• Introduction to Python
  • Apply foundational programming concepts in Python to solve
    real-world problems
  • Write and organize Python scripts using functions, loops, and
    core data structures
  • Use Python libraries to manipulate and analyze data
• Introduction to Data Science
  • Implement foundational statistical measurement with data
    using scripting
  • Demonstrate gathering insights from data with visualizations
  • Integrate object oriented programming (OOP) with Python for
    data cleaning and analysis
• Introduction to SQL
  • Utilize industry standard techniques to programmatically
    analyze data with Python, SQL, and the cloud
  • Explore and manipulate data with mathematics, probability,
    and statistics
  • Analyze data for a business problem with visualizations with a
    dashboard

• Cloud Computing, Generative AI & Dashboards
  • Process and analyze large-scale datasets using PySpark to
    combine Python, SQL, and distributed computing
  • Transform and visualize big data with NumPy, Pandas,
    Matplotlib, and Seaborn to surface real insights
  • Build AI-enhanced dashboards and interactive projects that
    communicate data clearly and at scale
• Inferential Statistics
  • Apply statistical inference techniques using Python to analyze
    real datasets
  • Implement hypothesis testing, confidence intervals, and
    probabilistic models across diverse data types
  • Use mathematics, statistics, and probability to support sound,
    data-driven conclusions
• Regression
  • Build and evaluate regression models, including linear,
    multiple linear, and regularized approaches (Lasso and
    Ridge)
  • Compare regression techniques using diagnostics,
    transformations, and statistical performance measures
  • Apply mathematics, statistics, and probability to interpret
    results and draw data-driven insights

• Introduction to Machine Learning
  • Build and apply foundational machine learning models,
    including supervised learning and decision trees
  • Prepare data for machine learning through preprocessing,
    feature extraction, and normalization
  • Apply mathematics, statistics, and probability to evaluate
    models and derive meaningful insights
• Machine Learning with Scikit-Learn
  • Build supervised and unsupervised machine learning models
    using Scikit-Learn
  • Apply distance metrics, clustering, and dimensionality
    reduction techniques such as k-means and PCA
  • Prepare and transform data for machine learning through
    preprocessing, feature engineering, and normalization
• Natural Language Processing, Time Series & Neural Networks
  • Apply NLP techniques to transform and model text data for
    classification and analysis
  • Analyze and model time-dependent data using visualization
    and time series techniques
  • Build and train neural networks using Keras, grounded in core
    mathematical and statistical principles
• Neural Networks & Similar Models
  • Design and train advanced neural network architectures,
    including CNNs, RNNs, and transformers
  • Apply normalization and regularization techniques to
    optimize deep learning models
  • Use mathematical and statistical principles to evaluate, tune,
    and interpret neural network performance

Large Language Models

• Deploy and manage machine learning models using the
  open-source MLOps stack
• Improve LLM performance through data-centric workflows,
  curation, and evaluation
• Apply fine-tuning and prompt engineering to tailor LLM
  outputs for real-world applications

AI & Data Science Capstone

• Design and implement an end-to-end supervised learning
  project using a non-regression classification approach
• Build an unsupervised project leveraging large language
  models to extract structure, insight, or automation from
  unstructured data
• Apply mathematical, statistical, and data science principles to
  generate clear, actionable insights tied to business outcomes