BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!
Our 2-day, instructor-led Data Science for Leaders: Building a Data Driven Strategy Training course teaches leaders and executives how to identify opportunities to make better decisions by utilizing new insights from data. At the conclusion of this course, students will be able to:
- Choose the correct tools, techniques and approaches for data science projects
- Avoid pitfalls when drawing conclusions from data
- Create a strategy for building a data science team
While no prerequisites are required, people with some experience working with data will benefit the most from this course
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 301-258-8200.
Data Science User Cases & Frameworks
- What is data science and how is it related to Big Data?
- Data Science Use Cases and Success Stories
- Which frameworks can you use to organize the workflow of a data science project and direct your team?
- Which approaches can you use to find insights using data?
Data Science Tools and Technologies — Reference Architecture
- Which steps are required in the data pipeline from ingestion to analysis?
- Which technologies are available for working with data at various stages of the data pipeline?
- How do different tools and technologies for working with data compare in their functionality, strengths and weaknesses?
Building and Managing a Data Science Team
- Which skills does you data science team need for a robust capability?
- What to look for when hiring data scientists and what to avoid
- How to structure your data science team for maximum input
- Should you buy or build a data science capability and how much can it cost?
Data Science Challenges — Getting it Right
- What are the pitfalls of analyzing data? How can you avoid statistical fallacies?
- Which assumptions do you need to be aware of when prosecuting data science projects?
- How to best visualize data and how to avoid optical illusions and other traps