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Data Analysis and Visualization

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Data Analysis and Visualization

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

Instructors: Peter Baumgartner

20,870 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

172 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

172 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

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

Tools you'll learn

Minitab

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments¹

AI Graded see disclaimer

Taught in English

91% of learners achieved a positive career outcome

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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

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.

Module details

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 Visualization1 minute
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 Data3 minutes
Introduction to Visualization3 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

Objectives3 minutes
Variation Sources2 minutes
Control and Specification Limits5 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 minutes
Data Story7 minutes
Tableau, Excel, and Power BI7 minutes
Dan Gerena Discusses Data Visualization3 minutes
Insight Evaluation4 minutes
Testing and Re-Evaluation3 minutes
Dan Gerena Discusses Sustaining a Data-Driven Strategy4 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

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Instructors

Instructor ratings

(56 ratings)

Peter Baumgartner

The State University of New York

2 Courses37,006 learners

Offered by

University at Buffalo

The State University of New York

Learner reviews

5 stars
75%
4 stars
18.02%
3 stars
5.81%
2 stars
0.58%
1 star
0.58%

Showing 3 of 172

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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