1. Basics and Fundamentals
1.1. Mathematics
1.1.1. Linear Algebra
1.1.2. Calculus
1.1.3. Probability
1.1.4. Discrete Mathematics
1.2. Statistics
1.2.1. Descriptive Statistics
1.2.2. Inferential Statistics
1.3. Programming
1.3.1. Python (Variables, Data Structures, Functions)
1.3.2. R (Optional)
1.3.3. SQL (Database Queries)
2. Data Analysis and Visualization
2.1. Data Analysis
2.1.1. Libraries: NumPy, Pandas
2.1.2. Data Manipulation: Cleaning, Transformation, Aggregation
2.2. Data Visualization
2.2.1. Libraries: Matplotlib, Seaborn, Plotly
2.2.2. BI Tools: Tableau, Power BI
3. Advanced Statistics and Data Wrangling
3.1. Probability Distributions
3.1.1. Normal, Binomial, Poisson Distributions
3.2. Hypothesis Testing
3.2.1. T-tests, Chi-Square, ANOVA
3.3. Data Cleaning
3.3.1. Handling Missing Values
3.3.2. Outliers, Normalization Techniques
4. Machine Learning
4.1. Supervised Learning
4.1.1. Regression (Linear, Logistic)
4.1.2. Classification (Decision Trees, Random Forest, SVM, Naive Bayes)
4.2. Unsupervised Learning
4.2.1. Clustering (K-means, Hierarchical, DBSCAN)
4.2.2. Dimensionality Reduction (PCA, t-SNE)
4.3. Model Evaluation
4.3.1. Confusion Matrix, Precision, Recall, F1-score, ROC-AUC
4.4. Libraries
4.4.1. Scikit-Learn
4.4.2. TensorFlow, PyTorch (Deep Learning)
5. Deep Learning
5.1. Neural Networks
5.1.1. Artificial Neural Networks (ANN)
5.1.2. Activation Functions, Backpropagation
5.2. Convolutional Neural Networks (CNNs)
5.2.1. Image Classification
5.3. Recurrent Neural Networks (RNNs)
5.3.1. Sequence and Time-Series Data
5.4. Natural Language Processing (NLP)
5.4.1. Tokenization, Sentiment Analysis, Word Embeddings (Word2Vec, GloVe)
6. Big Data and Data Engineering
6.1. Big Data Tools
6.1.1. Hadoop, Apache Spark
6.2. NoSQL Databases
6.2.1. MongoDB, Cassandra
6.3. Cloud Services
6.3.1. AWS, Google Cloud, Microsoft Azure
7. Model Deployment
7.1. APIs
7.1.1. Flask, FastAPI
7.2. Containerization
7.2.1. Docker
7.3. Cloud Platforms
7.3.1. AWS, GCP, Azure
8. Data Science Projects
8.1. Data Analysis
8.1.1. Cleaning, Visualization
8.2. Machine Learning Projects
8.2.1. Predictive Modeling, Classification
8.3. NLP Projects
8.3.1. Chatbot, Sentiment Analysis
8.4. Deep Learning Projects
8.4.1. Image Classification with CNN
9. Resources and Learning Platforms
9.1. Courses
9.1.1. Coursera, Udacity, DataCamp
9.2. Books
9.2.1. "Python for Data Analysis"
9.2.2. "Hands-On Machine Learning"
9.3. Kaggle Competitions
10. Advanced Topics
10.1. Time Series Analysis
10.1.1. ARIMA, LSTM
10.2. Reinforcement Learning
10.3. Model Optimization
10.3.1. Hyperparameter Tuning, Grid Search