🧭 1. Foundations of Data Science

a. Introduction to Data Science

• What is data science?

• Data science workflow (data collection → cleaning → analysis → modeling → visualization → deployment).

• Roles: data analyst, data engineer, machine learning engineer, data scientist.

b. Mathematics & Statistics

• Descriptive statistics (mean, median, variance, standard deviation).

• Probability theory & distributions (normal, binomial, Poisson, etc.).

• Inferential statistics (hypothesis testing, p-values, confidence intervals).

• Linear algebra (vectors, matrices, dot product, eigenvalues).

• Calculus basics (derivatives, gradients for optimization).

c. Programming for Data Science

• Languages: Python (primary), R (optional).

• Key Python libraries:

  • NumPy, Pandas → data manipulation

  • Matplotlib, Seaborn, Plotly → visualization

  • Scikit-learn → machine learning

  • Statsmodels → statistical analysis

💾 2. Data Handling & Preparation

a. Data Collection

• APIs, web scraping, databases (SQL, NoSQL).

• Data formats: CSV, JSON, Excel, Parquet.

b. Data Cleaning

• Handling missing values.

• Dealing with duplicates and outliers.

• Feature scaling, encoding categorical data.

• Data transformation and normalization.

c. Exploratory Data Analysis (EDA)

• Summary statistics and visualizations.

• Correlation and covariance analysis.

• Identifying data patterns and anomalies.

🤖 3. Machine Learning Fundamentals

a. Supervised Learning

• Regression: Linear, Logistic, Decision Trees, Random Forest, XGBoost.

• Classification: k-NN, SVM, Naive Bayes, Neural Networks.

b. Unsupervised Learning

• Clustering: K-means, Hierarchical, DBSCAN.

• Dimensionality reduction: PCA, t-SNE.

c. Model Evaluation

• Train-test split, cross-validation.

• Metrics: accuracy, precision, recall, F1-score, ROC-AUC, RMSE.

• Avoiding overfitting (regularization, dropout, etc.).

🧠 4. Advanced Topics

• Deep Learning: Neural networks, CNNs, RNNs, Transformers.

• Natural Language Processing (NLP): Text cleaning, embeddings, sentiment analysis.

• Time Series Analysis: Forecasting, ARIMA, LSTM.

• Big Data Tools: Spark, Hadoop.

• MLOps: Model deployment, monitoring, versioning.

📊 5. Data Visualization & Communication

• Dashboarding tools: Tableau, Power BI, Plotly Dash.

• Storytelling with data — turning insights into business decisions.

• Communicating results to non-technical audiences.

🧰 6. Tools and Platforms

• Development environments: Jupyter Notebook, Google Colab, VS Code.

• Version control: Git/GitHub.

• Cloud platforms: AWS, Azure, Google Cloud.

🎯 7. Capstone Projects & Real-World Practice

• End-to-end project: data collection → model building → deployment.

• Domain-specific projects: finance, healthcare, e-commerce, etc.