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• Basic Statistical Concepts:
o Descriptive statistics (mean, median, mode, standard deviation)
o Probability distributions (normal, binomial, Poisson)
o Hypothesis 1 testing
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• Data Exploration and Visualization:
o Data cleaning and preprocessing
o Exploratory data analysis (EDA)
o Data visualization techniques (histograms, box plots, scatter plots
• Simple Linear Regression:
o Model assumptions and estimation
o Model interpretation and evaluation
• Multiple Linear Regression:
o Model specification and estimation
o Model selection and diagnostics
o Multicollinearity and its impact
• Hands-on: Building a Linear Regression Model
• Introduction to Logistic Regression:
o Binary logistic regression
o Model interpretation and evaluation
• Model Building and Evaluation:
o Model selection and regularization
o Confusion matrix, ROC curve, and AUC
• Hands-on: Building a Logistic Regression Model
• Model Evaluation Metrics:
o Mean squared error (MSE), root mean squared error (RMSE)
o Mean absolute error (MAE)
o R-squared and adjusted R-squared
• Model Validation and Cross-Validation:
o Train-test split
o K-fold cross-validation
• Model Improvement Techniques:
o Feature engineering and selection
o Regularization techniques (L1, L2 regularization)
• Time Series Analysis:
o Time series components (trend, seasonality, and noise)
o Time series forecasting models (ARIMA, exponential smoothing)
• Machine Learning Pipelines:
o Building end-to-end machine learning pipelines
o Model deployment and monitoring
• Case Studies:
o Real-world applications of regression models
o Best practices for data-driven decision-making