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. Her current research interests focus on new product forecasting, based on the combination of statistical and judgemental methods. This includes providing support to managers making decisions on multiple objectives and integrating scenario planning with multiattribute decision methods.

She is currently working on a software development which is designed to aid forecasting a new product in the consumer goods sector.

Prior to entering academia she worked in telecommunication domain, sectors related to new product development, sales forecasting and marketing analysis. She has several published works at leading academic journals such as International Journal of Forecasting and Journal of management mathematics.

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-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-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

  • Untitled (current)

    Student name:
    PhD
    Other supervisor: Dr Frederic Boy