1. Comprehend the mechanics of machine learning and multiple techniques such as pattern recognition or statistical hypothesis testing.
2. Apply the data requirements for regressions, classification, and clustering machine learning activities.
3. Implement data cleansing, normalization, and standardization techniques.
4. Evaluate model accuracy and implement improvements.
5. Apply advanced modeling techniques to a variety of business activities.
Class Papers and Projects
- SAS script analyzing visit patterns by day and month
- SAS script analyzing visit frequency to a shopping site
- SAS script performing statistical analysis of items in cart
- SAS script analyzing Year-To-Date (YTD) online revenue
- SAS script performing statistical analysis of purchase basket values
- SAS script analyzing customer membership frequency
- SAS script performing exploratory data analysis (EDA)
- SAS script importing Excel dataset for regression analysis
- Document discussing clustering models and techniques
- Document discussing clustering models and techniques
- Document analyzing data visualization outputs for final project
- Document discussing clustering models and techniques
- Python script scraping NHL statistics within a specified date range
- Jupyter Notebook for scraping NHL game statistics