Managing Data Analysis

1,618 ratings
226 reviews

Course 3 of 5 in the Executive Data Science Specialization

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

100% online course

Start instantly and learn at your own schedule.

Approx. 6 hours to complete

Suggested: 1 week of study, 4-6 hours
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Subtitles: English, Japanese

What you will learn

  • Check
    Describe the basic data analysis iteration
  • Check
    Differentiate between various types of data pulls
  • Check
    Explore datasets to determine if data is appropriate for a project
  • Check
    Use statistical findings to create convincing data analysis presentations

Skills you will gain

Data AnalysisData ManagementData SciencePredictive Modelling

100% online course

Start instantly and learn at your own schedule.

Approx. 6 hours to complete

Suggested: 1 week of study, 4-6 hours
Comment Dots


Subtitles: English, Japanese

Syllabus - What you will learn from this course


6 hours to complete

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 (Total 144 min), 17 readings, 7 quizzes
Video19 videos
Data Analysis Iteration8m
Stages of Data Analysis1m
Six Types of Questions6m
Characteristics of a Good Question6m
Exploratory Data Analysis Goals & Expectations11m
Using Statistical Models to Explore Your Data (Part 1)13m
Using Statistical Models to Explore Your Data (Part 2)5m
Exploratory Data Analysis: When to Stop6m
Making Inferences from Data: Introduction5m
Populations Come in Many Forms4m
Inference: What Can Go Wrong7m
General Framework8m
Associational Analyses10m
Prediction Analyses10m
Inference vs. Prediction12m
Interpreting Your Results10m
Routine Communication in Data Analysis6m
Making a Data Analysis Presentation5m
Reading17 readings
Pre-Course Survey10m
Course Textbook: The Art of Data Science10m
Conversations on Data Science10m
Data Science as Art10m
Epicycles of Analysis10m
Six Types of Questions10m
Characteristics of a Good Question10m
EDA Check List10m
Assessing a Distribution10m
Assessing Linear Relationships10m
Exploratory Data Analysis: When Do We Stop?10m
Factors Affecting the Quality of Inference10m
A Note on Populations10m
Inference vs. Prediction10m
Interpreting Your Results10m
Routine Communication10m
Post-Course Survey10m
Quiz7 practice exercises
Data Analysis Iteration10m
Stating and Refining the Question16m
Exploratory Data Analysis10m
Formal Modeling, Inference vs. Prediction10m
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Top Reviews

Well-organized content
Helpful quizzes
By ELMar 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.

By STNov 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



Jeff Leek, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

Roger D. Peng, PhD

Associate Professor, Biostatistics

About 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....

Frequently Asked Questions

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