Data Science Math Skills Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Building Blocks for Problem Solving
Estimated time: 3 hours
- Introduction to set theory and Venn diagrams
- Properties of the real number line
- Interval notation and its applications
- Summation and sigma notation
Module 2: Functions and Graphs
Estimated time: 3 hours
- Graphing on the Cartesian plane
- Understanding slope and distance formulas
- Defining and identifying functions
- Exploring function inverses
Module 3: Measuring Rates of Change
Estimated time: 3 hours
- Concept of instantaneous rate of change
- Tangent lines and their significance
- Exponents and their properties
- Logarithms and the natural logarithm function
Module 4: Introduction to Probability Theory
Estimated time: 3 hours
- Foundations of probability
- Basic rules and axioms of probability
- Bayes’ theorem and its applications
Module 5: Welcome to Data Science Math Skills
Estimated time: 0.4 hours
- Course structure and learning objectives
- Overview of video lectures and quizzes
- Information on earning the certificate of completion
Module 6: Final Project
Estimated time: 2 hours
- Solve real-world problems using set theory and interval notation
- Apply functions and graphing techniques to data scenarios
- Demonstrate understanding of probability and rate of change concepts
Prerequisites
- Basic high school algebra knowledge
- Familiarity with mathematical notation
- Interest in data science or analytical fields
What You'll Be Able to Do After
- Apply core mathematical concepts to data science problems
- Interpret and create graphs using Cartesian coordinates
- Use summation and probability notation effectively
- Understand and compute rates of change and logarithmic relationships
- Utilize Bayes’ theorem in practical scenarios