Data Scientist

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

Create a GitHub repository and Medium blog post to communicate your findings

Explore object-oriented programming (OOP), including classes, instances, magic methods, inheritance, and polymorphism. Utilize professional coding patterns for efficient Python software development.

Use and create virtual environments, write Python packages, manage file paths in a way that enables cross-platform compatibility, and ensure code quality through testing and linting.

Dig into deployment options for ML models using web servers, web forms, dynamic endpoints, and interactive dashboards with Python and FastHTML.

Master scikit-learn pipelines for efficient data workflows, preprocessing, feature engineering, and model optimization, enhancing machine learning projects with automation and clarity.

Explore computer vision by learning image preprocessing, feature extraction, and building classification pipelines with SVMs and CNNs using tools such as OpenCV and PyTorch.

Build machine learning pipelines with text data features, including tokenization, vectorization, and part-of-speech tagging with spaCy.

Explore unsupervised learning in data science, focusing on clustering techniques, k-means algorithms, metrics, and applications for enhanced data analysis.

Learn the fundamentals of dimensionality reduction and how to use it in your models effectively. Utilize Scikit-learn and matplotlib to calculate and visualize the dimension reduction process.

Learn about the different methods used to create recommendation engines with Python.

Learn how to evaluate unsupervised machine learning models, including clustering, dimensionality reduction, and recommendation systems, to improve their performance.

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.