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.
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Executive Data Science SpecializationJohns Hopkins UniversityAbout this Course
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Learner Career Outcomes
29%
started a new career after completing these courses
30%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 3 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 6 hours to complete
English
Subtitles: English, Japanese
What you will 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
Skills you will gain
Data AnalysisCommunicationInterpretationExploratory Data Analysis
Learner Career Outcomes
29%
started a new career after completing these courses
30%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 6 hours to complete
English
Subtitles: English, Japanese
Offered by
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.
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!
6 hours to complete
19 videos (Total 144 min), 17 readings, 7 quizzes
19 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
17 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
7 practice exercises
Data Analysis Iteration10m
Stating and Refining the Question16m
Exploratory Data Analysis10m
Inference10m
Formal Modeling, Inference vs. Prediction10m
Interpretation10m
Communication10m
Reviews
TOP REVIEWS FROM MANAGING DATA ANALYSIS
by BCDec 30, 2019
Like other reviewers said, this course is larger than the two previous courses. The content is excellent but I am giving 4/5 stars because I found many misspelled words throughout the course lectures.
by ELFeb 28, 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 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
by KLApr 16, 2018
Great course for those who want a comprehensive overview of an analysis from A-Z. The professor explains each aspect in perfect detail, gives easy to understand examples, very comprehensive.
About the Executive Data Science Specialization
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
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