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.
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 Excel and 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.
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.
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.
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.
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.
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.
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.
Modern analytics has endless applications at the intersection of financial and non-financial data. Get your hands dirty - explore and solve real-world problems and data sets and apply analytics in a financial setting. Analyse, explain and justify your findings to a VC general partner or a C-suite executive.
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.
Analytics is now revolutionising the finance industry and central to most finance activities. Learn the concepts and tools needed to be at the forefront of this change. Consider how lenders can use Big Data to make faster, better credit decisions and examine how traders can use data analytics to maximise portfolio returns.
Find out how to apply quantitative methods to distinguish between different strategic options. Expose potential pitfalls, hidden benefits and look at the strategic context in which these different options will be analysed.
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.
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.
A state-of-the-art elective portfolio showcases deep faculty insight and research expertise. Study alongside students across our community and take advantage of all we have to offer.
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