MODULE-1
■ Introduction to Data Science
■ What is Data Science?
■ Importance and applications of Data Science
■ Data Science vs. Data Analytics vs. Machine Learning

MODULE-2
■ Python for Data Science
■ Introduction to Python
■ Python libraries: NumPy, Pandas, Matplotlib
■ Basic data manipulation and visualization

MODULE-3
■ Statistics and Probability for Data Science
■ Descriptive statistics
■ Probability theory
■ Hypothesis testing
■ Data distributions

MODULE-4
■ Data Cleaning and Preprocessing
■ Handling missing data
Data normalization and standardization
■ Feature engineering
Data transformation techniques

MODULE-5
■ Exploratory Data Analysis (EDA)
■ Understanding the dataset
■ Data visualization techniques
■ Correlation analysis
■ Outlier detection

MODULE-5
■ Introduction to Machine Learning
■ Supervised vs. Unsupervised learning
■ Linear regression
■ Classification algorithms (e.g., Decision Trees, KNN)
■ Model evaluation metrics