Mariem Sayari

Skills: Microsoft Power BI · Spotfire · Python · Cloud Snowflake · Hive · SAP BO · Alteryx

In a bank's liquidity service, missing or inaccurate input data can generate significant losses and may, during test periods, cause its mission to fail. Anomalies, which include outliers (in small numbers) and inaccurate data, can be predefined by the existence (or nonexistence) of business knowledge. In this context, the use of Data Science methods is relevant and demonstrates the significance of relying on the exploratory analysis of data and the use of Machine Learning algorithms in a Big Data framework. These methods made it possible to uncover anomalies confirmed by the business lines on different types of data and to set the main axes of production launch. Keywords : Anomalies, Big Data, Data Science, Liquidity, Machine Learning, Unsupervised, Scalability.

Task : Monitoring SAS Keywords :Data mining, Logs, SAS, Monitoring, KPI, Server

Streamlining the recruitment procedure and making it more efficient by effectively negating the limitation of the tools which are already at the disposal of human resources agents such as professional social media and recruitment platforms (LinkedIn, Indeed, etc.) and automating a big part of the data collection and research steps and also harnessing the power of data in order to offer assistance to the human resources department agents using artificial intelligence. Technologies : Python, MongoDB, Machine Learning

Topic : Development of an image classification application using Deep Learning Keywords : Machine Learning, Deep Learning, Python, Classification, Image

Construction of subscriber lines Technologies : Optical fibre, Switching centre, Telephone network, ADSL

Bibliographic Research : Big Data Sampling Techniques Keywords : Hadoop, Big Data, Spark, Sampling