Data Analysis for Decision-Making Professional Certificate

Demand for data analysis skills are projected to grow in the U.S. 21% over the next 10 years, over four times the rate of the overall labor market. Fields like Data Science, Data Analytics, and Statistics are expected to grow up to 34%. According to the World Economic Forum, emerging global demand for data analytics skills across occupations is contributing to a “race for talent,” with more jobs available than qualified candidates. According to Burning Glass Technologies research, hybrid, more complex roles which combine field-centric skills with data analysis competencies are up to 40% higher paying than their single-focus counterparts, are high-growth, and immune to the threat of automation.
This Professional Certificate program prepares students, working professionals, and decision makers to become data literate in both their professional and personal lives. In today’s data-driven world, data literacy will transform you into a “data citizen,” allowing you to communicate and make decisions based upon facts with confidence. You will emerge as a champion for a data literate culture.
You will gain an understanding of how using data visualization, big data, data collection, and analytical tools to better understand business challenges and inform the decision-making process.
This program is valuable for students and professionals who want to go beyond data analysis software proficiency to develop the ability to read, write, and communicate using data in context, including an understanding of data sources and constructs.
What you will learn:
- How to apply critical thinking skills when working with data
- How to use Microsoft Excel to organize and clean data and create a data analysis plan
- Use Tableau to create meaningful tables and graphical representations to organize and visually present data
- Identify key take away points from data analysis and integrate them with other important information
- Critically assess graphical representations for accuracy and misrepresentation of data