Data Analysis and Visualization

  • 4.3
Approx. 11 hours to complete

Description

By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.

Knowledge

  • Identify stakeholders and key components imperative to an analytics project plan
  • Name strengths and weaknesses of different analysis and visualization tools
  • Visually identify, monitor, and remove process variation
  • Explain how to create a compelling data story

Outline

  • Data Analysis Software Tools
  • Introduction to Data Analysis and Visualization
  • Context, Objectives, and Analysis Plans
  • Excel, R, MINITAB, MATLAB, and Python
  • Techniques and Best Practices
  • Dan Gerena Discusses Maximizing the Value of Data
  • Introduction to Visualization
  • Welcome Message and Course Overview
  • Acknowledgements
  • Context, Objectives, and Analysis Plans Resources (Optional)
  • Data Analysis Tools Resources (Optional)
  • Techniques and Best Practices Resources (Optional)
  • Introduction to Visualization Resources (Optional)
  • Data Analysis Software Tools
  • Statistical Process Control (SPC)
  • Objectives
  • Variation Sources
  • Control and Specification Limits
  • Variation Analysis
  • Process Performance
  • Objectives (Optional)
  • Variation Sources (Optional)
  • Control and Specification Limits (Optional)
  • Variation Analysis (Optional)
  • Process Performance (Optional)
  • Statistical Process Control (SPC)
  • Data Visualization and Translation
  • Guiding Principles
  • Data Story
  • Tableau, Excel, and Power BI
  • Dan Gerena Discusses Data Visualization
  • Insight Evaluation
  • Testing and Re-Evaluation
  • Dan Gerena Discusses Sustaining a Data-Driven Strategy
  • Guiding Principles Resources (Optional)
  • Data Story Resources (Optional)
  • Visualization Tools Resources (Optional)
  • Insight Evaluation Resources (Optional)
  • Testing and Re-Evaluation Resources (Optional)
  • Data Visualization and Translation
  • Project: Data Analysis and Visualization
  • Project: Data Analysis and Visualization
  • Project: Data Analysis and Visualization (REQUIRED)

Summary of User Reviews

Learn how to analyze and visualize data with this highly rated course on Coursera. Users rave about the course's comprehensive approach to data analysis and its practical applications.

Key Aspect Users Liked About This Course

The course's practical approach to data analysis was highly praised by many users.

Pros from User Reviews

  • Comprehensive coverage of data analysis techniques
  • Hands-on exercises and practical applications
  • Great for beginners and those with some experience in data analysis

Cons from User Reviews

  • Some users found the course content to be too basic
  • Course materials may be outdated
  • Limited interaction with instructors and other students
English
Available now
Approx. 11 hours to complete
Peter Baumgartner, Brittany O'Dea
University at Buffalo, The State University of New York
Coursera

Instructor

Peter Baumgartner

  • 4.3 Raiting
Share
Saved Course list
Cancel
Get Course Update
Computer Courses