INTRODUCTION TO DATA SCIENCE AND PYTHON PROGRAMMING

Introduction to Data Science

• Understanding the role of data in decision-making, overview of Data Science process
• Applications of Data Science in various industries, ethics in data collection and analysis

Python Fundamentals for Data Science

• Introduction to Python programming language, variables, data types, and basic operations
• Control structures (if, else, loops), functions, and libraries for Data Science (NumPy, Pandas)

DATA MANIPULATION AND VISUALIZATION

Data Manipulation with Pandas

• Data ingestion, cleaning, and manipulation with Pandas Data Frames
• Data aggregation, merging, and reshaping for analysis

Data Visualization with Matplotlib and Seaborn

• Introduction to data visualization principles, plotting with Matplotlib
• Advanced plotting techniques, customization, and visualization best practices using Seaborn

STATISTICAL ANALYSIS AND EXPLORATORY DATA ANALYSIS (EDA)

STATISTICAL ANALYSIS

• Descriptive statistics, probability distributions, and hypothesis testing
• Inferential statistics, correlation, and regression analysis

Exploratory Data Analysis (EDA)

• Data summarization, visualization, and interpretation
• Advanced EDA techniques: feature engineering, dimensionality reduction, and outlier detection

MACHINE LEARNING BASICS

Introduction to Machine Learning

• Understanding supervised and unsupervised learning, model evaluation metrics
• Overview of scikit-learn library, building and evaluating machine learning models

Regression Models

• Linear regression, polynomial regression, and regularization techniques
• Evaluation of regression models, model selection, and hyperparameter tuning

CLASSIFICATION MODELS

CLASSIFICATION ALGORITHMS

• Logistic regression, k-Nearest Neighbors (k-NN), and decision tree classifiers
• Ensemble methods: Random Forests, Gradient Boosting, and model evaluation for classification

SUPPORT VECTOR MACHINES (SVM) AND NAIVE BAYES

• Understanding SVM, kernel methods, and model optimization
• Naive Bayes classifiers, text classification, and real-world applications

UNSUPERVISED LEARNING AND MODEL EVALUATION

CLUSTERING ALGORITHMS

• K-means clustering, hierarchical clustering, and evaluation metrics
• Density-based clustering (DBSCAN), clustering applications, and practical examples

DIMENSIONALITY REDUCTION TECHNIQUES

• Principal Component Analysis (PCA), Singular Value Decomposition (SVD)
• t-Distributed Stochastic Neighbor Embedding (t-SNE), manifold learning, and visualization techniques

ADVANCED TOPICS IN DATA SCIENCE

LANGUAGE PROCESSING (NLP)

• Text preprocessing, feature extraction, and sentiment analysis
• Topic modeling, word embeddings, and advanced NLP techniques

TIME SERIES ANALYSIS

• Introduction to time series data, visualization, and trend analysis
• ARIMA modeling, seasonality, forecasting, and model evaluation

DEEP LEARNING FUNDAMENTALS

INTRODUCTION TO NEURAL NETWORKS

• Basics of artificial neural networks (ANN), activation functions, and forward propagation
• Training neural networks, backpropagation algorithm, and optimization techniques

DEEP LEARNING WITH TENSORFLOW

• Introduction to deep learning frameworks, building neural networks with TensorFlow
• Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), and advanced architectures

CAPSTONE PROJECT AND SPECIALIZATION

• Capstone Project
• Students work on a substantial data science project from problem formulation to implementation, under the guidance of mentors
• Presentation of capstone projects, peer review, and feedback sessions
• Specialization (Optional)
• Advanced topics based on student interest (e.g., reinforcement learning, computer vision)
• Final review, exam preparation, and career guidance sessions