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Detail-oriented Python Data Analyst with over 5 years of experience in data analysis and visualization. Proficient in utilizing Python libraries such as Pandas, NumPy, and Matplotlib to extract insights from complex datasets. Skilled in SQL for data querying and manipulation. Strong understanding of statistical analysis and machine learning techniques.
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Employment History
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Junior Data Analyst - Bright Analytics
Jun, 2018 - Sep, 2020
Analyzed large datasets to identify trends and patterns, increasing reporting efficiency by 30%.
Developed automated data pipelines using Python, reducing manual data entry time by 50%.
Collaborated with cross-functional teams to deliver actionable insights that improved decision-making processes.
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Python Data Analyst - DataFlow Solutions
Jun, 2020
Utilized Python and SQL to extract, clean, and analyze data, resulting in a 25% increase in data accuracy.
Created interactive dashboards using Matplotlib and Seaborn, enhancing data visualization and user engagement by 40%.
Implemented machine learning models to predict customer behavior, achieving an accuracy rate of 85%.
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Education
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B.S. in Data Science at University of Washington
Jun, 2014 - Sep, 2018
Studied data analysis, statistical methods, and machine learning algorithms. Completed projects involving data visualization and predictive modeling.
Websites & Social Links
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LinkedIn
https://www.linkedin.com/in/michaelreeves
GitHub
https://github.com/michaelreeves
Skills
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Developed a machine learning model to predict customer churn using Python and Scikit-learn, achieving an accuracy of 90%.
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LinkedIn
https://www.linkedin.com/in/michaelreeves