Math notes / Algebra
This is more for overview (my own) than for teaching or exercise.
For some more indepth material to study, see e.g. the http://www.khanacademy.org/
Other data analysis, data summarization, learning

Contents
Elementary algebra
Elementary algebra or basic algebra are terms that frequently point at the parts of algebra regularly taught in secondary education, typically the more easily understood parts and may be useful to calculus.
To some degree, the concept of basic algebra seems to exist to contrast it with the more complex stuff typically left until university (though basic algebra courses tends to introduce these concepts only explored later).
Algebra assumes knowledge of arithmetic, and introduces some concepts that are central to like
 the concept of variables
 how to play with expressions and how they are affected by certain changes,
 polynomial equations (linear equation, quadratic equation, etc.)
 factorization, root determination, and such
Linear algebra
Linear algebra studies things like linear spaces (a.k.a. vector spaces) and linear functions (a.k.a. linear maps, linear transforms), systems of linear equations.
...in part because this fairly specific focus has relatively wide application.
A lot of said uses relate to, informally, "when a matrix multiplies a vector, it does something meaningful", and a decent area of linear algebra studies the various useful things that you can do.
Vectors and matrices
Notation and conventions
Vectors
Matrices
Can hold any tabular sort of data. Many uses are more specific and constrained. In many cases we deal with data as real numbers.
You may sometimes see irregular matrices or sparse matrices, often in computing. In general, matrices are assumed to be regular and nonsparse.
Uses of matrices
Matrices are used for various bookkeeping of nontrivial data, so have many specific(ally named) uses. Including:
In linear algebra
 representing certain numerical problems,
 for example, and commonly, the coefficients of a set of linear equations (each row being one equation)
 in part just a data storage thing, but there are some matrix properties/transforms that make sense
 Transformation matrix [1]
 storing linear transforms in matrices
 ...so that matrix multiplication, typically on coordinate vectors, will apply that transformation to that vector
 see e.g. the workings of OpenGL, and various introductions to 3D graphics
 Some singlepurpose transformation matrix examples:
 Rotation matrix  [2]
 Shift matrix  http://en.wikipedia.org/wiki/Shift_matrix (ones only on the subdiagonal or superdiagonal)
 Shear matrix  [3]
 Centering matrix  [4]
In graph theory and such
 distance matrix  distances between all given points. E.g. used for graphs, but also for other things where there is a sensible metric.
 adjacency matrix  [5]
 further matrices assisting graph theory, including degree matrix[6], incidence matrix[7],
 Similarity matrix [8]
 Substitution matrix [9]
 Stochastic matrix 
 a.k.a. probability matrix, transition matrix, substitution matrix, Markov matrix
 stores the transitions in a Markov chain
 [10]
In multivariate analysis, statstical analysis, eigenanalysis
 Covariance matrix  used in multivariate analysis. Stores the covariance between all input variables [11]
Other
 Confusion matrix
 a visualisation of the performance of a classification algorithm
 rows are predicted class, columns are known class
 numbers are the fraction of the ca
 the closer this is to an identity matrix, the better it performs
 representing differential equations
 Many others, see e.g. http://en.wikipedia.org/wiki/List_of_matrices
Notes:
 Many are defined in such a way that certain operations are meaningful (though the meaning of operations can obviously vary).
 For example, multiplication of graph's adjacency matrix with itself will express connections in as many steps
 Real vector space ℝ^{n} (often ℝ^{2} or ℝ^{3} in examples) are quite common in vectors, and common to various matrix uses.
 For example, when solving linear equations, you often have row vectors in ℝ^{n}, and many (though not all) operations
 Operations
Augmenting  an augmented matrix appends columns from two matrices (that have the same amount of rows) Seen in application to systems of linear equations
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Some supporting terms and properties
Operations
Common concepts around vectors and matrices
Space
Linear combination
Span, basis, and subspace
Simultaneous equations / systems of equasions
Linear independence
Rank
Basis
Orthogonality, orthonormality
Angle between two vectors
More complex concepts around here
Eigenvectors and eigenvalues
Eigenvalue algorithms
Power method / power iteration
Deflation Method
Eigendecomposition
Applications
SVD
On the decomposed matrices' sizes
Definition / main properties
In more detail
Further properties, observations, and uses
See also
Others
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Abstract algebra
Abstract algebra studies the possible generalizations within algebra.
It concerns concepts like group theory, rings, fields, modules, vector spaces, and their interrelations.