FOUNDATION IN DATA SCIENCE

Introduction To Data Science

• Overview of Data Science, its importance, and applications
• Basics of Python programming for Data Science (variables, data types, control structures)

Data Manipulation and Visualization

• Introduction to Pandas for data manipulation
• Data visualization using Matplotlib and Seaborn

Data Cleaning and Preprocessing

• Handling missing data and outliers
• Feature scaling, encoding categorical variables, and data normalization

ADVANCED DATA ANALYSIS

Exploratory Data Analysis (EDA)

• Understanding distributions, correlations, and summary statistics
• Advanced EDA techniques, including heatmap, pair plots, and dimensionality reduction

Stastical analysis

• Hypothesis testing and statistical significance
• Regression analysis and correlation testing

Introduction To Machine Learning

• Overview of supervised and unsupervised learning
• Introduction to scikit-learn and its modules for machine learning

SUPERVISED LEARNING

Regression Models

• Linear regression
• Polynomial regression and regularization techniques

Classification Models

• Logistic regression
• Decision trees and ensemble methods (Random Forests)

Evaluation Metrics and Model Optimization

• Performance metrics for classification and regression
• Hyperparameter tuning and model selection techniques

UNSUPERVISED LEARNING

Clustering

• K-means clustering
• Hierarchical clustering and DBSCAN

Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE) and its applications

ADVANCED TOPICS IN DATA SCIENCE

Natural Language Processing (NLP)

• Basics of text processing and tokenization
• Sentiment analysis and text classification using NLP techniques

Time Series Analysis

• Introduction to time series data and its characteristics
• ARIMA modeling and forecasting techniques

CAPSTONE PROJECT AND SPECIALIZATION

• Capstone Project
• Students work on a real-world data science project under the guidance of mentors
• Presentation of capstone projects and peer review
• Specialization (Optional)
• Deep learning fundamentals using TensorFlow
• Advanced topics based on student interest (e.g., reinforcement learning, computer vision)