LBS logo London experience. World impact.

Take advantage of a forward-looking, forward-thinking analytics degree that goes far beyond pure data skills.

 

We have a strong and proven track record of success in Early Career education. Navigating the intersection between business context, data science and machine learning, the MAM core curriculum ensures you’re business ready - able to operate and deliver global business impact through data.

 

Core courses

 

The academic core curriculum focuses on two main areas, Analytics and Data Science, and General Management. Both strands are fully integrated throughout the programme, giving you a hybrid skill set that enables you to analyse and interpret data, then translate it into powerful business results.

 

  • Analytics/Data Science: The rigorous Analytics core is underpinned by a solid programming and database curriculum delivered by expert practitioners and experienced PhD students. Develop and test hypotheses and learn how to communicate and tell a story through data.

  • General Management: General Management courses support every subject area. Develop a structured approach to problem solving and decision-making, with ample opportunity to apply what you learn in the analytics curriculum. Hands-on project work with data and applied methodology will provide subject specific business insight.

 

Covering all the major aspects of data projects - business understanding, data understanding, data preparation, modelling and evaluation, and deployment – we maximise your opportunities to practice software and programming languages through core course assignments and project work.

 

Analytics core

  • Applied Statistics

    Build a critical understanding of statistical models, including issues of credibility, overfitting and generalisation. You will also learn how to communicate and reason with data models.

     

    Key concepts taught

     

    • Multiple, logistical and nonlinear regression
    • Time series
    • Monte Carlo simulation
    • Bootstrapping
    • Sampling designs, including stratification.
  • Data Science for Business I

    The amount of data available for business decisions has grown tremendously in recent years since it became relatively cheap to collect, store and retrieve data due to widespread use of the world wide web and advances in computer technology. In this course you will learn the fundamentals of data science and the data science project cycle, identifying applications of data mining in business problems

     

    Key concepts taught

     

    • Predictive modelling
    • Regression-based methods for data mining
    • Model selection
    • Data fitting and over fitting
    • Model testing, cross-validation
    • Learning curves, clustering.
  • Data Science for Business II

    Going further, you will learn to identify applications of data mining in more complex business problems and to assess which learning algorithms to use in different situations. You will master data mining tools for data exploration.

     

    Key concepts taught

     

    • Clustering
    • Regression trees
    • Nearest neighbours
    • Naïve Bayesian learning
    • Ensemble methods.
  • Data Visualisation and Story Telling

    In this course you will build an understanding of how to effectively communicate information about data using graphical, verbal and visual means for three major audiences: data experts (e.g. Head of Analytics); consumer and presentation experts (e.g. Chief Marketing Officer); and executive leadership (e.g. Chief Executive Officer).

     

    Key concepts taught

     

    • Visualisation tools in Tableau & R

    • Design principles for effective charts and graphs

    • Visualising different types of data (e.g. categorical time series and geospatial data)

    • Effective dashboard design & effective digital presentations.

  • Data Management

    In this course you’ll learn the fundamentals of data storage, building essential skills in data cleansing and retrieval in order to facilitate data usage and to ensure data quality in organisations and data science projects.

     

    Key concepts taught

     

    • Relational databases

    • SQL

    • Data manipulation

    • Accessing data sources

    • Web APIs

    • Web crawling

    • Parsing text data.

  • Machine Learning for Big Data

    This course will cover contemporary machine learning methods to analyse large data sets, teaching you how to use dimension reduction techniques to deal with large data as well as building your understanding of how to use text data for prediction, classification and data exploration.

     

    Key concepts taught

     

    • Regularization and variable selection in regression methods, e.g. Lasso and ridge regression

    • General dimension reduction techniques e.g. Principal Component Analysis and Linear Discriminant Analysis

    • Deep learning and neural networks

    • Text Mining: How to convert text (or other unstructured) data to structured data and analyse it using dimension reduction techniques introduced in this class.

  • Decision Technology

    Turn real-world problems into computational models to help make better managerial decisions.

     

    Key concepts taught

     

    • Linear and Integer programming
    • Simulation
    • AI techniques
    • Risk Analysis
    • Decision Trees.

Management core

  • Accounting – Financial and Management

    Modern analytics has endless applications at the intersection of financial and non-financial data, and the best way to learn is to get your hands dirty with a problem to solve. In this course, we will study real-world problems and data sets as you learn how to apply analytics in a financial setting, all the way from getting your hands dirty executing an analysis, to explaining and justifying your findings to a VC general partner or a C-suite executive.

  • Economics

    This course in applied microeconomics has a primary focus on the needs of managers. It is about markets, how they operate and affect firms’ choices. On successful completion of this course, you will be able to evaluate major strategic bets in commodity markets, using fact-based, logically-grounded predictions about costs and the path of market prices; Identify the categories of costs that are relevant for critical business decisions such as pricing, new market entry, and capacity abandonment; learn how the interplay between cost and demand fundamentals determine profit-maximizing pricing decisions and learn how to apply game theory to analyze interactions among strategic agents and how to apply it to concentrated markets (oligopoly).

  • Finance

    Analytics is permeating most finance activities and revolutionizing the industry. Students will have the opportunity to learn the concepts and tools needed to be at the forefront of the changes. For instance, how lenders can use big data to make faster and better credit decisions, how traders can use data analytics to maximize portfolio returns, among others.

  • Strategy

    In the Strategy course, students will learn to apply quantitative methods to distinguish between different strategic options, exposing potential pitfalls and hidden benefits. We will pay particular attention to understanding the strategic context in which the various options are to be analysed.

  • Organisational Behaviour

    Performing in Organisations: Career success depends not only on performance in terms of deliverables and meeting objectives, but also on “performance” on the organisational stage: building coalitions, mapping/managing social networks, understanding/changing cultures, and, most importantly, working in teams. The goal of this course is to enhance students’ interpersonal skills to help them successfully navigate the social side of their organisational and professional life. These will be the three main themes of the course: (i) develop an awareness of how the social environment of organizations work and how to assess its characteristics (ii) develop the ability to effectively work in a team, as well as to help teammates to contribute at their best to teamwork (iii) learn the social sciences of organisations necessary to thrive and grow within different organisations.

  • Marketing

    Marketing is the managerial process by which a firm creates value for its customers and captures a share of that value for itself in the form of revenue. The goal of the marketing process is to assemble a detailed understanding of customers and prospects, and to use this knowledge to organize the firm’s offer to those groups. This course introduces you to the key concepts, frameworks and tools that are relevant to analysing business settings from a marketing perspective, and sees you applying the relevant frameworks and tools to analyse marketing‐related problems and develop appropriate recommendations for the decision maker.

Contact us

Email: mam@london.edu
Tel: +44 (0)20 7000 7378


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