# Linear Algebra & Statistical Techniques

#### About This Course

**Linear algebra** is the branch of mathematics concerning **linear** equations which provides concepts which are useful to many areas of computer science. It’s really useful in Data Science when data written as vectors and then operations performed on them in order to measure them. Linear Algebraic methods are necessary to do that.

Statistics is about collection, organization, displaying, analysis, interpretation and presentation of data

#### Target Audience

- All Students

#### Curriculum

44 Lessons

#### Quadrant 2 Elementary Row Transformations

In this lecture we will introduce elementary row transformations on matrices.

##### Draft Lesson

##### Draft Lesson

##### Lesson 200:00:00

##### MCQ 1

#### Quadrant 2 Solving Linear Systems using Gaussian Elimination

In this lecture we will learn to the study of the nature of the solutions of a system of linear equations.

#### Quadrant 2 &4 : Bases and Dimensions-1

We will study about basis and dimension of vector spaces

#### Quadrant 2: Bases and Dimensions -2

continuation of bases and dimensions of vector spaces

#### Quadrant 2: Identical spaces

To study linear spaces

#### Quadrant 2:Linear Space

To Study about linear Spaces

#### Quadrant 2 Gauss Jordan Row Reduction Method

In this lecture we will learn solving the system of linear equations by Gauss Jordan Row Reduction Method .

#### Quadrant 1 Eigen Value & Eigen Vector Module-I

#### Quadrant 1Eigen Value & Eigen Vector Module-2

#### Quadrant 1 & 4 Cayley Hamilton theorem

#### Quadrant 1 Cayley Hamilton theorem

#### Quadrant 1 Diagonalization of matrices Module -I

#### Quadrant 1 Diagonalization of matrices Module- II

#### Quadrant 1 Non Homogeneous Linear Systems Module -I

#### Quadrant 1 Non Homogeneous Linear Systems Module -II

#### Quadrant 1 Homogeneous Linear Systems Module -I

#### Quadrant 1 Homogeneous Linear Systems Module-II

#### Quadrant 1 Homogeneous Linear Systems Module-II

#### Quadrant 1 Rank of Matrix Module-I

#### Quadrant 1Rank of Matrix Module -II

#### Quadrant 1 Introduction to Matrices Module-I

#### Quadrant 1 &4: Vector Spaces Module 1

To study about vector spaces

#### Quadrant 1 &4 : Vetor subspaces Module 1

To study vector subspaces

#### Quadrant 1: Vector subspaces module 2

#### Quadrant 1:Linear combination of vectors

To know what is linear combination of vectors

#### Quadrant 1:Span of a Set

To understand span of a set

#### Quadrant 2 Properties Of Linear Transformations

In this lecture ,we will discuss some special types of linear transformations .We will also examine some elementary properties of linear transformations.

#### Quadrant 2: The matrix of a linear transformation continued

In this lecture we will discuss the matrix representation of a linear transformation and will solve a few problems.

#### Quadrant 2 Linear operator and similarity

In this lecture we will show that any two matrices for the same linear operator (on a finite-dimensional vector space) with respect to different ordered bases are similar.

#### Quadrant 2 Kernel and Image of a linear transformation

In this lecture, we will study two special subspaces associated with a linear transformation T: V → W, called the “kernel” and “range” of T. We will also illustrate techniques for calculating bases for both the kernel and range. We will see how dimensions of kernel and range are related to the dimension of the domain of a linear transformation.

#### Quadrant 2 Kernel and Image of a linear transformation continued

In this lecture we will discuss about the dimension theorem and will also solve some problems.

#### Quadrant 4 – Correlation Analysis

To determine correlation coefficient.

#### Quadrant 4 – Regression Analysis

To compute regression coefficients and regression equations.

#### Quadrant 4- Sampling Theory

To apply test of significance

#### Quadrant 3: FAQ

Frequently Asked Questions

#### Quadrant 2: Vector Subspaces -II

Algebra of Subspaces