Managing Data Analysis
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Managing Data Analysis
This course is part of Executive Data Science Specialization
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3,382 reviews
3,382 reviews
What you'll learn
Differentiate between various types of data pulls
Describe the basic data analysis iteration
Explore datasets to determine if data is appropriate for a project
Use statistical findings to create convincing data analysis presentations
Details to know
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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
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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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
Reviewed on Feb 7, 2020
Excellent course. Really enjoyed the instructor. Finally getting into some analysis. Made me want to refresh my stat skills and get working on a challenging project!
Reviewed on Sep 3, 2017
One of the best courses I've taken. The instructor presented a clear approach and variety of suggestions for improving the consistency and quality of data science projects. Very useful!
Frequently asked questions
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