Netflix
Using nearest neighbour methods in recommendation engines for TV shows and movies.
Learn the fundamentals of Python & drive business decisions with descriptive, predictive, and prescriptive analytics.
18 November 2021
4 months, online
4-6 hours per week
£1,790 £1,590 or get £220 off with a referral
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.
Round 1
The first application deadline is 31 October 2021
Fee: £1,590
Round 2
The second application deadline is 18 November 2021
Fee: £1,690
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.
Learn the basics of statistics and probability, including theory and models, Bayes’ rule, conditional probability, probability distribution, binomial distribution, central limit theorem, and manipulating normal variables.
Gain an overview of operating systems; use variables in Python; create and manage lists; understand tuples and dictionaries in Python; delve into Boolean and conditional variables; expand your knowledge of functions, and work on code manipulation.
Evaluate data for business decisions; estimate statistics of a data set and maximum likelihood; learn detection and quantification of correlation; understand outliers and linear regression, and discover how these concepts are used in real-life applications.
Dive into machine learning; understand supervised learning; compare forecasting vs. inference; use nearest neighbors for classification; predict outcomes using regression trees; classify data using support vector machines; measure the similarity of data clusters, and predict outcomes for different clusters.
Build your knowledge of linear programming by tackling problems of optimisation, production planning, and capital budgeting; identify constraints and the optimal solution; model business problems as linear programmes; learn tricks of the trade.
Learn the basics of statistics and probability, including theory and models, Bayes’ rule, conditional probability, probability distribution, binomial distribution, central limit theorem, and manipulating normal variables.
Dive into machine learning; understand supervised learning; compare forecasting vs. inference; use nearest neighbors for classification; predict outcomes using regression trees; classify data using support vector machines; measure the similarity of data clusters, and predict outcomes for different clusters.
Gain an overview of operating systems; use variables in Python; create and manage lists; understand tuples and dictionaries in Python; delve into Boolean and conditional variables; expand your knowledge of functions, and work on code manipulation.
Build your knowledge of linear programming by tackling problems of optimisation, production planning, and capital budgeting; identify constraints and the optimal solution; model business problems as linear programmes; learn tricks of the trade.
Evaluate data for business decisions; estimate statistics of a data set and maximum likelihood; learn detection and quantification of correlation; understand outliers and linear regression, and discover how these concepts are used in 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.
Open-sourcing Torchnet to accelerate AI research.
Using and sharing a deep learning toolkit to increase advances in AI.
Using an optimisation-based solution to improve the variety of product offerings.
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 optimisation techniques to develop new flood protection standards.
— Lin Emilie Lassen, Product Owner, Swire Shipping
— Christos Kotsis, Treasurer & Risk Manager, A.M. Nomikos
— Jared Phau, Senior Manager, Ministry of Health (Singapore)
— Ben Dair, Chief Product Officer at Motion Impossible
Professor Wolfram Wiesemann
Dr Alex Ribeiro-Castro
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.
Dr Alex Ribeiro-Castro
Data Scientist
Alex holds an advisory position linked to the Business Analytics MSc and is an occasional guest lecturer for Executive Education. He also works as a quantitative analyst for the financial industry. He was previously a Data Scientist and Senior Teaching Fellow at Imperial College Business School. 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.
Upon completion of the programme, participants will be awarded a verified Digital Certificate by Imperial College Business School Executive Education.
Please note that this programme contributes to earning Associate Alumni status. Visit the Associate Alumni page to find out more.
Download BrochureFlexible payment options available.