Introduction to Data Science and Machine Learning equips professionals with essential skills to analyze, model, and deploy data-driven solutions.
Scope Statement:
This course introduces core concepts and practical skills in data science and machine learning (ML). Participants will learn to identify different data types, perform exploratory data analysis, clean and prepare data, and build basic predictive models using supervised and unsupervised techniques. The course emphasizes a complete ML workflow, from data collection through deployment, while highlighting real-world problem-solving and communication in a data science context.
Throughout the course, students will develop skills in:
- Defining and differentiating between structured and unstructured data.
- Understanding data storage methods and formats.
- Learning the components and roles within the field of Data Science.
- Understanding the lifecycle and workflow of ML projects.
- Applying exploratory data analysis techniques to univariate and multivariate datasets.
- Performing data enrichment and preparation, including handling missing data, outliers, and encoding variables.
- Developing predictive models using supervised and unsupervised learning techniques.
- Understanding key evaluation metrics and model deployment strategies.
The course progression follows a module-by-module structure, building foundational skills before advancing to more complex topics. The learning environment encourages open dialogue and is tailored to encourage growth and curiosity, combining structured methods with creative problem solving. This course is designed to meet federal mission needs.
No prior experience in coding or data science is required.
