comprehensive curriculum
AI & data science bootcamp equips students with end-to-end data and AI skills, progressing from Python, SQL, and statistics to machine learning, neural networks, LLMs, and applied projects.
Course 1 : Introduction to Python
Programming fundamentals: scripting, compiled vs. interpreted languages, operators, loops, and core data structures (lists, tuples, dictionaries, strings). Culminates in a Python script that analyzes a data file.
- Apply programming methodologies to real-world scenarios
- Demonstrate foundational Python scripting skills
Course 2 : Introduction to Data Science
Statistical measures, data types, and data analysis with pandas. Covers data visualization for qualitative, quantitative, and multivariate data, and object-oriented programming for data cleaning.
- Implement foundational statistical measurement
- Gather insights from data using visualizations
- Integrate OOP with Python for data cleaning and analysis
Course 3 : Introduction to SQL
Database fundamentals and query writing: filtering, ordering, grouping, and joining data. Introduces database design, the data engineering lifecycle, and combining SQL with Python/Pandas.
- Analyze data using Python, SQL, and the cloud
- Manipulate data with math, probability, and statistics
- Analyze a business problem and present findings via dashboard
Course 4 : Cloud Computing, Generative AI & Dashboards
Scalable, cloud-based data processing with PySpark, NumPy, and Pandas. Covers visualization with Seaborn and an introduction to generative AI and interactive dashboards.
- Build a dashboard using industry-standard tools
- Model exploratory data analysis across multiple data sets
- Process large-scale data with PySpark
Course 5 : Inferential Statistics
Statistical inference with Python: probability distributions, confidence intervals, and hypothesis testing for single and multiple groups, including multivariate data sets.
- Perform statistical inference programmatically
- Implement statistical inference methodologies
- Apply math, statistics & probability to derive insights
Course 6 : Regression
Simple and multiple linear regression, model diagnostics, transformations, interaction terms, and regularization (Lasso, Ridge). Emphasis on the bias-variance tradeoff.
- Perform logistic, lasso, and ridge regression
- Compare results across regression types
- Apply math, statistics & probability to derive insights
Course 7 : Introduction to Machine Learning
The machine learning workflow: cleaning and preparing data, choosing algorithms, and evaluating performance using Python and scikit-learn pipelines, applied to healthcare, finance, and e-commerce examples.
- Apply foundational ML modeling (e.g., decision trees)
- Prepare data via preprocessing and normalization
- Apply math, statistics & probability to derive insights
Course 8 : Machine Learning with scikit-learn
Supervised and unsupervised models: k-Nearest Neighbors, recommender systems (SVD), k-means clustering, and PCA for dimensionality reduction. Culminates in a combined supervised/unsupervised project.
- Apply foundational ML modeling
- Prepare data via preprocessing and normalization
- Integrate math, statistics & probability to derive insights
Course 9 : NLP, Time Series & Neural Networks
Natural language processing (text classification, vectorization), time series analysis, and an introduction to neural network theory and implementation with Keras.
- Develop insights from language, time, and image data using NLP and neural networks
- Integrate math, statistics & probability to derive insights
Course 10 : Neural Networks & Similar Models
Normalization and regularization techniques, Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) for sequence data, and an introduction to transformers and BERT.
- Create an advanced neural network application
- Integrate math, statistics & probability to derive insights
Course 11 : Large Language Models
Statistical inference with Python: probability distributions, confidence intervals, and hypothesis testing for single and multiple groups, including multivariate data sets.
- Use ML models and the open-source MLOps stack
- Integrate data-centric LLMs with data science methods
- Apply fine-tuning and prompt engineering to business outputs
Capstone
Three culminating projects framed around real business problems: a regression model, a supervised classification model, and an unsupervised model — plus peer critique and a final showcase discussion.
- Apply a regression method
- Apply a non-regression supervised method
- Apply a non-regression unsupervised method
- Use math, statistics & probability to derive business insights