Forecasts of births and deaths play an important role in determining the dynamics of both population size and gender-age structure. Since population forecasts are the basis of long-term planning of socio-economic development, the statistical accuracy of forecasts is particularly important, and the applied methods play a special role here. The purpose of this study is to evaluate Autoregressive Integrated Moving Average (ARIMA) model ability to forecast the yearly number of births and deaths in Azerbaijan. In the analysis, the Box-Jenkins methodology was followed when building the suggested model. Besides, Akaike’s information criterion (AIC) and Bayesian Information Criteria (BIC) are used to select the best ARIMA model, compared to another estimated models.  The prediction results of the models are evaluated using the mean absolute percentage error (MAPE) and the root mean square error (RMSE) . Comparing the predicted data from the ARIMA models shows that the correct selection of model parameters, it possible to fairly accurately predict the yearly number of births and deaths. Thus, using the advantages of the ARIMA model, it is possible to obtain forecasts of birth and death rates for the near future and it possible to observe changes that will occur in the age structure of the population. And these interpretations can guide policymakers to focus on socio-economic development and comprehensive healthcare system strengthening as crucial strategies for raising the fertility level and further reducing mortality rate.

The digitization of nearly all media and the increasing migration of social and economic activities to the ?nternet, the development of social networking technologies, the ?nternet of Things and cloud computing caused rapid increase in the volume of data and the formation of Big Data paradigm. Big Data involves technologies and tools for collecting, processing, analyzing and extracting useful knowledge from structured and unstructured data of large volumes generated at high speed by different sources. Increasing the volume, speed, diversity and value of Big Data began to play an important role in the creation of social relationships, competitive advantage and innovative fields. The development of the information society, the formation of digital economy, and the application Big Data technologies in different spheres of human activity required the quantitative and qualitative assessment of Big Data. In this article some approaches relate to the definition of Big Data have been reviewed. Methodological approaches and indicators for measuring Big Data have been researched. At the end, the indicators have been proposed for the measurement of factors that affected the growth and development of Big Data.