Data Science and Artificial Intelligence

🚀 Data Science & Applied AI: 20-Hour Intensive Crash Course

📦 Module 1: Environment Setup & Numerical Computing

• Duration: 2 Hours

• Key Tools: Jupyter Notebooks, VS Code, NumPy

• Topics Covered:

  • Setting up the Python environment, virtual environments, and notebook workflows.

  • NumPy arrays vs. Python lists: Memory efficiency and vectorized computation.

  • Multi-dimensional array operations, slicing, indexing, and matrix manipulation.

  • Broadcasting mechanics and eliminating iterative loops for high-speed linear algebra.

🐼 Module 2: Data Manipulation & Wrangling

• Duration: 4 Hours

• Key Tools: Pandas

• Topics Covered:

  • Ingesting heterogeneous data formats (CSV, Excel, JSON, SQL databases).

  • Working with Pandas Series, DataFrames, and index alignment.

  • Data hygiene: Handling missing values, imputation strategies, string cleaning, and duplicate removal.

  • Advanced data operations: Grouping, aggregations (groupby), joining, and merging multi-source datasets.

📊 Module 3: Exploratory Data Analysis & Visualization

• Duration: 3 Hours

• Key Tools: Matplotlib, Seaborn

• Topics Covered:

  • Descriptive statistics and distribution analysis (Histograms, KDE plots).

  • Bivariate and multivariate relationship modeling (Scatter plots, pair plots, line graphs).

  • Outlier detection and statistical anomalies using Box Plots and IQR methods.

  • Feature correlation analysis using heatmaps to identify key predictors prior to modeling.

🤖 Module 4: Applied Machine Learning

• Duration: 4 Hours

• Key Tools: Scikit-Learn

• Topics Covered:

  • The standardized machine learning workflow (fit / predict / transform).

  • Supervised Learning - Regression: Continuous target prediction using Linear & Ridge Regression.

  • Supervised Learning - Classification: Categorical decision boundary modeling with Logistic Regression and Random Forests.

  • Feature processing: Train-test splitting, One-Hot Encoding, and Feature Scaling (Standard vs. MinMax).

  • Model evaluation metrics: Accuracy, Precision, Recall, $F1$-score, Confusion Matrix, and $R^2$ Score.

🧠 Module 5: Foundations of Artificial Intelligence & Deep Learning

• Duration: 4 Hours

• Key Tools: PyTorch / TensorFlow, OpenAI API

• Topics Covered:

  • Introduction to Modern AI: Distinguishing classical ML, Artificial Neural Networks (ANNs), and Deep Learning.

  • Neural Network Mechanics: Perceptrons, activation functions (ReLU, Sigmoid, Softmax), forward propagation, and gradient descent.

  • Introduction to Generative AI & LLMs: Overview of Transformer architectures, attention mechanisms, and tokenization.

  • API & Prompt Engineering: Integrating pre-trained GenAI models into Python apps for text summarization, extraction, and automated reasoning.

🏆 Module 6: Capstone Project & Portfolio Execution

• Duration: 3 Hours

• Key Tools: Full Data Science & AI Tech Stack

• Topics Covered:

  • End-to-end execution on a raw, unstructured real-world dataset.

  • Problem formulation, data extraction, exploratory visualization, and cleaning pipeline.

  • Training, tuning, and evaluating predictive ML/AI models.

  • Generating data-driven insights, presenting performance metrics, and structuring the codebase for GitHub portfolio showcase.

⏱️ Total Course Hours Breakdown:

• Modules 1–3 (Data Foundations): 9 Hours

• Modules 4–5 (Machine Learning & AI): 8 Hours

• Module 6 (Hands-on Capstone): 3 Hours

• Total: 20 Hours
  

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