| S.No |
Topic |
Sub-Topics |
| 1 | Data Science Overview | Definition, Lifecycle, Use Cases, Roles, Skills Required |
| 2 | Mathematics for Data Science | Linear Algebra Basics, Probability, Statistics, Calculus Overview, Optimization |
| 3 | Python for Data Science | Python Basics, NumPy, Pandas, Matplotlib, Jupyter Notebooks |
| 4 | Data Collection | Data Sources, APIs, Web Scraping, Databases, Data Warehouses |
| 5 | Data Cleaning | Missing Values, Outlier Detection, Data Imputation, Data Normalization, Deduplication |
| 6 | Exploratory Data Analysis | Summary Statistics, Data Visualization, Correlation Analysis, Distribution Analysis, Insights |
| 7 | Data Visualization | Matplotlib, Seaborn, Plotly, Dashboards, Storytelling |
| 8 | Statistics for Data Science | Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Confidence Intervals, A/B Testing |
| 9 | Feature Engineering | Feature Creation, Feature Scaling, Encoding Categorical Data, Feature Selection, Dimensionality Reduction |
| 10 | Machine Learning Basics | Supervised Learning, Unsupervised Learning, Model Evaluation, Bias-Variance Tradeoff, Pipelines |
| 11 | Supervised Learning Algorithms | Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting |
| 12 | Unsupervised Learning Algorithms | K-Means, Hierarchical Clustering, DBSCAN, PCA, Association Rules |
| 13 | Model Training and Evaluation | Train-Test Split, Cross Validation, Metrics, Overfitting, Underfitting |
| 14 | Model Tuning | Hyperparameter Tuning, Grid Search, Random Search, Bayesian Optimization, Regularization |
| 15 | Time Series Analysis | Time Series Components, ARIMA, Seasonality, Forecasting, Evaluation |
| 16 | Natural Language Processing | Text Preprocessing, Tokenization, Vectorization, Topic Modeling, Sentiment Analysis |
| 17 | Deep Learning Basics | Neural Networks, Activation Functions, Backpropagation, Optimization Algorithms, Frameworks |
| 18 | Deep Learning Models | CNNs, RNNs, LSTMs, Transformers, Use Cases |
| 19 | Big Data for Data Science | Hadoop, Spark, Distributed Computing, Data Lakes, Scalability |
| 20 | Model Deployment | Model Serialization, REST APIs, Batch vs Real-time Inference, Monitoring, Scaling |
| 21 | MLOps Fundamentals | Version Control, CI/CD, Experiment Tracking, Model Registry, Automation |
| 22 | Model Monitoring | Data Drift, Concept Drift, Performance Metrics, Alerts, Retraining |
| 23 | Ethics and Responsible AI | Bias, Fairness, Explainability, Transparency, Privacy |
| 24 | Data Storytelling | Business Context, Visual Narratives, Communication, Dashboards, Stakeholder Presentation |
| 25 | Domain Knowledge | Business Understanding, KPIs, Industry Use Cases, Problem Framing, Decision Making |
| 26 | Cloud for Data Science | AWS, Azure, GCP, Managed ML Services, Cost Management |
| 27 | Advanced Analytics | Recommendation Systems, Anomaly Detection, Graph Analytics, Causal Inference, Optimization |
| 28 | Data Science Tools | Scikit-learn, TensorFlow, PyTorch, MLflow, DVC |
| 29 | Best Practices | Reproducibility, Documentation, Code Quality, Collaboration, Experiment Management |
| 30 | End-to-End Data Science Project | Problem Definition, Data Preparation, Modeling, Evaluation, Deployment |
