Medical Device Entrepreneurship
This will be a new course taught by Prof. Moore covering topics relevant to biomedical device entrepreneurship. Lectures on business plan preparation, incubators, regulatory, business structures, funding, and IP will be provided by Prof. Moore and guest lecturers. In addition, case studies presented by successful entrepreneurs will be included. Assessment is via weekly reports prepared by each student summarizing the content of each presentation.
Statistics and Data Analysis Probability Theory
Experiments and outcomes; random variables, probability, probability density & mass functions; conditional probability, joint probability. Bayes' theorem. Standard distributions. Estimating properties of distributions: mean and variance, skewness and kurtosis. Goodness of fit of experimental data to distributions: chi-squared test, tests for normal distributions. Two populations: mixture models, t-tests, paired and unpaired. F-tests. Bonferroni correction; Analysis of variance (ANOVA). Parametric vs non-parametric statistics. Regression: Univariate and multivariate regression; significance testing on linear regression; Pearson correlation coefficient; stepwise regression; nonlinear regression. Sampling methods: Sampling from distributions, Monte-Carlo methods, bootstrapping. Time series: Notation, types of time series, simple time-series descriptors. Cross and auto-correlation. Trend removal. Linear filtering. The DFT and its applications. Inference: Bayesian methods. Decision boundaries. Bayesian and neural networks. PCA and KL transforms. MAP and ML decisions. Practicals: C/Matlab for implementation of basic data analysis methods: 2 hours/week for 10 weeks. Applied to medical, epidemiological and biological data sets.
Computational Methods for Bioengineering
Introduction to MATLAB for bioengineers. Use of MATLAB for analysis of physiological data, and preparing scientific figures. Advanced MATLAB programming. A MATLAB “workshop” to which students address a problem related to a research project. Students then choose either (i) Computational Fluid Dynamics, or (ii) Analysis of biological datasets using Python. Assessment is via a report on a small data analysis project performed as part of the course.
Students will be expected to document attendance at least one seminar per week throughout the first two terms, in the Department of Bioengineering. An essay on the topic of one of the seminars attended must be submitted as part of the assessment requirements.
Bioengineering MRes and MSc Courses
Two courses, selected from Optical Imaging Techniques, Techniques in Molecular Bioengineering, or any course from MSc Biomedical Engineering programme.
Imperial College Business School Courses
A minimum of one or two courses selected from MBA electives: High-Tech Strategy, IP Valuation, New Ventures in the Health System, VC Finance; and MSC Innovation, Entrepreneurship and Management electives: Innovation Management, Business Models and IP.
Professional Skills Development Courses
Attendance of at least 2 courses from the Imperial College Graduate School MasterClass Programme is compulsory during the MRes year.
The MasterClass Programme offers 90 minute lecture courses on professional skills development, such as: Academic Writing, Research Skills and Reference Management, Preparing and Writing a Literature Review, Developing your Career through Networking, Interview Skills, Informational Posters - Layout and Design, Negotiating Skills, and others.