- Academic Registry
Course & Unit Catalogues


MA12008: Statistics and data science 1B

[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:
Semester 2
Assessment Summary: CWOG 20%, CWRI 40%, CWSI 12%, EXCB 28%
Assessment Detail:
  • Probability & Statistics 1b (EXCB 28%)
  • Probability & Statistics 1b, Coursework 1 (CWSI 3%)
  • Probability & Statistics 1b, Coursework 2 (CWSI 3%)
  • Probability & Statistics 1b, Coursework 3 (CWSI 3%)
  • Probability & Statistics 1b, Coursework 4 (CWSI 3%)
  • Programming 1b (CWRI 40%)
  • Connections coursework (CWOG 20%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take MA12005
Learning Outcomes: By the end of the unit, you will be able to:
  • Apply modern object-oriented programming paradigms in data science applications; analyse the complexity of algorithms; read and manipulate data; apply statistical methods to extract features and analyse data.
  • Calculate properties of continuous random variables, manipulate joint probability distributions; formulate, fit and assess statistical models; use the R statistical package for simulation and data exploration.
  • Discuss the breadth and importance of modern mathematical research.



Synopsis: You will develop further your skills in statistics and data science. You will use continuous random variables and explore statistical modelling and parameter estimation. You will advance your skills in Python programming with an emphasis on data science. You will explore the beauty and the importance of university-level mathematics.

Content: Probability & Statistics: Properties of continuous random variables, common continuous distributions including the uniform, exponential, normal and gamma distributions. Transformations of random variables (RVs). Proof of the law of the unconscious statistician for a continuous RV. Joint probability density function (PDF). Marginal and conditional distributions of continuous RVs. Independence: factorisation of joint PDF as a product of marginals. Properties of covariance and correlation. Distribution of a sum of continuous RVs, including normal and exponential examples. Statement and application of the central limit theorem. Introduction to model fitting. Exploratory data analysis and model formulation. Parameter estimation by the method of moments and maximum likelihood. Estimators as random variables. Sampling distributions of estimators. Bias, variance and mean-square error of an estimator. Graphical assessment of goodness of fit. Programming & Data Science: Object oriented programming. Programming with objects and classes. Introduction to the design and analysis of algorithms: divide-and-conquer paradigm; sorting algorithms. Computational complexity of algorithms. Python libraries for data and network analysis. Applications to data science. Connections: Mathematical research at the University of Bath, including applications of degree-level mathematics in industry and society, outreach, and advocacy.

Course availability:

MA12008 is Compulsory on the following courses:

Department of Mathematical Sciences
  • USMA-AFB33 : BSc(Hons) Mathematics, Statistics and Data Science (Year 1)
  • USMA-AKB33 : BSc(Hons) Mathematics, Statistics and Data Science with professional placement (Year 1)
  • USMA-AKB33 : BSc(Hons) Mathematics, Statistics and Data Science with study abroad (Year 1)

Notes:

  • This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.