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
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!
Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be easy.
Managing Data Analysis is the third course in “Executive Data Science” specialization offered by John Hopkins University on Coursera. The one-week course discusses the process of data analysis at a high level from formulating questions to exploratory...
Managing Data Analysis is the third course in “Executive Data Science” specialization offered by John Hopkins University on Coursera. The one-week course discusses the process of data analysis at a high level from formulating questions to exploratory analysis, inference, modeling and communicating results. Grading is based on several short comprehension quizzes.
The lectures in Managing Data Analysis are of good quality and the instructor is generally easy to understand. The lectures do, however, use some jargon and concepts that aren’t always adequately explained. Unlike the first two courses of the specialization, which are geared toward managers, this course is more geared toward people who are actually going to be conducting data analysis. The concepts in this course are definitely important for data science managers to understand, but non-technical students may find this to be a jarring change of pace. In addition, certain parts may be confusing if you have had no prior exposure to statistics or machine learning other than the first two courses of this specialization.
Managing Data Analysis provides a useful overview of the process of data analysis, but it is taught at a level appropriate for data analysts. “The Data Analysis Process” would be a more appropriate name for this course.
I give Managing Data Analysis 3.5 out of 5 stars: Good.
I don't understand bad reviews. This course is not technical. It's focused on methodology and correct approach to data analysis. I know a lot of people, who knows the tools and packages, but don't know how to ask correct questions, how to take value from the data and how to avoid common pitfalls of data science. I believe this course is really valuable for everyone working with data.
The course is based on "The Art of Data Science" book, which I also recommend.