Data Science: Statistics and Machine Learning Specialization Course Syllabus

Overview: This specialization provides a comprehensive journey through key data science concepts, blending theory with hands-on practice. Over approximately 130 hours of content, learners will master statistical inference, regression modeling, machine learning techniques, and data product development, culminating in a capstone project using real-world data. The flexible structure is designed for working professionals seeking to enhance their analytical expertise and build job-ready skills in data science.

Module 1: Statistical Inference

Estimated time: 54 hours

• Understanding data distributions and variability
• Using p-values and significance testing
• Constructing confidence intervals
• Applying permutation tests for hypothesis testing

Module 2: Regression Models

Estimated time: 53 hours

• Performing regression analysis using least squares
• Interpreting ANOVA and ANCOVA models
• Analyzing residuals and model fit
• Assessing variability in regression outcomes

Module 3: Practical Machine Learning

Estimated time: 8 hours

• Building prediction functions
• Differentiating training and test sets
• Identifying and avoiding overfitting
• Evaluating models using error rates

Module 4: Developing Data Products

Estimated time: 10 hours

• Creating interactive data visualizations
• Designing data products for public use
• Telling stories with data

Module 5: Data Science Capstone

Estimated time: 5 hours

• Integrating statistical and machine learning methods
• Working with real-world datasets
• Building a functional data product

Module 6: Final Project

Estimated time: 5 hours

• Deliverable 1: A complete data analysis pipeline
• Deliverable 2: A machine learning model with evaluation metrics
• Deliverable 3: An interactive data visualization or application

Prerequisites

• Familiarity with R programming
• Basic understanding of statistics
• Comfort with mathematical reasoning and data analysis

What You'll Be Able to Do After

• Perform statistical inference to draw conclusions from data
• Apply regression models to analyze variability and relationships
• Build and evaluate machine learning prediction functions
• Develop interactive data products for public audiences
• Demonstrate data science proficiency through a real-world capstone project