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Junteng Ma
Apprenant.e
(1)
1
Portails
Catégories
Modélisation des données Apprentissage automatique Intelligence artificielle Bases Data science

Compétences

Dashboard 1 Machine learning algorithms 1 Occupational safety and health 1 Pandas (python package) 1 Predictive modeling 1 Pyspark 1 Python (programming language) 1 Sql (programming language) 1 Time series 1 Time series analysis and forecasting 1

Réalisations

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Project Participant
Émis par Riipen
The Project Participant achievement is awarded to learners who start their first project, demonstrating their commitment to putting learned skills to practice.
juill 3, 2023

Dernières rétroactions

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Vinh Lam
Jr Data Scientist
avril 17, 2023
Rétroaction d'equipe
Time series analysis and forecasting Pyspark Python (programming language) Predictive modeling Sql (programming language) Machine learning algorithms Occupational safety and health Pandas (python package) Dashboard Time series
Thank you for joining the project with Sofvie. You showed a deep understanding of the concepts that they were learning in the course and were able to apply them to our project in a meaningful way. You were also proactive in seeking out additional information and resources when needed, which helped ensure that the project was completed on time and to a high standard. Wishing you all the best for a bright and successful future!
Concordia University
Big Data Analytics
Concordia University
Sofvie Inc.
Time series analysis
Sofvie Inc.

Projets récents

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Sofvie Inc.
Sofvie Inc.
Greater Sudbury, Ontario, Canada

Time series analysis

For this project, students will be tasked with using Python, SQL, and PySpark to analyze occupational health and safety data and make predictions. The goal of the project is to use time series analysis techniques to identify trends and patterns in the data, and to develop models that can be used to make informed predictions about future incidents and accidents. Once the data has been analyzed, students will need to use time series analysis techniques to identify trends and patterns, and to develop predictive models that can be used to forecast future incidents and accidents. These models may be implemented using machine learning algorithms or other statistical methods. Finally, students will need to present their findings and recommendations to stakeholders, possibly in the form of a report or presentation. This may involve creating visualizations or dashboards to help communicate the results of the analysis.

Jumelages 7
Catégorie Apprentissage automatique + 4
Fermé