John Hopkins University - Managing Data Analysis
5.0 /5- Offered byCoursera
Managing Data Analysis at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Managing Data Analysis at Coursera Highlights
- 26%
- started a new career after completing these courses.
- 28%
- got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Managing Data Analysis at Coursera Course details
- 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
Managing Data Analysis at Coursera Curriculum
Managing Data Analysis
What this Course is About
Data Analysis Iteration
Stages of Data Analysis
Six Types of Questions
Characteristics of a Good Question
Exploratory Data Analysis Goals & Expectations
Using Statistical Models to Explore Your Data (Part 1)
Using Statistical Models to Explore Your Data (Part 2)
Exploratory Data Analysis: When to Stop
Making Inferences from Data: Introduction
Populations Come in Many Forms
Inference: What Can Go Wrong
General Framework
Associational Analyses
Prediction Analyses
Inference vs. Prediction
Interpreting Your Results
Routine Communication in Data Analysis
Making a Data Analysis Presentation
Pre-Course Survey
Course Textbook: The Art of Data Science
Conversations on Data Science
Data Science as Art
Epicycles of Analysis
Six Types of Questions
Characteristics of a Good Question
EDA Check List
Assessing a Distribution
Assessing Linear Relationships
Exploratory Data Analysis: When Do We Stop?
Factors Affecting the Quality of Inference
A Note on Populations
Inference vs. Prediction
Interpreting Your Results
Routine Communication
Post-Course Survey
Data Analysis Iteration
Stating and Refining the Question
Exploratory Data Analysis
Inference
Formal Modeling, Inference vs. Prediction
Interpretation
Communication
Managing Data Analysis at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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