Mathematical Foundations for Data Science

Mathematical Foundations for Data Science

🔹 Builds a strong conceptual and practical understanding of mathematics used in data science

🔹 Covers core areas: Linear Algebra, Calculus, Probability, and Statistics

🔹 Focuses on applying mathematical concepts to data analysis, machine learning, and modeling

🔹 Explains key ideas through structured lessons and real-world examples

🔹 Develops skills in matrix operations and vector analysis

🔹 Teaches optimization techniques using derivatives and gradients

🔹 Helps interpret and analyze multivariate data

🔹 Strengthens analytical thinking and problem-solving abilities

🔹 Builds mathematical intuition required to understand data science algorithms

🔹 Suitable for students and professionals aiming to advance in data science and AI

Why You’ll Learn This

✅ To understand the mathematics behind data science and machine learning models

✅ To confidently implement and interpret data science algorithms

✅ To improve logical reasoning and analytical problem-solving skills

✅ To prepare for advanced studies in data science, AI, and machine learning

✅ To build a strong foundation for real-world, data-driven decision making

Course Content: Mathematical Foundations for Data Science

🟢 Session 1: Linear Algebra – Vector Properties and Operations

🔵 Session 2: Matrix Introduction – Types of Matrix

🟣 Session 3: Basic Matrix Transformations and Determinant

🟠 Session 4: Inverse Matrix and Trace Matrix

🔴 Session 5: Covariance and Orthogonal Matrix

🟡 Session 6: Eigenvectors and Eigenvalues

🟢 Session 7: Calculus – Derivatives and Gradients Method