Data Science & AI in Human Resource
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
Introduction to Python, Python libraries, and visualization of variables using exploratory data analysis.
- 02
Applying Data Science and AI to employee management using a dataset of 4,654 rows and 9 columns. Topics include preprocessing, K-means clustering, the Elbow method, PCA cluster visualization, Gradient Boosting, Logistic Regression, Decision Tree, Random Forest, accuracy, ROC AUC, precision, recall, F1-score, model comparison, and identifying the factors affecting employee intention to leave.
- 03
Measuring employee satisfaction using Gradient Boosting, Logistic Regression, Decision Tree, Random Forest, SVM, and Neural Network models. Topics include identifying satisfaction factors, RFE, PFI, model comparison, and selecting the best model using a dataset of 501 rows and 14 columns.
- 04
Gauging employee turnover through survival analysis using Kaplan-Meier survival curves on a dataset of 1,130 rows and 19 columns. Compare survival rates across sectors and categories to identify higher and lower employee retention.
- 05
VAK sentiment analysis using a dataset of 15,541 rows and 15,450 words to identify the most popular words associated with Visual, Auditory, and Kinesthetic training methods.