comprehensive curriculum
Our data science & AI bootcamp blends analytics, ML, and AI with hands-on Python and SQL training.
Data Analysis and Engineering
Students master Python, data manipulation, and visualization tools to clean, analyze, and present data effectively.
- Master Python programming for data processing and analysis.
- Use pandas and NumPy for data manipulation and statistical analysis.
- Perform data cleaning and web scraping to gather and prepare datasets.
- Create data visualizations using Matplotlib, Seaborn, and Tableau.
- Use Git/GitHub for version control and collaboration.
- Access data through APIs and understand data ethics.
Scientific Computing & Quantitative Methods
students apply statistical methods, hypothesis testing, and SQL to perform quantitative analysis and build linear regression models.
- Apply probability theory, combinatorics, and statistical distributions to analyze data.
- Conduct hypothesis testing and A/B testing to draw insights from data.
- Perform inferential statistics and understand concepts like the Central Limit Theorem.
- Build linear regression models and evaluate their performance.
- Use SQL to query databases and extract data efficiently
Machine Learning Fundamentals
students build predictive models using regression and classification techniques, while learning data preprocessing and model evaluation.
- Build predictive models using linear regression and logistic regression.
- Understand object-oriented programming in Python for reusable code.
- Use scikit-learn for machine learning tasks like classification and decision trees.
- Perform data preprocessing, feature engineering, and hyperparameter tuning.
- Learn gradient descent and optimization techniques for model training.
- Evaluate models using classification metrics and cross-validation.
Advanced Machine Learning
students explore advanced AI techniques, including NLP, deep learning, recommendation systems, and unsupervised learning.
- Apply unsupervised learning techniques like clustering and dimensionality reduction.
- Build recommendation systems using collaborative filtering and content-based methods.
- Perform natural language processing (NLP) tasks like text classification and vectorization.
- Explore deep learning with neural networks and frameworks like TensorFlow or PyTorch.
- Understand model interpretability and the differences between black-box and white-box models.
- Work with big data and time series analysis.
Capstone Project
students complete a capstone project, integrating all skills to analyze data, build models, and communicate actionable insights.
- Develop a capstone project that integrates all skills learned in the program.
- Gather, clean, and analyze data to build statistical or machine learning models.
- Communicate findings through data visualization and storytelling techniques.
- Present the project to instructors and peers, demonstrating the ability to deliver actionable insights.
- Gain experience working on a large-scale data science project from start to finish.