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課程信息
4.5
1,783 ratings
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.
This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know how to….
1. Describe the basic data analysis iteration
2. Identify different types of questions and translate them to specific datasets
3. Describe different types of data pulls
4. Explore datasets to determine if data are appropriate for a given question
5. Direct model building efforts in common data analyses
6. Interpret the results from common data analyses
7. Integrate statistical findings to form coherent data analysis presentations
Commitment: 1 week of study, 4-6 hours
Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
您將學到的內容有
- Describe the basic data analysis iteration
- Differentiate between various types of data pulls
- Explore datasets to determine if data is appropriate for a project
- Use statistical findings to create convincing data analysis presentations
您將獲得的技能
Data AnalysisCommunicationInterpretationExploratory Data Analysis
教學大綱 - 您將從這門課程中學到什麼
1
章節
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!
19 個視頻（共 144 分鐘）, 17 個閱讀材料, 7 個測驗
19 個視頻
Data Analysis Iteration8分鐘
Stages of Data Analysis1分鐘
Six Types of Questions6分鐘
Characteristics of a Good Question6分鐘
Exploratory Data Analysis Goals & Expectations11分鐘
Using Statistical Models to Explore Your Data (Part 1)13分鐘
Using Statistical Models to Explore Your Data (Part 2)5分鐘
Exploratory Data Analysis: When to Stop6分鐘
Making Inferences from Data: Introduction5分鐘
Populations Come in Many Forms4分鐘
Inference: What Can Go Wrong7分鐘
General Framework8分鐘
Associational Analyses10分鐘
Prediction Analyses10分鐘
Inference vs. Prediction12分鐘
Interpreting Your Results10分鐘
Routine Communication in Data Analysis6分鐘
Making a Data Analysis Presentation5分鐘
17 個閱讀材料
Pre-Course Survey10分鐘
Course Textbook: The Art of Data Science10分鐘
Conversations on Data Science10分鐘
Data Science as Art10分鐘
Epicycles of Analysis10分鐘
Six Types of Questions10分鐘
Characteristics of a Good Question10分鐘
EDA Check List10分鐘
Assessing a Distribution10分鐘
Assessing Linear Relationships10分鐘
Exploratory Data Analysis: When Do We Stop?10分鐘
Factors Affecting the Quality of Inference10分鐘
A Note on Populations10分鐘
Inference vs. Prediction10分鐘
Interpreting Your Results10分鐘
Routine Communication10分鐘
Post-Course Survey10分鐘
7 個練習
Data Analysis Iteration10分鐘
Stating and Refining the Question16分鐘
Exploratory Data Analysis10分鐘
Inference10分鐘
Formal Modeling, Inference vs. Prediction10分鐘
Interpretation10分鐘
Communication10分鐘
4.5
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Well-organized content
(24)Helpful quizzes
(3)創建者 EL•Mar 1st 2017
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.
創建者 ST•Nov 23rd 2016
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
講師
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
關於 Johns Hopkins University
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.
關於 Executive Data Science 專項課程
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.
常見問題
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
還有其他問題嗎？請訪問 學生幫助中心。
