UNIT 1..........................................................................1
FOUNDATIONS OF DATA SCIENCE
1.1 Introduction to Data Science and its multidisciplinary nature
1.2 Evolution and applications in healthcare, finance, marketing, etc.
1.3 Data Science vs. Business Intelligence vs. Analytics
1.4 Data Science lifecycle: From problem definition to deployment
1.5 Overview of tools: Python, R, Jupiter Notebook, Anaconda, Git
1.6 Types of Data: Structured, Semi-Structured, Unstructured

UNIT 2..................................................................15
PYTHON FOR DATA HANDLING AND PREPROCESSING
2.1 Introduction to Python syntax and IDEs (Jupyter, Colab)
2.2 Data2 Types, Variables, Lists, Tuples, Dictionaries, Loops, Functions
2.3 NumPy for numerical data: arrays, slicing, broadcasting
2.4 Pandas: Series, Data Frames, indexing, merging, grouping
2.5 Data cleaning: Missing values, outliers, duplicates, inconsistent formats
2.6 Encoding categorical variables (Label Encoding, One-Hot)
2.7 Normalization & Standardization.

UNIT 3...........................................................................61
DATA VISUALIZATION AND EXPLORATORY DATA ANALYSIS (EDA)
3.1 Role of EDA in data science projects
3.2 Descriptive statistics for EDA: Summary statistics, correlation, skewness and kurtosis
3.3 Basic data visualization using Matplotlib (line, bar, scatter, histogram)
3.4 Statistical data visualization using Seaborn (pairplot, heatmap, boxplot)
3.5 Interactive data visualization using Plotly
3.6 Multivariate analysis and dimensionality reduction: basics concept and PCA - overview
3.7 Creating basic analytical dashboards using Google Sheets or Power BI
3.8 Data storytelling and communication.

LAB ON DATA SCIENCE.................................................105