EXECUTIVE EDUCATION

Imperial Business Analytics: From Data to Decisions

Learn the fundamentals of Python & progress to concepts of descriptive, predictive & prescriptive analytics that will help you drive business decisions.

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Course Dates
STARTS ON

28 January 2021

Course Duration

DURATION

4 months, online
4-6 hours per week

Course Duration

Why enrol for the Business Analytics programme?

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:

  • Take you through the fundamentals of the programming language Python to help you expand your understanding of business analytics.
  • It will teach you how to use descriptive, predictive and prescriptive analytics to identify, analyse and solve critical business problems.
  • It will help you understand and explore fundamental methods, frameworks and techniques of business analytics to make sense of your data and use it to make informed business decisions.

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.

Who is this programme for?

This international programme is designed for experienced professionals, including:

  • Technical managers implementing analytics in their function or organisation.
  • Professionals seeking to enter into the field of analytics & data science.
  • Mid-to-senior functional managers looking to improve their decision making using data.
  • Consultants aiming to develop their knowledge of business analytics.

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.

Drive business decisions with

Modules

Module 1:

Maths and Statistics Primer (2 weeks)

Basics of probability & statistics

Module 2:

Python Primer (3 weeks)

Fundamentals of Python

Module 3:

Descriptive Analytics (3 weeks)

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

Module 4:

Predictive Analytics (4 weeks)

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

Module 5:

Prescriptive Analytics (4 weeks)

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

Module 1:

Maths and Statistics Primer (2 weeks)

Basics of probability & statistics

Module 4:

Predictive Analytics (4 weeks)

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

Module 2:

Python Primer (3 weeks)

Fundamentals of Python

Module 5:

Prescriptive Analytics (4 weeks)

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

Module 3:

Descriptive Analytics (3 weeks)

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

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Case studies

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.

Netflix

Using nearest neighbour methods in recommendation engines for TV shows and movies.

Union Airways

Using discrete optimisation models for employee scheduling.

Pandora

Using nearest neighbour methods in recommendation engines for music.

Kearns & Associates

Using discrete optimisation models in construction.

New Bedford Steel Company

Using optimisation models to lower transportation costs in coal acquisition.

Security Industry

Using nearest neighbour methods to improve facial recognition in the security industry.

American Red Cross

Optimising blood processing to decrease cost per donation.

Memorial Sloan-Kettering Cancer Center

Using optimisation models to improve treatment of prostate cancer.

Hewlett Packard

Using an optimisation-based solution to improve the variety of product offerings.

The Netherlands

Using optimisation techniques to develop new flood protection standards.

Facebook

Open-sourcing Torchnet to accelerate AI research.

Microsoft

Using and sharing a deep learning toolkit to increase advances in AI.

Faculty

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... More info

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... More info

Certificate

Certificate

Upon completion of the programme, participants will be awarded a verified Digital Certificate by Imperial College Business School Executive Education.

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