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

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Assessments

3 assignments

Taught in English

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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.

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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'.

What's included

5 videos5 readings1 assignment

5 videosTotal 17 minutes

Objectives2 minutesPreview module
Variation Sources2 minutes
Control and Specification Limits4 minutes
Variation Analysis4 minutes
Process Performance3 minutes

5 readingsTotal 110 minutes

Objectives (Optional)10 minutes
Variation Sources (Optional)20 minutes
Control and Specification Limits (Optional)40 minutes
Variation Analysis (Optional)20 minutes
Process Performance (Optional)20 minutes

1 assignmentTotal 30 minutes

Statistical Process Control (SPC)30 minutes

This module provides tools for leveraging data through visualization and translation.

What's included

7 videos5 readings1 assignment

7 videosTotal 36 minutes

Guiding Principles8 minutesPreview module
Data Story6 minutes
Tableau, Excel, and Power BI6 minutes
Dan Gerena Discusses Data Visualization3 minutes
Insight Evaluation4 minutes
Testing and Re-Evaluation3 minutes
Dan Gerena Discusses Sustaining a Data-Driven Strategy3 minutes

5 readingsTotal 150 minutes

Guiding Principles Resources (Optional)30 minutes
Data Story Resources (Optional)30 minutes
Visualization Tools Resources (Optional)40 minutes
Insight Evaluation Resources (Optional)30 minutes
Testing and Re-Evaluation Resources (Optional)20 minutes

1 assignmentTotal 30 minutes

Data Visualization and Translation 30 minutes

This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.

What's included

1 video1 reading1 peer review1 discussion prompt

Instructors

Instructor ratings

4.6 (44 ratings)

Peter Baumgartner

The State University of New York

2 Courses30,191 learners

Offered by

University at Buffalo

The State University of New York

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Reviewed on Jun 12, 2022

Awesome feeling! I am grateful Coursera. Thank you enoumously.

Reviewed on Dec 15, 2020

Examples and better instructions on the assignment would improve this course.

Reviewed on Jul 11, 2022

A very educative course indeed. Thanks to Coursera and its multiple partners who made this possible. lam forever grateful for being afforded such an opportunity. Thanks to the course facilitators.

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