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Course
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Data-driven Problem Solving for Data Analysts
Turn business data into useful solutions by learning the complete data analysis process. By the end, you'll know how to analyze real business problems and present data-backed recommendations.
What you'll learn
Data analysts must handle complex business problems and translate raw data into actionable insights, leveraging multiple tools to implement the complete lifecycle of a data analysis project.
In this course, Data-driven Problem Solving for Data Analysts, you'll gain the ability to understand the typical workflow of a data analysis project, from defining problems to presenting it to stakeholders, using tools like Microsoft Excel, Python, and Tableau along the way.
First, you'll explore how to define data-driven business problems and the process of data exploration.
Next, you'll discover the process of cleaning and preparing data, and identifying key trends.
Finally, you’ll learn how the data analysis process can culminate in creating compelling visualizations and presenting your findings effectively to stakeholders.
When you're finished with this course, you'll have the skills and knowledge of how the end-to-end data analysis workflow is applied to tackle real-world business problems and deliver actionable recommendations backed by data.
Table of contents
About the author
Ria Cheruvu is an AI SW Lead Architect and Generative AI Evangelist at Intel Corporation. She has a master's degree in data science from Harvard University, and is an instructor of data science curricula, having previously taught for Harvard University, Eduonix, Udacity, and Educative. Ria is a passionate and renowned industry speaker having delivered technical talks, podcasts, and keynotes on AI, including DEFCON, TedX, Women in Data Science communities, the QS EduData Summit, and Intel Innovation. She is a published poet, children's book author, and neuroscience enthusiast.
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