Level: Beginner to Intermediate
Data science teams leverage data in many formats, with various levels of quality, and often in high volume. Visualization techniques help make sense of this sea of information at every stage of a project—from establishing a business goal to identifying source data to validating analytic models.
Communication with stakeholders is also a key part of the data science process, which requires calibrating visualizations for the skill level of the audience. In this course, you will learn foundational principles for visualizing data and communicating data-driven insights.
This is part of an optional Data Science Bootcamp. Learn more about the courses offered, or attend this individual course.
You Will Learn
- Best practices for applying visualization in each stage of a data science project
- Visual techniques for understanding the business challenges targeted by data science
- Exploratory data analysis (EDA) techniques that support problem framing and source selection
- Methods for visual interpretation of data science results
- How to communicate data science insights to technical and non-technical stakeholders
- How to monitor data science model value
This course is geared to technical and non-technical professionals getting started with data science, including:
- Business analysts
- Business stakeholders
- Data scientists
- Analytics practitioners
- Data engineers
- Analytics project leads
- BI and data management professionals
Experienced data scientists will find this course to be a review, but they will find it valuable if they have not been formally exposed to key principles and practices.