
완료하는 데 6시간 필요
19개 동영상 (총 144분), 17 readings, 7 quizzes
19개의 동영상
Data Analysis Iteration8m
Stages of Data Analysis1m
Six Types of Questions6m
Characteristics of a Good Question6m
Exploratory Data Analysis Goals & Expectations11m
Using Statistical Models to Explore Your Data (Part 1)13m
Using Statistical Models to Explore Your Data (Part 2)5m
Exploratory Data Analysis: When to Stop6m
Making Inferences from Data: Introduction5m
Populations Come in Many Forms4m
Inference: What Can Go Wrong7m
General Framework8m
Associational Analyses10m
Prediction Analyses10m
Inference vs. Prediction12m
Interpreting Your Results10m
Routine Communication in Data Analysis6m
Making a Data Analysis Presentation5m
17개의 읽기 자료
Pre-Course Survey10m
Course Textbook: The Art of Data Science10m
Conversations on Data Science10m
Data Science as Art10m
Epicycles of Analysis10m
Six Types of Questions10m
Characteristics of a Good Question10m
EDA Check List10m
Assessing a Distribution10m
Assessing Linear Relationships10m
Exploratory Data Analysis: When Do We Stop?10m
Factors Affecting the Quality of Inference10m
A Note on Populations10m
Inference vs. Prediction10m
Interpreting Your Results10m
Routine Communication10m
Post-Course Survey10m
7개 연습문제
Data Analysis Iteration10m
Stating and Refining the Question16m
Exploratory Data Analysis10m
Inference10m
Formal Modeling, Inference vs. Prediction10m
Interpretation10m
Communication10m
4.6
309개의 리뷰Managing Data Analysis의 최상위 리뷰
하이라이트
Helpful quizzes
(3)Well-organized content
(24)A long course compared to others in the specialization, but a lot of great material. Very well presented, the instructors know how to present this material and make it easy to grasp and understand.
The course is full of the cases and the real life examples coupled with the theory background. Its very simple to understand and the course will definitely be of an value for people looking for
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존스홉킨스대학교
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Executive Data Science 전문 분야 정보
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
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