Data Analysis and Visualization

Data Analysis and Visualization

This course is part of Data-Driven Decision Making (DDDM) Specialization

Instructors: Peter Baumgartner

What you'll learn

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

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

This course is part of the Data-Driven Decision Making (DDDM) Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

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.

Material features online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the second course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11.

This module provides an overview of the tools needed for data analysis.

What's included

6 videos6 readings1 assignment

6 videosTotal 24 minutes

Introduction to Data Analysis and Visualization0 minutesPreview module
Context, Objectives, and Analysis Plans4 minutes
Excel, R, MINITAB, MATLAB, and Python7 minutes
Techniques and Best Practices6 minutes
Dan Gerena Discusses Maximizing the Value of Data2 minutes
Introduction to Visualization2 minutes

6 readingsTotal 127 minutes

Welcome Message and Course Overview5 minutes
Acknowledgements2 minutes
Context, Objectives, and Analysis Plans Resources (Optional)40 minutes
Data Analysis Tools Resources (Optional)30 minutes
Techniques and Best Practices Resources (Optional)30 minutes
Introduction to Visualization Resources (Optional)20 minutes

1 assignmentTotal 30 minutes

Data Analysis Software Tools30 minutes

This module covers SPC, a way to analyze variation over time in your process using data. It is helpful in identifying current problems and can also be used to monitor the process for any deviations once the process is ‘in control'.