Michel Davidovich
About Michel Davidovich
Michel Davidovich is an Associate Data Scientist at TomTom and a Data Science Teacher at Asociación Española de Programadores Informáticos, with a background in teaching Python and data science concepts.
Title
Michel Davidovich is an Associate Data Scientist with professional experience in data science education and practical machine learning applications.
Current Positions
Michel Davidovich currently holds the position of Data Science Teacher at the Asociación Española de Programadores Informáticos since 2022. Additionally, he serves as an Associate Data Scientist at TomTom, also starting in 2022. Both roles are based in Madrid, Comunidad de Madrid, España.
Past Professional Experience
Davidovich has accumulated diverse experience in data science and educational roles. He worked as a Python Teacher at the Asociación Española de Programadores Informáticos in 2022 for 11 months. He was also a Data Science Intern at TomTom from 2021 to 2022. His career began in Montevideo, Uruguay, with roles at Gianni as a Data Scientist (2019-2021) and Operations Manager (2017-2019). Additionally, he served as a Sports Writer at Gr from 2020 to 2021 and a Teaching Assistant in Numerical Computation at Universidad de Montevideo from 2016 to 2017.
Education and Expertise
Davidovich's educational background is robust. He earned a Master in Data Science from MBIT School (2021-2022), completed a Data Science Bootcamp on Udemy in 2021, and studied Ingeniería Industrial at Universidad de Montevideo from 2012 to 2016. His expertise includes teaching machine learning concepts such as supervised and unsupervised learning, natural language processing, deep learning, data preparation techniques, and evaluation metrics.
Teaching and Curriculum Development
In his teaching roles, Davidovich focuses on both introductory and advanced machine learning concepts. His curriculum includes practical applications of natural language processing and deep learning. He emphasizes teaching data preparation techniques, data cleaning, and statistical concepts to prepare data for machine learning processes. He also provides detailed explanations of evaluation metrics for various machine learning tasks.