1. Introduction to Data Science:

• Overview of data science
• Importance and applications of data science
• Historical context and evolution of data science

2. Data Collection and Preprocessing:

• Data sources and types
• Data acquisition methods
• Data preprocessing techniques (cleaning, formatting, imputation, etc.)
• Exploratory Data Analysis (EDA)

3. Statistics and Probability:

• Descriptive statistics
• Inferential statistics
• Probability theory
• Statistical distributions

4. Data Visualization:

• Principles of data visualization
• Visualization tools and libraries (e.g., Matplotlib, Seaborn, ggplot2)
• Graph types and when to use them

5. Machine Learning Fundamentals:

• Supervised learning
• Unsupervised learning
• Model evaluation and validation
• Bias-variance tradeoff

6. Regression Analysis:

• Linear regression
• Polynomial regression
• Regularization techniques (L1, L2 regularization)
• Non-linear regression

7. Classification Algorithms:

• Logistic regression
• Decision trees
• Random forests
• Support Vector Machines (SVM)
• Naive Bayes

8. Clustering Techniques:

• K-means clustering
• Hierarchical clustering
• DBSCAN
• Evaluation metrics for clustering

9. Dimensionality Reduction:

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Feature selection and extraction

10. Natural Language Processing (NLP):

• Text preprocessing
• Tokenization
• Sentiment analysis
• Named Entity Recognition (NER)
• Topic modeling (e.g., LDA)

11. Deep Learning:

• Neural network architecture
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer learning
• Hyperparameter tuning

12. Big Data Technologies:

• Hadoop
• Spark
• MapReduce
• Distributed computing

13. Time Series Analysis:

• Time series components
• ARIMA models
• Seasonal decomposition
• Forecasting techniques

14. Model Deployment and Productionization:

• Model deployment strategies
• Containerization (e.g., Docker)
• Web services (e.g., Flask, Django)
• Monitoring and maintenance

15. Ethics and Privacy in Data Science:

• Data privacy regulations (e.g., GDPR)
• Bias and fairness in machine learning
• Responsible AI practices