Netflix
Using nearest neighbour methods in recommendation engines for TV shows and movies.
EXECUTIVE EDUCATION
EXECUTIVE EDUCATION
Learn the fundamentals of Python & progress to concepts of descriptive, predictive & prescriptive analytics that will help you drive business decisions.
28 January 2021
4 months, online
4-6 hours per week
Special pricing up to 20% discount is available if you enrol with your colleagues. Please send an email to group-enrollments@emeritus.org for more information.
Imperial Business Analytics: From Data to Decisions is an online programme brought to you by Executive Education at Imperial College Business School. This immersive and interactive programme will:
You will draw on expertise from Imperial College Business School faculty, industry experts, case studies and your peers. You will also explore the practical applications of the analytical frameworks you are learning.
There is no prior programming knowledge required.
This international programme is designed for experienced professionals, including:
The programme’s content and lessons are applicable across industries, including: banking and financial services, IT, healthcare, consulting, advertising, education, fast moving consumer goods, retail, and telecommunications.
Basics of probability & statistics
Fundamentals of Python
What is data, data and decision making, estimate statistics of a data set, maximum likelihood estimation, detection and quantification of correlation, outliers, linear regression, real-life applications
Introduction to machine learning, machine learning process, recommendation algorithms for increased engagement, supervised learning, forecasting vs inference, using nearest neighbours for classification problems, predict outcomes in a business context using regression trees, classify data using support vector machines, measure similarity of data clusters, predict outcomes for different clusters, machine learning in the real world
Foundations of linear programming, optimisation problems, production planning problem, capital budgeting problem, identifying the constraints, the optimal solution, solving the problem in excel, model business problems as linear programmes, integer programming, optimisation models, tricks-of-the-trade for business decisions, real-life applications
Basics of probability & statistics
Introduction to machine learning, machine learning process, recommendation algorithms for increased engagement, supervised learning, forecasting vs inference, using nearest neighbours for classification problems, predict outcomes in a business context using regression trees, classify data using support vector machines, measure similarity of data clusters, predict outcomes for different clusters, machine learning in the real world
Fundamentals of Python
Foundations of linear programming, optimisation problems, production planning problem, capital budgeting problem, identifying the constraints, the optimal solution, solving the problem in excel, model business problems as linear programmes, integer programming, optimisation models, tricks-of-the-trade for business decisions, real-life applications
What is data, data and decision making, estimate statistics of a data set, maximum likelihood estimation, detection and quantification of correlation, outliers, linear regression, real-life applications
The case studies and industry examples featured throughout the programme provide a wide-ranging look at how companies, organisations, and governments are applying analytics techniques to solve business problems.
Using nearest neighbour methods in recommendation engines for TV shows and movies.
Using discrete optimisation models for employee scheduling.
Using nearest neighbour methods in recommendation engines for music.
Using discrete optimisation models in construction.
Using optimisation models to lower transportation costs in coal acquisition.
Using nearest neighbour methods to improve facial recognition in the security industry.
Optimising blood processing to decrease cost per donation.
Using optimisation models to improve treatment of prostate cancer.
Using an optimisation-based solution to improve the variety of product offerings.
Using optimisation techniques to develop new flood protection standards.
Open-sourcing Torchnet to accelerate AI research.
Using and sharing a deep learning toolkit to increase advances in AI.
Dr Alex Ribeiro-Castro
Professor Wolfram Wiesemann
Dr Alex Ribeiro-Castro
Data Scientist and Senior Teaching Fellow, Imperial College Business School
Alex Ribeiro-Castro is a Data Scientist and Senior Teaching Fellow at Imperial College Business School, where he teaches on the Global Business Analytics MSc. Dr Ribeiro-Castro holds a MA and PhD in Mathematics from the University of California (Santa Cruz), and held a professorship in Mathematics from the Pontifical Catholic University (PUC-Rio) in Rio de Janeiro. He was previously a Visiting Lecturer in the Applied Mathematics Department at Imperial College London and has accumulated over 10 years of teaching experience across the globe, in diverse subjects in pure and applied mathematics. He has previously taught at the University of California in Santa Cruz (USA), the University of Toronto (Canada), and the Institute of Applied and Pure Mathematics (Brazil).
With over five years of consultancy experience, Dr Ribeiro-Castro has made contributions in various industries, including machine learning and optimisation problems in FinTech, health, energy, and more recently in retail. Some of his most recent industry partners include KPMG, Quantum Black/McKinsey, Ion Trading (finance), Ovo Energy/Kaluza (energy), DoctorLink (health) and more recently Otravo BV (retail). He is currently working as a technical adviser for a pioneering data science project in maritime insurance.
Professor Wolfram Wiesemann
Professor of Analytics and Operations, Imperial College Business School
Wolfram Wiesemann is Professor of Analytics and Operations at Imperial College Business School, London, where he also serves as the Academic Director of the MSc Business Analytics programme as well as a Fellow of the KPMG Centre for Advanced Business Analytics.
Before joining the faculty of Imperial College Business School in 2013, he was a post-doctoral researcher at Imperial College London (2010-2011) and an Imperial College Research Fellow (2011-2012). He was a visiting researcher at the Institute of Statistics and Mathematics at Vienna University of Economics and Business, Austria, in 2010, the Computer-Aided Systems Laboratory at Princeton University, USA, in 2011, and the Industrial Engineering and Operations Research Department at Columbia University, USA, in 2012.
Wolfram’s research interests revolve around the methodological aspects of decision-making under uncertainty and machine learning, as well as applications in operations management, energy and finance.
Upon completion of the programme, participants will be awarded a verified Digital Certificate by Imperial College Business School Executive Education.
Download BrochureFlexible payment options available.