In this section, we define core data science terminology, explain the three domains of data science, and introduce basic Python syntax for data tasks.

In this section, we explore structured versus unstructured data, quantitative versus qualitative data, and the four levels of data for effective analysis and modeling.

In this section, we explore the five steps of data science, focusing on problem definition, data preprocessing with pandas, and effective data visualization and communication.

In this section, we explore fundamental mathematical concepts including symbols, logarithms, set theory, and matrix operations, essential for data science modeling and analysis.

In this section, we explore probability's core principles, compare frequentist and Bayesian approaches, and apply probability rules to model uncertain real-world events.

In this section, we examine advanced probability concepts like Bayes' theorem and random variables, focusing on their application in predictive modeling and decision-making processes.

In this section, we explore unbiased data sampling, measures of center and variation, z-scores, and the empirical rule to analyze and interpret data effectively.

In this section, we explore hypothesis testing, confidence intervals, and the central limit theorem to make population inferences from sample data. Key concepts include point estimates and sampling distributions for data-driven decision-making.

In this section, we explore methods for communicating data effectively, focusing on identifying misleading visualizations, understanding correlation versus causation, and creating clear, insightful visuals for diverse audiences.

In this section, we explore machine learning fundamentals, including regression, classification, and model evaluation.

In this section, we explore naive Bayes, decision trees, and PCA for real data analysis and prediction.

In this section, we explore transfer learning and pre-trained models, focusing on their application in ML tasks. Key concepts include BERT, GPT, and adapting models for computer vision and NLP.

In this section, we explore algorithmic bias mitigation, model and data drift handling, and strategies for building fair and robust machine learning systems.

In this section, we explore structured approaches to data, ML, and architectural governance to drive digital transformation, ensure compliance, and unlock value through effective management and control.

In this section, we analyze the COMPAS dataset for bias detection and implement text embeddings using OpenAI models. We focus on feature standardization, encoding, and practical data science applications.