In this course, you will implement data science techniques in order to address business issues.
- Use data science principles to address business issues.
- Apply the extract, transform, and load (ETL) process to prepare datasets.
- Use multiple techniques to analyze data and extract valuable insights.
- Design a machine learning approach to address business issues.
- Train, tune, and evaluate classification models.
- Train, tune, and evaluate regression and forecasting models.
- Train, tune, and evaluate clustering models.
- Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.
Who Should Attend
To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
- Initiate a Data Science Project
- Formulate a Data Science Problem
- Extract Data
- Transform Data
- Load Data
- Examine Data
- Explore the Underlying Distribution of Data
- Use Visualizations to Analyze Data
- Preprocess Data
- Identify Machine Learning Concepts
- Test a Hypothesis
- Train and Tune Classification Models
- Evaluate Classification Models
- Train and Tune Regression Models
- Evaluate Regression Models
- Train and Tune Clustering Models
- Evaluate Clustering Models
- Communicate Results to Stakeholders
- Demonstrate Models in a Web App
- Implement and Test Production Pipelines