The Applied Data Science & Machine Learning training course is designed to take a deeper look at data analysis techniques and presents opportunities for participants to practice with relevant data.
The course begins by introducing topics like data cleansing, performance, and operationalizing analytics. Next, participants will get more instruction about how Python can be used to analyze data so that they can apply techniques through exercises and hands-on labs. Throughout the course, participants will explore a company-specific dataset. The course concludes by building on the Introduction to Data Science & Machine Learning course and takes a deeper dive into Data Science, Data-Driven, and Visualization techniques.
Purpose
| Learn intermediate data analysis techniques and how programming languages can be used to further analyze data |
Audience
| Developers and developer teams looking to learn some powerful data analysis tools |
Role
| Data Engineer | Data Scientist | Software Developer |
Skill Level
| Intermediate |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 3 Days |
Related Technologies
| Python |
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Learning Objectives
- Describe advanced statistical techniques to support Machine Learning (ML) analytics and conduct multiple tests for confidence
- Describe the difference between good and bad data and implement data cleansing techniques
- Improve the results of your data analysis by implementing dimensionality reduction
- Explain multiple ML analysis techniques in detail and apply those techniques to your own data sets to develop qualitative intent
- Apply ML analysis concepts to new projects in order to make better use of data
