Ce cours fait partie de la Spécialisation Executive Data Science
Managing Data Analysis
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À propos de ce cours
4.5
1,779 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
Ce que vous allez apprendre
- 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
Compétences que vous acquerrez
Data AnalysisCommunicationInterpretationExploratory Data Analysis
Programme du cours : ce que vous apprendrez dans ce cours
1
Section
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 vidéos (Total 144 min), 17 lectures, 7 quiz
19 vidéos
Data Analysis Iteration8 min
Stages of Data Analysis1 min
Six Types of Questions6 min
Characteristics of a Good Question6 min
Exploratory Data Analysis Goals & Expectations11 min
Using Statistical Models to Explore Your Data (Part 1)13 min
Using Statistical Models to Explore Your Data (Part 2)5 min
Exploratory Data Analysis: When to Stop6 min
Making Inferences from Data: Introduction5 min
Populations Come in Many Forms4 min
Inference: What Can Go Wrong7 min
General Framework8 min
Associational Analyses10 min
Prediction Analyses10 min
Inference vs. Prediction12 min
Interpreting Your Results10 min
Routine Communication in Data Analysis6 min
Making a Data Analysis Presentation5 min
17 lectures
Pre-Course Survey10 min
Course Textbook: The Art of Data Science10 min
Conversations on Data Science10 min
Data Science as Art10 min
Epicycles of Analysis10 min
Six Types of Questions10 min
Characteristics of a Good Question10 min
EDA Check List10 min
Assessing a Distribution10 min
Assessing Linear Relationships10 min
Exploratory Data Analysis: When Do We Stop?10 min
Factors Affecting the Quality of Inference10 min
A Note on Populations10 min
Inference vs. Prediction10 min
Interpreting Your Results10 min
Routine Communication10 min
Post-Course Survey10 min
7 exercices pour s'entraîner
Data Analysis Iteration10 min
Stating and Refining the Question16 min
Exploratory Data Analysis10 min
Inference10 min
Formal Modeling, Inference vs. Prediction10 min
Interpretation10 min
Communication10 min
4.5
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(3)Well-organized content
(24)par 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.
par 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
Enseignants
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
À propos de 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.
À propos de la Spécialisation Executive Data Science
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.
Foire Aux Questions
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?
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