Core courses

Analytics and Data Science. General Management. The MAM core is fully integrated. Build a hybrid skill set that enables you to analyse and interpret data, then translate it into powerful business results.

Analytics/Data Science:

Rigorous analytics underpinned by a solid programming and database curriculum and delivered by expert practitioners and LBS faculty. Explore and test hypotheses and learn how to communicate and tell a story through data.
General Management:

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 provides subject specific business insight.

Pre-programme

Online pre-programme courses
Lay the groundwork for your learning before term starts. LBS online modules in finance, accounting and statistics blend seamlessly into the rest of your core courses. Other pre-work including programming ensure you are up to speed on the applications used in the core.

Pre-programme courses are mandatory, but students with prior knowledge and experience in these areas may take the online tests without completing the coursework.

Analytics core

Applied statistics
Using state-of-the-art business software, build a critical understanding of statistical models, including issues of credibility, overfitting and generalisation. Learn how to communicate and reason with data models.

Key concepts taught:

R and R studio for data analysis and decision making

Multiple, logistical and nonlinear regression analysis

Inferential Statistics, including sampling and Bootstrapping.
Intro to R for Data Science
Sometimes referred to as the “golden child” of Data Science, R is a vital programming language for all big data analysts.

Key concepts taught:

Write and use R script files and functions

Loops

Basic plotting

Data modelling

Graphics.
Intro to Python for Data Science
One of the most popular programming languages, Python is an important part of your data analysis toolkit.

Key concepts taught:

Conditional statements and loops

Work with strings, lists and dictionaries

Read and write data

Pandas for data analysis

Time series and data frames

Matplotlib for visualisation.
Data Science for Business I
It is now relatively cheap to collect, store and retrieve data, thanks to widespread use of the world wide web and advances in computer technology. 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 and learning curves

Classifications

Clustering.
Data Science for Business II
Next, learn to identify applications of data mining in more complex business problems. Assess which learning algorithms best suit different situations and master data mining tools for data exploration.

Key concepts taught:

Clustering

Regression trees

Nearest neighbours

Advanced classification methods, such as Naïve Bayesian learning.

Data Visualisation and Storytelling
Understand how to effectively communicate information about data. Use graphical, verbal and visual means targeting 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 and digital presentations.
Data Management
Explore the fundamentals of data storage and build essential skills in data cleansing and retrieval. Learn to facilitate data usage to ensure data quality in organisations and data science projects.

Key concepts taught:

Data management, including RDMS and ERD

Database design

Data analysis with SQL

Data warehouses and data lakes

Internet database environments.
Machine Learning for Big Data
How do you use dimension reduction techniques to deal with large data? Use contemporary machine learning methods to analyse large data sets and build your understanding of how to use text data for prediction, classification and data exploration.

Key concepts taught:

Dimension reduction

Text mining

Discriminant Analysis

Neural networks

Processing text data and text mining.
Decision Analytics and Modelling
Use data to turn real-world problems into actionable business decisions.

Key concepts taught:

Decision Modelling

Linear, Convex and Integer programming

Risk Analysis

Monte Carlo simulation

Decision trees.

Management core

Financial and Managerial Accounting
Accounting is the language of business. Whether you choose a career as a corporate manager, investor, advisor or entrepreneur, you will need to understand accounting information to make your business decisions. Learn the fundamental concepts in financial and managerial accounting and you will understand how business transactions are reflected in the accounts and how to analyse appropriately financial statement information. Engage in accounting data analysis to acquire first-hand experience of how professionals deal with accounting information in the real world.
Economics
Take a closer look at the needs of managers, examining markets, how they operate and how they affect firms’ choices. Evaluate major strategic bets in commodity markets and use 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 determines profit-maximising pricing decisions and apply game theory to analyse interactions among strategic agents.
Finance

Strategy
Organisational Behaviour - Performing in Organisations
Prime yourself for career success, not just in terms of deliverables and meeting objectives but also in how you ‘perform’ on the organisational stage. Build coalitions, map and manage social networks, understand/change cultures and most importantly, learn how to work in teams. Our goal is to enhance your interpersonal skills, helping you navigate the social side of your organisational and professional life: Build an awareness of how social organisations work, and learn how to assess their characteristics; develop the ability to work effectively in a team and help teammates contribute at their best; and learn the social sciences necessary for you to thrive and grow within different firms.
Marketing
How does a firm create value for its customer? How does it capture 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 organise the firm’s offer to those groups. Learn the key concepts, frameworks and tools relevant to analysing business settings from a marketing perspective and apply them to marketing-related problems, developing appropriate recommendations – and solutions – for the decision maker.

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Pre-programme

Analytics core

Management core

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