I need a mentor for my daughter who is taking up IIT bs for datascience and programming. Since she is a commerce student she haven't went through in maths in 11th and 12th
Hence i need a mentor to teach her and make comfortable from beginner to expertise in both
1.Maths for data science
2,Statiscs for data science

The Core Mathematics Curriculum
Mathematics at this level is generally split into discrete structures and continuous mathematics.

Discrete Mathematics (Often Math 1): This focuses on logic, set theory, combinatorics, and graph theory. Graph theory (trees, connectivity, coloring) is particularly crucial, as it forms the direct foundation for algorithms and data structures.

Linear Algebra (Often Math 2): This involves matrices, vector spaces, eigenvalues, and eigenvectors. Linear algebra is the engine that drives almost all machine learning and data transformation algorithms.

Calculus (Often Math 2): You will move past basic derivatives into multivariable calculus, limits, continuity, and gradients. Understanding gradients is essential for optimization problems, such as gradient descent in machine learning.

The Core Statistics Curriculum
Statistics usually transitions from simply describing data to making predictions and inferences based on it.

Descriptive Statistics & Probability (Stat 1): This covers measures of central tendency, variance, Bayes' theorem, and standard probability distributions (Binomial, Poisson, Normal, Uniform). This lays the groundwork for understanding uncertainty and randomized algorithms.

Inferential Statistics (Stat 2): This focuses on sampling distributions, hypothesis testing (Z-tests, T-tests, ANOVA), confidence intervals, and correlation/regression. This area is strictly geared toward making data-driven decisions and evaluating models.