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.
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Data Analysis and Visualization
This course is part of Data-Driven Decision Making (DDDM) Specialization
Instructors: Peter Baumgartner
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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
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There are 4 modules in this course
This module provides an overview of the tools needed for data analysis.
What's included
6 videos6 readings1 assignment
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
This module provides tools for leveraging data through visualization and translation.
What's included
7 videos5 readings1 assignment
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
Recommended if you're interested in Business Strategy
Fundação Instituto de Administração
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