Data Science Methodology Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides an essential, non-technical foundation in the end-to-end structure of data science projects. You'll learn the core methodology behind real-world data science workflows, from defining business problems to deploying solutions and gathering feedback. The course is designed for beginners and consists of four modules, each taking approximately one week to complete, with short, digestible lessons and hands-on exercises to reinforce understanding. Total time commitment is around 16-20 hours.
Module 1: From Problem to Approach
Estimated time: 4 hours
- Data science vs traditional research
- Business problem framing
- Analytic approaches
- Identifying project objectives from sample business problems
Module 2: Understanding and Preparing Data
Estimated time: 4 hours
- Data requirements
- Data sources
- Data collection methods
- Cleaning and formatting
- Identifying suitable data sets for given problems
Module 3: Modeling and Evaluation
Estimated time: 4 hours
- Model selection
- Model building
- Performance evaluation metrics
- Case-based decision-making on appropriate models
Module 4: Deployment and Feedback
Estimated time: 4 hours
- Sharing insights
- Delivering results
- Feedback loops
- Continuous improvement
- Creating a basic outline of a deployment plan
Prerequisites
- Familiarity with basic business concepts
- No prior technical or programming experience required
- Interest in data-driven decision making
What You'll Be able to Do After
- Describe the full lifecycle of a data science project
- Define data science problems and determine appropriate analytic approaches
- Identify data requirements and suitable data sources
- Explain how models are selected, built, and evaluated
- Create a basic deployment and feedback strategy for data science solutions