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

Created by: Johns Hopkins University

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	4.5 stars Average User Rating 4.5See what learners said

Syllabus

WEEK 1

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 videos, 17 readings

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

Graded: Data Analysis Iteration

Graded: Stating and Refining the Question

Graded: Exploratory Data Analysis

Graded: Inference

Graded: Formal Modeling, Inference vs. Prediction

Graded: Interpretation

Graded: Communication

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Coursework

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Creators

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.

Pricing

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Ratings and Reviews

Rated 4.5 out of 5 of 1,107 ratings

Buen curso me encanto

The course was good but as a suggestion, walkthrough of an example for the modelling would have helped. I was little confused when the equation was used during the course to explain the confounder, predictor and outcome. Instead of using X, Y, Z - may be use an actual example and show they all relate would have made the course

Great course!

sometime it was not easy to understand the lecturer. also, it would be good to try some things out versus reading the expamples. other than that - a great course!