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 Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Basic Info |
Course 3 of 5 in the
Executive Data Science Specialization.
|
| Commitment | 1 week of study, 4-6 hours |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: What this Course is About
- Reading: Pre-Course Survey
- Reading: Course Textbook: The Art of Data Science
- Reading: Conversations on Data Science
- Reading: Data Science as Art
- Video: Data Analysis Iteration
- Video: Stages of Data Analysis
- Reading: Epicycles of Analysis
- Video: Six Types of Questions
- Reading: Six Types of Questions
- Video: Characteristics of a Good Question
- Reading: Characteristics of a Good Question
- Video: Exploratory Data Analysis Goals & Expectations
- Reading: EDA Check List
- Video: Using Statistical Models to Explore Your Data (Part 1)
- Video: Using Statistical Models to Explore Your Data (Part 2)
- Reading: Assessing a Distribution
- Reading: Assessing Linear Relationships
- Video: Exploratory Data Analysis: When to Stop
- Reading: Exploratory Data Analysis: When Do We Stop?
- Video: Making Inferences from Data: Introduction
- Video: Populations Come in Many Forms
- Video: Inference: What Can Go Wrong
- Reading: Factors Affecting the Quality of Inference
- Reading: A Note on Populations
- Video: General Framework
- Video: Associational Analyses
- Video: Prediction Analyses
- Video: Inference vs. Prediction
- Reading: Inference vs. Prediction
- Video: Interpreting Your Results
- Reading: Interpreting Your Results
- Video: Routine Communication in Data Analysis
- Video: Making a Data Analysis Presentation
- Reading: Routine Communication
- Reading: Post-Course Survey
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Excellent course! Highly recommend it.
Dr. Peng covers the essentials of Managing Data Analysis in a balanced way, and empowers the data science manager/executive to be able to apply these right away.
Very detailed and extremely helpful in my current role in leading a Data Science Team.
Amazing course . Must for all data professionals.
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VG
Good overview of the process. Helped me in bridging data analysis processes with things that I already do as part of project management or business analytics/decision support projects.