Do you need to have a math Ph.D to become a data scientist?
Mathematical knowledge is an important skill for a data scientist. But, how much math is really needed to do data science?
Mathematics is a lot of areas such as algebra, analysis and statistics. It is not necessary to know every area in order to apply the different algorithms.
In this Talk, we would specify the areas needed for data science and the different definitions and concepts.
Math is a tool and not a monster to be afraid of
- Linear algebra basics
* Basic properties of matrix and vectors: scalar multiplication, linear transformation, determinant
* Special matrices: square matrix, identity matrix, triangular matrix, idea about sparse and dense matrix, unit vectors
* Eigenvalues, eigenvectors, diagonalization, singular value decomposition
- statistics and probabilities:
* Basic probability: basic idea, expectation, probability calculus, Bayes’ theorem, conditional probability
* Probability distribution functions: uniform, normal, binomial, central limit theorem
* Sampling, measurement, error, random number generation
* Hypothesis testing, A/B testing, confidence intervals, p-values, ANOVA, t-test