The Big Data, Analytics and Data-Driven Decision Making programme focuses on econometric models and machine learning techniques used to analyse high-dimensional data sets, termed big data, and the implications of these in the context of rapid changes occurring in data availability and computational technology. 

There are no formal entry requirements for this programme. However, a general interest in data, or an understanding of agile and design-based thinking is useful.

Big Data, Analytics and Data-Driven Decision Making is suitable for people working within mid- to senior management level roles in any organisation where there is a primary focus on big data. 

The programme is useful for those working in a variety of roles, where finding patterns and relationships in large volumes of data, including marketing, fraud detection and national security.

The course is also suitable for MBA and Executive MBA (EMBA) students. 

Big Data, Analytics and Data-Driven Decision Making will cover several key topics: smart data management, defining questions, use case creation, knowledge about tools, and understanding aspects of key data science.

Big data econometric models provide a vehicle for modelling and analysing complex phenomena and for incorporating rich sources of often confounding information into economic models. 

Participants will benefit from an applied, hands-on introduction to these methods and will be able to read and understand theoretical papers on the subject, by the end of the course. They will also learn to implement techniques in Python programming as well as being able to apply these techniques to data used in economics and business. 

Sources of data sets used in the course include World Bank Group, Kaggle, Federal Reserve Economic Data and Google Finance.

The course will also introduce participants to applications of classification and learning algorithms in artificial intelligence. Integration of these algorithms to business analytics frameworks will be demonstrated using real-world examples. Supervised and unsupervised learning, deep learning techniques, text analytics and recommender systems will be covered in the course.

By the end of the programme participants will be able to assess and model data required for enhanced and better-informed decision making by analysing large sets and wide-ranging sources of data.

Big Data, Analytics and Data-Driven Decision Making is taught by leading big data experts:

Florian Krueger, executive director at Syndikat Ltd and EMEA chair of MIT’s International Society of Chief Data Officers. Florian has over 20 years’ experience of working in fields of big data, IT and the digital space.

Mike Servaes, whose previous roles include Head of Data for the Royal Army. Mike is experienced in data strategy, governance, ethics and quality and is an executive director at the International Society of Chief Data Officers.

Dr Ali Habibnia is an assistant professor in the Department of Economics and the Computational Modeling and Data Analytics, College of Science, Virginia Tech. His specialist disciplines include machine learning, statistics and economics. 

The course will be taught entirely online via LSBU’s learning platform.

The course will total approximately 35 hours teaching, running over 10 sessions for the duration of 5 weeks.

There is no formal assessment for this course. 

There is no formal accreditation for this course. By the end delegates will be able to assess and model data to apply to decision-making, be able to effectively instruct data scientists and be able to differentiate between various tool sets depending on their requirements.

For information on our policies (payment, cancellation, terms and conditions, etc.) visit our general information page.

If you have a question that we haven’t answered here, please contact lsbushortcourses@lsbu.ac.uk and we will respond to your query within 24 hours.