Data analytics for executives

Key information

Duration: 16 weeks (online, part-time)
Programme dates:

Location: Study online, 16 weeks
Fees: £1,880

Programme overview

Imperial Business Analytics: From Data to Decisions will expand your understanding of business analytics and teach you how to use descriptive, predictive and prescriptive analytics to identify, analyse and solve critical business problems. You will learn to effectively evaluate recommendations and communicate credibly with those who have deep technical knowledge in the field, having a greater impact within your organisation.

The 16-week online programme draws on expertise from Imperial College Business School faculty, industry experts and case studies. You will also work alongside your peers to explore the practical applications of the analytical frameworks you are learning.

You will experience live online teaching sessions and video lectures with interactive activities and assignments whilst receiving personal support from a dedicated Learning Team. 

You will finish the programme prepared to implement what you’ve learnt. On completion you will receive a verified Digital Certificate from Imperial College Business School Executive Education.

Who should attend?

This international programme is designed for experienced professionals, including:

  • Mid-to-senior functional managers looking to improve their decision making and advance their career

  • Technical managers implementing analytics who need to understand languages like Python to guide their teams

  • Executives seeking a competitive edge in the market or wanting to scale their business or function

  • Consultants aiming to develop their knowledge to build effective solutions for their clients

The programme is applicable across industries, including: advertising, banking and financial services, consulting, education, FMCG, healthcare, IT, retail and telecommunications.

No coding experience is required and you will take two primer modules to bring you up to speed in mathematics, statistics and Python.

Learning objectives

  • Recognise patterns in data using clustering techniques
  • Understand your data using descriptive analytic
  • Draw insights and make predictions from data, using predictive machine learning techniques
  • Assess the feasibility and practical implications of quantitative decisions, as well as their strengths and weaknesses
  • Differentiate between optimal and sub-optimal solutions for real-world business problems
  • Recommend solutions for real-world business problems using analytical techniques
  • Identify problems which can be solved using data
  • Detect and identify correlation between features of a dataset using classification and regression trees
  • Evaluate the power and limitations of analytics in decision making
  • Suggest problems and data sets that are optimally solved by support vector machines

Learning Journey

Module 1: Mathematics & Statistics Primer

Module 2: Python Primer

Module 3: Descriptive Analytics

Module 4: Predictive Analytics

Module 5: Prescriptive Analytics

Programme Faculty and Experts

Professor Wolfram Wiesemann

Professor of Analytics & Operations

Professor Wiesemann is Professor of Analytics & Operations at Imperial College Business School, where he also serves as the Academic Director of the MSc Business Analytics programme. He is also a Fellow of the KPMG Centre for Advanced Business Analytics.

Alex Ribeiro Castro

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.

Become an Associate Alumni

Take your partnership with Imperial College Business School to the next level by becoming an Associate Alumni. Complete one (1) of our on-campus, online, and virtual programmes to claim 'Associate Alumni' status and join our active alumni community.

Key information

Duration: 16 weeks (online, part-time)
Programme dates:

Location: Study online, 16 weeks
Fees: £1,880