27-29 November, Vilnius

Conference about Big Data, High Load, Data Science, Machine Learning & AI

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VALENTINA DJORDJEVIC

Things Solver, Serbia

VALENTINA DJORDJEVIC

Things Solver, Serbia

Biography

Valentina Djordjevic works as a Data Scientist at Things Solver. She has a Bachelor’s degree in Information Systems and Technologies and a Master’s degree in Business intelligence, at University of Belgrade, Faculty of Organizational Sciences. The main fields of studies she focuses on the most are time series analysis and anomaly detection techniques. She has a strong technical knowledge in the field of Data Science, including programming (Java, Python, SQL, ETL), statistics (descriptive statistics, hypothesis testing, probability theory,…), modeling (machine learning algorithms- neural networks, random forest, linear regression, k-means, isolation forest, association rules, recommender systems, ARIMA models…) and visualization (Matplotlib, Plotly, Tableau,…). Data science problems that she’s been working on are coming from various business domains, from telecommunications to retail and banking, where she’s dealing with forecasting, predictive maintenance, anomaly detection, segmentation, churn prevention, etc.

Talk

Anomaly Detection in Telecommunications

This presentation will cover the types of anomalies often met in the data, and comparative analysis of two different techniques that could be used for their detection Autoencoders and Isolation Forest. After a short introduction to theoretical concepts of these techniques, as well as their pros and cons, the results of their application to data from a telecommunication network will be presented and analysed.

Workshop

Time series forecast

This workshop will cover basic concepts of time series analysis, like time series decomposition, stationarity analysis, trend and seasonality smoothing. Afterwards, some of the most popular algorithms used for time series forecasting will be presented and explored. The workshop will include programming in Python, and its time series forecasting library – PyFlux.