Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.
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About this Course
최근 조회 16,078회
학습자 경력 결과
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이 강좌를 수료한 후 새로운 경력 시작하기
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귀하가 습득할 기술
StatisticsData ScienceData AnalysisData Management
학습자 경력 결과
33%
이 강좌를 수료한 후 새로운 경력 시작하기
14%
이 강좌를 통해 확실한 경력상 이점 얻기
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 6시간 필요
권장: 1 week of study, 4-6 hours
영어
자막: 영어
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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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 5시간 필요
Introduction, the perfect data science experience
This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.
완료하는 데 5시간 필요
22개 동영상 (총 160분), 10 readings, 6 quizzes
22개의 동영상
Data science in the ideal versus real life Part 14m
Data science in the ideal versus real life Part 23m
Examples7m
Machine Learning vs. Traditional Statistics Part 114m
Machine Learning vs. Traditional Statistics Part 23m
Managing the Data Pull11m
Experimental design and observational analysis10m
Causality part 18m
Causality Part 29m
What Can Go Wrong?: Confounding5m
A/B Testing9m
Sampling bias and random sampling5m
Blocking and adjustment11m
Multiplicity6m
Effect size, significance, & modeling7m
Comparison with benchmark effects4m
Negative controls5m
Non-significance5m
Estimation Target is Relevant10m
Report writing8m
Version control4m
10개의 읽기 자료
Pre-Course Survey10m
Course structure10m
Grading10m
The data pull is clean10m
The experiment is carefully designed10m
The experiment is carefully designed, things to do10m
Results of analyses are clear10m
The decision is obvious10m
The analysis product is awesome10m
Post-Course Survey10m
6개 연습문제
The Data Pull is Clean10m
The experiment is carefully designed principles10m
The experiment is carefully designed, things to do10m
Results of analyses are clear8m
The Decision is Obvious10m
The analysis product is awesome10m
검토
DATA SCIENCE IN REAL LIFE의 최상위 리뷰
SM 제공Aug 19, 2017
A very good and concise course that helps to understand the basics of the Data Science and its applications. The examples are very relevant and helps to understand the topic easily.
ES 제공Nov 11, 2017
Highly educational course on the realities of data analysis. Many good tips for your own analyses as well as for managing others responsible for coherent and accurate analyses.
YY 제공Oct 26, 2016
Dr.Caffo is really well-versed with his field but I feel like concepts should be given more concrete examples so that they seem more interesting. Respect him all the way!
PE 제공Mar 11, 2017
Brian Caffo does a terrific job teaching some of more advanced material, I very much appreciate his jokes and humor, as well as his helpful explanations of the material.
NJ 제공Mar 4, 2018
Examples used in this course are related to Lifescience and candidates like me find it difficult to correlate. It would be beneficial to use some common life examples.
KL 제공Aug 12, 2016
Is good to have some data science background to enroll in this course, overall still good to learn and get the hint of how real life data scientist life is.
JA 제공May 8, 2018
Good course - I'm now confident to oversee an end-to-end data science experiment. Some interactivity would make this the perfect overview of data science.
PF 제공Feb 11, 2018
Another excellent Executive Data Science course. Brian gives clear and concise explanations of the ideal versus real world of the data science workplace.
RF 제공Dec 22, 2017
I like that this course examples the many ways an experiment/analysis can go wrong and how to address these issues. Very practical for the practitioner.
MM 제공Jan 6, 2017
Clear explanation of various concepts with good examples. Of course, reference to some cool cartoons from time to time made the concepts more memorable!
AG 제공Nov 13, 2016
An amazing course for those who are not very familiar with statistics and a very refreshing perspective for those who actually knows statistics!
ST 제공Apr 2, 2016
Again a course with depth in content but the presentation of the course could improve , it seems a bit patchy and pre-reads would help.
LL 제공Jan 28, 2019
This is a recommended course where you understand what are the requirements for a data engineer, data manager and data scientis
VV 제공Mar 7, 2020
A very well crafted course, with apt messaging and good assessments. Was able to learn a lot about the nuances of Data Science
EM 제공Oct 9, 2018
Great course, although, if you are not already working or have knowledge in this particular filed/topic, it is challenging.
ML 제공Mar 30, 2018
An excellent overview of the topic material without a lot of unnecessary clutter. Well-organized and -communicated. Kudos.
PS 제공Jul 7, 2019
The authors really present real situation and challenges that data scientists face in their daily activities. Very good.
KN 제공Apr 30, 2016
Good concepts - apply to anyone new to data science.\n\nLot of good 'read further' links and materials. Learnt a lot.
RA 제공Sep 23, 2018
Very easy to follow with good examples. The focus on this course was on practicality and I really appreciated that
WK 제공Jan 3, 2017
Excellent course. The material is good enough that will help me where to look for information, considerations, a
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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