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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    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.

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    Who Is This Program For?

    Data Science in Healthcare is designed for technical professionals who have at least a moderate level of comfort with some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics. In this program, you will learn to:

    • Use RStudio and Python analytics tools to address specific healthcare applications
    • Use predictive analytics for public health issues
    • Use data science to increase efficiency on the operations side
    • Understand how to design precision solutions for patient care using AI
    • Use predictive analytics to prevent fraud and other undesired outcomes

    Although these topics could be applied to a range of businesses, this program will be particularly useful for entry to mid-career professionals in roles similar to the following:

    Analysts – Ideal for professionals working in analytics roles in healthcare or industries adjacent to healthcare, such as insurance, pharmaceuticals, or biotech.

    Mid-Level Managers – Ideal for professionals on the executive track who have quantitative responsibilities and relevant experience in a healthcare field.

    Entry-Level Professionals – Ideal for professionals just beginning their careers who are looking to develop a data foundation with applications in the healthcare industry.

    Representative roles well suited to this program include:

    • Data Analyst
    • MIS Analyst
    • Healthcare Analyst
    • Clinical Analyst
    • Business Analyst
    • Healthcare Operations Analyst
    • Hospital Research Analyst
    • Fraud Analyst
    • Healthcare Fraud Investigator
    • Financial Analyst
    • Risk Analyst

    Drive business decisions with

    Modules

    Module 1:

    Maths and Statistics Primer

    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.

    Module 4:

    Predictive Analytics

    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.

    Module 2:

    Python Primer

    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.

    Module 5:

    Prescriptive Analytics

    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.

    Module 3:

    Descriptive Analytics

    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.

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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.

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    Netflix

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

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    Union Airways

    Using discrete optimisation models for employee scheduling.

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    Pandora

    Using nearest neighbour methods in recommendation engines for music.

    Program Faculty

    Certificate

    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.

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