数据分析管理

About this Course This one-week course describes the pr…

约翰霍普金斯大学

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数据分析管理

About this Course

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

数据分析管理 is course 3 of 5 in the Executive Data Science Specialization.

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.

授课教师

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Roger Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Syllabus

Week 1 Managing Data Analysis
Introduction
The Data Analysis Iteration

Six Types of Questions
Characteristics of a Good Question
Exploratory Data Analysis
Using Models to Explore Your Data
Exploratory Data Analysis: When to Stop
Inference
Formal Modeling
Inference vs. Prediction: Implications for Modeling Strategy
Interpretation of Results
Communication

Quiz: Data Analysis Iteration
Quiz: Stating and Refining the Question
Quiz: Exploratory Data Analysis
Quiz: Inference
Quiz: Formal Modeling, Inference vs. Prediction
Quiz: Interpretation
Quiz: Communication

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