Course Outline

• Introduction to Statistical Learning: Overview of statistical concepts, data types, and methods for data analysis and interpretation.
• Exploratory Data Analysis (EDA): Techniques for exploring and visualizing data to uncover patterns, trends, and relationships.
• Probability and Statistical Inference: Fundamentals of probability theory, hypothesis testing, and confidence intervals for statistical inference.
• Regression Analysis: Linear and nonlinear regression models for predicting and understanding the relationship between variables.
• Classification Methods: Supervised learning techniques including logistic regression, decision trees, and support vector machines for classification tasks.
• Clustering and Dimensionality Reduction: Unsupervised learning methods such as k-means clustering and principal component analysis (PCA) for data segmentation and dimensionality reduction.
• Resampling Methods: Bootstrapping and cross-validation techniques for estimating model performance and assessing model stability.
• Model Selection and Validation: Strategies for selecting and validating statistical models, including regularization methods and model assessment criteria.
• Time Series Analysis: Methods for analyzing time-series data, including trend analysis, seasonality detection, and forecasting techniques.
• Machine Learning Pipelines: Building end-to-end machine learning pipelines for data preprocessing, model training, and evaluation.
• Big Data Analytics: Introduction to big data technologies and tools for scalable data analysis and machine learning on large datasets.
• Applications in Data Science: Case studies and real-world applications of statistical learning and data analysis in various domains such as finance, healthcare, and marketing.