The Data Science Course: Complete Data Science Bootcamp 2025

⭐️ Course Contents ⭐️ ⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

• Welcome (1.1)
• Demand for Data Science (2.1)
• The Data Science Venn Diagram (2.2)
• The Data Science Pathway (2.3)
• Roles in Data Science (2.4)
• Teams in Data Science (2.5)
• Big Data (3.1)
• Coding (3.2)
• Statistics (3.3)
• Business Intelligence (3.4)
• Do No Harm (4.1)
• Methods Overview (5.1)
• Sourcing Overview (5.2)
• Coding Overview (5.3)
• Math Overview (5.4)
• Statistics Overview (5.5)
• Machine Learning Overview (5.6)
• Interpretability (6.1)
• Actionable Insights (6.2)
• Presentation Graphics (6.3)
• Reproducible Research (6.4)
• Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

• Welcome (1.1)
• Metrics (2.1)
• Accuracy (2.2)
• Social Context of Measurement (2.3)
• Existing Data (3.1)
• APIs (3.2)
• Scraping (3.3)
• New Data (4.1)
• Interviews (4.2)
• Surveys (4.3)
• Card Sorting (4.4)
• Lab Experiments (4.5)
• A/B Testing (4.6)
• Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

• Welcome (1.1)
• Spreadsheets (2.1)
• Tableau Public (2.2)
• SPSS (2.3)
• JASP (2.4)
• Other Software (2.5)
• HTML (3.1)
• XML (3.2)
• JSON (3.3)
• R (4.1)
• Python (4.2)
• SQL (4.3)
• C, C++, & Java (4.4)
• Bash (4.5)
• Regex (5.1)
• Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

• Welcome (1.1)
• Elementary Algebra (2.1)
• Linear Algebra (2.2)
• Systems of Linear Equations (2.3)
• Calculus (2.4)
• Calculus & Optimization (2.5)
• Big O (3.1)
• Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

• Welcome (1.1)
• Exploration Overview (2.1)
• Exploratory Graphics (2.2)
• Exploratory Statistics (2.3)
• Descriptive Statistics (2.4)
• Inferential Statistics (3.1)
• Hypothesis Testing (3.2)
• Estimation (3.3)
• Estimators (4.1)
• Measures of Fit (4.2)
• Feature Selection (4.3)
• Problems in Modeling (4.4)
• Model Validation (4.5)
• DIY (4.6)
• Next Step (5.1)