MA12012: Core pure and statistical mathematics A
[Page last updated: 23 October 2023]
Academic Year: | 2023/24 |
Owning Department/School: | Department of Mathematical Sciences |
Credits: | 15 [equivalent to 30 CATS credits] |
Notional Study Hours: | 300 |
Level: | Certificate (FHEQ level 4) |
Period: |
|
Assessment Summary: | EXCB 100% |
Assessment Detail: |
|
Supplementary Assessment: |
|
Requisites: | |
Learning Outcomes: |
After taking this unit, you will be able to:
|
Synopsis: | You will develop core skills in mathematics. You will study algebra and build a firm grounding in the fundamental objects of mathematics such as sets, functions, numbers, polynomials and matrices. You will study analysis to define the notions of convergence and limit. You will develop a solid foundation in probability theory that will facilitate further study in probability and statistics. |
Content: | Algebra:
Sets and functions: constructions and properties, cardinality. Equivalence relations and partitions. Permutations: cycle notation and sign, the symmetric group. Numbers (natural, integer, rational, real, complex); algebraic properties as rings and fields. Primes, factorisation, and Euclid's algorithm; modular arithmetic. The complex plane: complex exponentials, roots of unity. Polynomials: division with remainder, coprime polynomials. Matrices with real coefficients: matrix algebra, linear transformations, 2-by-2 and 3-by-3 determinants, geometric interpretation. Systems of linear equations: matrix representation, row operations and row echelon form.
Probability & Statistics:
Sample space, events as sets, unions, and intersections. Axioms and laws of probability. Equally likely events. Sampling methods: with or without ordering and replacement. Conditional probability. Partition theorem. Bayes' theorem. Independence of events. Bernoulli trials. Discrete random variables (RVs). Probability mass function (PMF). Bernoulli, Geometric, Binomial and Poisson distributions. Joint and marginal discrete distributions. Definition of continuous random variables (RVs), cumulative distribution functions (CDFs) and probability density functions (PDFs). Some common continuous distributions including uniform, exponential and normal. Independence of RVs (including joint distribution as a product of marginals). Expectation of RVs. Properties of expectation. Expectation of product of independent RVs. Variance and properties. Standard deviation. Moments, covariance, correlation. Sums of independent random variables. Key application: Random walks. Statement of the law of large numbers.
Sequences:
Logic, quantifiers. Numbers and inequalities. Sequences. Series. Limits |
Course availability: |
MA12012 is Compulsory on the following courses:Department of Computer Science
|
Notes:
|