Data Science & AI for Marketing & Operations
This course offers a fully self-paced learning experience through pre-recorded video lectures that can be accessed and replayed indefinitely, with no live online sessions required. Enrollees receive free access to the Python environment setup, downloadable Excel exercise files, and complete Python scripts for every practical project. The program also includes one attempt at the final course assessment exam upon completion of the video modules.
Course syllabus
- 01
Manage mall customers using Hierarchical clustering, Visualize clusters with a dendrogram, and create feature pairplots. Use age, annual income, and spending scores to infer customer groups and customize marketing strategies in Python.
- 02
Predict e-tailer sales revenue using XGBoost, CatBoost, HGBoost, and NGBoost. Compare the boosting methods using error metrics, select the best-performing model, and use it to forecast revenue.
- 03
Analyze seasonal revenue distribution for e-tailers using the Prophet model. Study marketing expenditure and forecast weekly and yearly revenue to identify the days and periods with the strongest revenue performance.
- 04
Identify good and bad credit using K-means clustering on a dataset of 1,001 rows and 10 columns. Apply the Elbow method as a preprocessing step and use clustering to understand credit quality patterns.
- 05
Measure hospital efficiency using Data Envelopment Analysis. Evaluate Physician, Nurse, and Active Bed inputs against Bed Occupancy Rates and Inpatient Admissions to judge resource utilization and efficiency.