Description:

This course is designed to provide the foundational mathematics knowledge necessary for understanding and analyzing data in the field of Data Science. Topics covered include linear algebra, calculus, statistics, and probability theory with a focus on applications in data analysis and machine learning.

Key Highlights:

• Foundational Mathematics for Data Science
• Applications in Data Analysis and Machine Learning
• Key Concepts in Linear Algebra, Calculus, Statistics, and Probability
• Hands-on Practice with Data Science Problems

What you will learn:

• Understanding Linear Algebra
  Learn key concepts such as matrices, vectors, and eigenvalues to apply them in data analysis.
• Applying Calculus in Data Science
  Explore how calculus concepts like derivatives and integrals are used in optimizing machine learning algorithms.
• Mastering Statistics for Data Analysis
  Gain proficiency in statistical methods for summarizing and interpreting data in real-world scenarios.
• Probability Theory in Data Science
  Understand the foundations of probability theory and its role in making data-driven decisions.