By the conclusion of the course, participants will possess the skills to:

1. In Linear Algebra, they will comprehend vectors, matrices, and the operations necessary for effective data manipulation.

2. They will also understand eigenvalues, eigenvectors, and matrix factorization, which are pivotal in machine learning and dimensionality reduction.

3. Additionally, they will be equipped to solve systems of linear equations and apply these principles to practical data scenarios.

4. In Calculus, participants will learn the significance of calculus in optimization and learning algorithms.

5. They will be able to apply derivatives for gradient calculations, a fundamental aspect of gradient descent in training machine learning models. Furthermore, they will delve into multivariable calculus, focusing on partial derivatives and Jacobians to optimize complex functions.

6. In Statistics, they will grasp both descriptive and inferential statistics, including central tendency measures, variance, and probability distributions. They will conduct hypothesis testing, analyze p-values, and estimate confidence intervals. Participants will also utilize statistical techniques to analyze datasets, identify trends, and make predictions.

7. The course will provide a robust mathematical foundation for machine learning algorithms, enabling the use of statistical models to derive insights from real-world data. Participants will apply linear algebra methods for data transformations and visualizations, as well as implement calculus principles in model optimization and machine learning frameworks.

8. This course is ideal for aspiring data scientists and machine learning professionals, as well as students and practitioners seeking a thorough understanding of the mathematics underpinning data science. It is also suitable for individuals preparing for advanced studies in artificial intelligence, data analysis, or related disciplines.