This is for college students who want to learn the most in-demand job requirements with decent or high pay
Here, the students will learn about the following
| Introduction to Data Science | What is Data Science? |
| Data Science workflow | |
| Types of data | |
| Applications of Data Science | |
| Python for Data Science (Review & Expansion) | NumPy: Array operations |
| Pandas: Data manipulation and analysis | |
| Data cleaning and preprocessing | |
| Data Visualization | Matplotlib: Basic plots and charts |
| Seaborn: Statistical data visualization | |
| Creating informative visualizations | |
| Statistical Analysis | Descriptive statistics |
| Probability and distributions | |
| Hypothesis testing | |
| Correlation and regression | |
| Machine Learning Fundamentals | Introduction to Machine Learning |
| Supervised vs. Unsupervised Learning | |
| Model evaluation metrics | |
| Supervised Learning: Regression | Linear Regression |
| Polynomial Regression | |
| Model evaluation (R-squared, MSE) | |
| Supervised Learning: Classification | Logistic Regression |
| K-Nearest Neighbors (KNN) | |
| Decision Trees | |
| Model evaluation (accuracy, precision, recall, F1-score) | |
| Unsupervised Learning | Clustering (K-Means) |
| Dimensionality reduction (PCA) | |
| Project/Case Study | Applying learned skills to a real-world dataset |
| Presenting findings and insights |
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