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There is 1 module in 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
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!
What's included
19 videos17 readings7 assignments
Show info about module content
19 videos•Total 144 minutes
What this Course is About•3 minutes
Data Analysis Iteration•8 minutes
Stages of Data Analysis•1 minute
Six Types of Questions•7 minutes
Characteristics of a Good Question•7 minutes
Exploratory Data Analysis Goals & Expectations•12 minutes
Using Statistical Models to Explore Your Data (Part 1)•13 minutes
Using Statistical Models to Explore Your Data (Part 2)•5 minutes
Exploratory Data Analysis: When to Stop•7 minutes
Making Inferences from Data: Introduction•5 minutes
Populations Come in Many Forms•4 minutes
Inference: What Can Go Wrong•7 minutes
General Framework•9 minutes
Associational Analyses•10 minutes
Prediction Analyses•11 minutes
Inference vs. Prediction•12 minutes
Interpreting Your Results•10 minutes
Routine Communication in Data Analysis•7 minutes
Making a Data Analysis Presentation•5 minutes
17 readings•Total 170 minutes
Pre-Course Survey•10 minutes
Course Textbook: The Art of Data Science•10 minutes
Conversations on Data Science•10 minutes
Data Science as Art•10 minutes
Epicycles of Analysis•10 minutes
Six Types of Questions•10 minutes
Characteristics of a Good Question•10 minutes
EDA Check List•10 minutes
Assessing a Distribution•10 minutes
Assessing Linear Relationships•10 minutes
Exploratory Data Analysis: When Do We Stop?•10 minutes
Factors Affecting the Quality of Inference•10 minutes
A Note on Populations•10 minutes
Inference vs. Prediction•10 minutes
Interpreting Your Results•10 minutes
Routine Communication•10 minutes
Post-Course Survey•10 minutes
7 assignments•Total 210 minutes
Data Analysis Iteration•30 minutes
Stating and Refining the Question•30 minutes
Exploratory Data Analysis•30 minutes
Inference•30 minutes
Formal Modeling, Inference vs. Prediction•30 minutes
Interpretation•30 minutes
Communication•30 minutes
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Learner reviews
4.6
3,382 reviews
5 stars
67.80%
4 stars
24.62%
3 stars
5.61%
2 stars
1.03%
1 star
0.91%
Showing 3 of 3382
M
MA
5·
Reviewed on Jul 20, 2017
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.
S
ST
5·
Reviewed on Nov 22, 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
J
JQ
5·
Reviewed on Apr 2, 2020
Muy bueno para entender como manejar el proceso de análisis con un equipo de analistas. Elaborar bien la pregunta que se quiere estudiar y clasificarla me pareció lo mas interesante. Gracias!
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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
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