This training covers data science concepts, frameworks and tools that you and your staff need to use to make data-driven decisions and increase the efficiency of your organization. By the end of the program, students will be able to understand the landscape of data science tools and techniques, communicate effectively with data analysts and data scientists, and identify the dangerous pitfalls of using data incorrectly.
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.
Unlocking Your Potential Through Data Science Course Objectives:
- Understand the landscape of data science tools and techniques
- Effectively communicate with data analysts and data scientists
- Identify the dangerous pitfalls of using data incorrectly.
Identifying Data Science Use Cases & Developing Project Frameworks
- What is data science and how is it related to Big Data?
- How has data science been used successfully?
- Which frameworks can you use to organize the workflow of a data science project and direct your team?
- Which approaches should you use to derive insights from data?
Demystifying Data Science Techniques
- What are Unsupervised Machine Learning techniques?
- What are Supervised Machine Learning techniques?
- How do you know when to use these methods?
- What are advanced machine learning techniques and which questions can they answer?
Using the Right Data Science Tools
- What data science tools are available today?
- What are open-source tools?
- How do you know which tool you should use?
- When should you buy and when should you build?
Avoiding the Pitfalls of Data Science
- What are the pitfalls of analyzing data?
- How can you discern effective data visualizations?
- What are the legal and ethical implications of data science?
- What steps do you need to take to make data-driven decisions?
This course is ideal for professional who communicate with data scientists and analysts, work with Excel spreadsheets and other data sources, need to improve the effectiveness of their organization, want to develop a strong foundation in managing data science projects and resources to stand out in their career, and those who need to improve communication between domain experts and technical talent.