Linear Algebra & Matrix
- Linear Combinations
- Vectors & Matrices
- Quantities
- Vectors
- Matrices
- Transpose Matrix
- Inverse Matrix
- Trace of a Matrix
- Determinant Matrix
- Dot Product
- Linear Mappings
- Functions
- Measurements
- Linear Mapping Composition
- Vectors & Matrices
- Vector Spaces
- Formal Rules
- Algebraic structures
- Vector subspaces of a Linear Mapping
- Data Redundancy
- Linear dependence
- Basis and dimension
- Dimension of matrix spaces
- Formal Rules
- Fundamental Theorem of Linear Algebra
- Data Information
- Partition of linear mapping domain and codomain
- Data Partitioning
- Mappings as data
- The Singular Value Decomposition (SVD)
- Data Information
How Much Math Do You Need to Become a Data Scientist?
Data Science is a large field that requires vast expertise and being at a beginner’s level, that’s a fair question to ask “How much maths is required to become a Data Scientist?” or “How much do you need to know in Data Science?”. The point is when you’ll be working on solving real-life problems, you’ll be required to work on a wide scale and that would certainly need to have clear concepts of Mathematics.
The pillar of acing mathematics withholds 4 pillars that can help you to start from scratch and will definitely help you in getting a job in the field of Data Science. These 4 pillars that you’ll be learning need to be applied inside which means you need to also learn how and where to program those mathematics algorithms while working on the system.
The very first skill that you need to master in Mathematics is Linear Algebra, following which Statistics, Calculus, etc. come into play. We will be providing you with a structure of Mathematics that you need to learn to become a successful Data Scientist.