1. Introduction to Python for Data Science

• Python Basics: Variables, data types, basic operators, and control flow (if, for, while).
• Python Libraries for Data Science:
• NumPy: For numerical operations and handling arrays.
• Pandas: For data manipulation and analysis (Series, DataFrames).
• Matplotlib and Seaborn: For data visualization.

2. Data Wrangling and Preprocessing

• Data Cleaning: Handling missing data, duplicates, and incorrect data types.
• Data Transformation: Normalization, standardization, and encoding categorical variables.
• Feature Engineering: Creating new features from existing data.
• Data Aggregation and Grouping: Using groupby in Pandas.

3. Exploratory Data Analysis (EDA)

• Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
• Data Visualization: Histograms, bar charts, scatter plots, box plots, and pair plots.
• Correlation Analysis: Understanding relationships between variables.

4. Statistical Analysis

• Probability Distributions: Normal distribution, binomial distribution, etc.
• Hypothesis Testing: t-tests, chi-square tests, p-values, and confidence intervals.
• ANOVA: Analysis of variance.

5. Machine Learning with Python

• Supervised Learning:
• Regression: Linear regression, multiple linear regression, and regularization techniques (Ridge, Lasso).
• Classification: Logistic regression, decision trees, random forests, k-nearest neighbors, and support vector machines (SVM).
• Unsupervised Learning:
• Clustering: K-means, hierarchical clustering, and DBSCAN.
• Dimensionality Reduction: Principal Component Analysis (PCA).
• Model Evaluation and Validation:
• Metrics: Accuracy, precision, recall, F1-score, ROC-AUC.
• Cross-Validation: k-fold cross-validation, leave-one-out cross-validation.

6. Advanced Topics

• Natural Language Processing (NLP): Text preprocessing, tokenization, TF-IDF, and basic sentiment analysis.
• Time Series Analysis: ARIMA models, seasonal decomposition, and forecasting.
• Deep Learning: Introduction to neural networks using TensorFlow or PyTorch.
• Recommender Systems: Collaborative filtering and content-based filtering.

7. Data Science Project Workflow

• Problem Definition and Data Collection: Understanding the problem, gathering relevant data.
• Data Preparation: Cleaning and transforming the data for analysis.
• Modeling: Selecting and training models.
• Model Evaluation: Assessing the performance of the model.
• Deployment: Using models in production environments.
• Communication: Presenting findings through reports, dashboards, or presentations.

8. Data Science Tools

• Jupyter Notebooks: For interactive data analysis.
• Git: Version control for data science projects.
• Docker: Containerization for reproducible environments.
• Cloud Platforms: AWS, Google Cloud, Azure for scalable data science.

9. Case Studies and Real-World Applications

• Predictive Modeling: Predicting sales, customer churn, etc.
• Classification Problems: Spam detection, image classification.
• Clustering: Customer segmentation, market basket analysis.
• NLP Projects: Sentiment analysis, chatbots.
• Time Series Projects: Stock price prediction, demand forecasting.

10. Ethics in Data Science

• Bias and Fairness: Understanding and mitigating bias in models.
• Privacy: Handling sensitive data responsibly.