Data Analysis and Artificial Intelligence Course

Lesson 1 • Descriptive Statistics Essentials

Introduces measures of central tendency, spread, and distribution shape. Equips students to summarize any dataset before deeper analysis.

Lesson 2 • Data Quality and Governance Basics

Defines accuracy, completeness, consistency, and timeliness as quality dimensions. Connects governance principles to trustworthy analytical outcomes.

Lesson 3 • The Data Analytics Lifecycle

Maps the end-to-end process from problem definition to insight delivery. Provides a repeatable framework students apply throughout the course.

Lesson 1 • Importing and Inspecting Raw Data

Covers loading data from files, databases, and APIs into an analytical environment. Sets the stage for systematic cleaning by establishing inspection habits.

Lesson 2 • Merging, Joining, and Reshaping Data

Demonstrates inner, outer, left, and right joins alongside pivot and melt operations. Enables students to integrate multi-source datasets seamlessly.

Lesson 3 • Handling Missing and Erroneous Values

Teaches detection, imputation, and removal strategies for incomplete or corrupt records. Directly improves model and analysis reliability.

Lesson 4 • Automating Data Pipelines

Introduces scripted, repeatable workflows that replace manual preparation steps. Prepares students for production-grade data engineering practices.

Lesson 1 • Relational Database Fundamentals

Explains tables, keys, relationships, and normalization as the basis for query design. Ensures students understand the data model before writing queries.

Lesson 2 • Window Functions and Analytical SQL

Introduces RANK, ROW_NUMBER, LAG, LEAD, and running totals for advanced analysis. Unlocks calculations impossible with standard GROUP BY alone.

Lesson 3 • Query Optimization and Best Practices

Addresses indexing, execution plans, and query refactoring to improve performance. Prepares students to work responsibly with large production databases.

Lesson 4 • Core SQL Query Syntax

Covers SELECT, FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses with practical examples. Forms the essential toolkit for all subsequent SQL work.

Lesson 1 • Choosing the Right Chart Type

Maps data types and analytical questions to appropriate chart families. Prevents common visualization mistakes that mislead audiences.

Lesson 2 • Interactive and Dashboard Visualization

Builds dynamic, filterable dashboards that allow stakeholders to explore data independently. Extends static charts into self-service analytical tools.

Lesson 3 • Storytelling with Data

Structures analytical findings into a coherent narrative with a clear call to action. Connects EDA outputs to business decision-making.

Lesson 1 • Regression Algorithms

Covers linear, polynomial, and regularized regression for continuous target prediction. Connects mathematical intuition to practical implementation.

Lesson 2 • Clustering and Dimensionality Reduction

Applies k-means, hierarchical clustering, and PCA to find structure in unlabeled data. Extends analytical capability beyond labeled datasets.

Lesson 3 • Core Machine Learning Concepts

Defines training, validation, testing, bias-variance tradeoff, and generalization. Creates the mental model required for all modeling decisions ahead.

Lesson 4 • Classification Algorithms

Introduces logistic regression, decision trees, and k-nearest neighbors for categorical targets. Builds skill in choosing and tuning classifiers.

Lesson 1 • Handling Imbalanced Datasets

Addresses class imbalance using resampling, cost-sensitive learning, and threshold adjustment. Critical for fraud, churn, and medical classification tasks.

Lesson 2 • Random Forests and Gradient Boosting

Implements and tunes random forests, XGBoost, and LightGBM for tabular data tasks. Covers the most widely used algorithms in applied data science.

Lesson 3 • Advanced Feature Engineering

Creates interaction terms, target encoding, and time-based features to boost model accuracy. Demonstrates that feature quality often matters more than algorithm choice.

Lesson 4 • Model Interpretability and Explainability

Applies SHAP values and partial dependence plots to explain complex model predictions. Builds stakeholder trust and supports regulatory compliance.

Lesson 1 • Recurrent Networks and Sequence Modeling

Applies RNNs, LSTMs, and GRUs to time-series and text sequence tasks. Extends deep learning to temporal and sequential data structures.

Lesson 2 • Practical Deep Learning Workflow

Integrates experiment tracking, early stopping, and model checkpointing into a reproducible workflow. Prepares students for production-level deep learning projects.

Lesson 3 • Training Neural Networks

Covers backpropagation, gradient descent variants, and regularization techniques for stable training. Directly addresses the most common training failures.

Lesson 4 • Neural Network Architecture Basics

Explains neurons, layers, activation functions, and forward propagation as the building blocks of deep learning. Establishes intuition before implementation.

Lesson 1 • Monitoring and Model Drift Detection

Implements data drift, concept drift, and performance monitoring to maintain model quality over time. Prevents silent model degradation in production.

Lesson 2 • AI Strategy and Business Value

Frames AI investment decisions using ROI analysis, use-case prioritization, and roadmap planning. Equips students to lead AI initiatives at an organizational level.

Lesson 3 • Model Serving and API Design

Packages models as REST APIs and batch inference services for consumption by applications. Bridges the gap between data science and software engineering.

Lesson 4 • Responsible AI and Ethical Deployment

Addresses fairness, transparency, accountability, and privacy as non-negotiable deployment requirements. Aligns AI systems with organizational and societal values.