Dr Karima Dyussekeneva
Lecturer
Business
Telephone: (01792) 295294
Room: Office 320 - 320
Third Floor
School of Management
Bay Campus

Dr. Karima Dyussekeneva is a lecturer in the School of Management. She has a PhD in Information Decision Making from the University of Bath, with a thesis that explored methods of new product forecasting and empirically investigated quantitative and qualitative forecasting instruments.

She also holds an MBA from Durham University. Prior to entering academia, she worked in the telecommunications industry in domains including new product development, sales forecasting and marketing analysis. She advised a number of organisations and companies in both the public and private sector, including major international brands, in forecasting and predictive analytics.

Her current research interests focus on predictive analytics and data mining, machine learning and artificial intelligence, forecasting, decision making and business analytics in the interdisciplinary fields of management, computer science, finance and healthcare. Prospective PhD students are welcome to contact her with regard to related topics.

Areas of Expertise

  • Business analytics and forecasting
  • New product forecasting
  • Predictive analytics
  • Data mining
  • Statistics for business

Publications

  1. & New product forecasting: Methods. LAP LAMBERT Academic Publishing.
  2. & Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets. IFAC Proceedings Volumes 46(9), 87-92.
  3. & The use of analogies in forecasting the annual sales of new electronics products. IMA Journal of Management Mathematics 24(4), 407-422.
  4. & The challenges of pre-launch forecasting of adoption time series for new durable products. International Journal of Forecasting 30(4), 1082-1097.

Teaching

  • MN-1017 Essential Analytic Skills for Business

    The module will provide an overview of the mathematical and statistical methods necessary for management/finance/economics analysis, focusing specifically on the use of computer packages (primarily SPSS) to conduct statistical analysis.

  • MN-2506 Maths 2 for Business

    This module extends the first year maths/statistics module taken to provide further tools and techniques in mathematics.

  • MN-2517 Statistics 2 for Business

    This module extends the first year maths/statistics module taken to provide tools and techniques in statistics using computer packages to perform the analysis. The module is applied to SPSS and AMOS statistical analysis packages.

  • MN-3024 Data Mining

    The module is designed to provide students with practical and applied knowledge of how to conduct data mining activities for business and management purposes. This includes conceptual approaches and key concepts in data mining as well as the statistical and modelling techniques necessary to analyse large data sets to generate meaningful business intelligence. The module takes a data driven approach to operation of data analysis.

  • MN-M534 Business Analytics

    Business analytics as applied to big data is a game-changing opportunity for business practice. The last decade has seen an explosion in the volume of data collected by business and government ¿ providing opportunities to analyse behaviours at individual and macro- levels. The purpose of this module is to inform students as to the major concepts of business analytics ¿ both in theory and in practice from a strategic perspective.

  • MN-M535 Data Mining

    The module is designed to provide students with practical and applied knowledge of how to conduct data mining activities for business and management purposes. This includes conceptual approaches and key concepts in data mining as well as the statistical and modelling techniques necessary to analyse large data sets to generate meaningful business intelligence. The module takes a data driven approach to operation of data analysis.

Supervision

  • A decision system for children's rights policy development (current)

    Student name:
    PhD
    Other supervisor: Dr Frederic Boy