KYLE VOSEN
Lubbock,TX
Summary
Data Analyst with a passion and curiosity for solving problems through analytics. Experience in data-mining (Python, SQL), statistical analysis, Qlik, machine learning (scikit-learn, Pandas, NumPy), deep learning (Keras) and visualizing findings with Power BI and Looker Studio. I am a detail-oriented individual who strives to perform and provide valuable insight.
Overview
3
3
years of professional experience
Work History
Data Analyst
The Signatry
07.2022 - 07.2024
- Produced monthly snapshots using GCP and utilized SQL to pull together multiple databases.
- Enhance business efficiency by utilizing Power BI and Looker Studio to effectively communicate business insights
- Utilized Caspio to create a new online database to streamline the workflow and improve efficiency for 3 different teams.
- Created several data governance documents to ensure understanding across the company.
Data Science Intern
O3 Solutions
09.2021 - 12.2021
- Determined the optimal path of construction for capital projects by querying client databases (SSMS) and calculating R values across large datasets in an agile work environment
- Used Qlik Sense to create operational, analytical, and strategic dashboards to provide better understandings of analytical data and reduced the time spent looking at metrics by 15%
- Targeted areas in clientele databases in which data collection was underperforming and communicated this to clientele to improve data collection.
Education
B.S. - Marketing and Management
Birmingham Southern College
Birmingham, AL05.2022
Data Science Certificate -
Flatiron School
Birmingham, AL08.2021
Skills
- Programming: Python, SQL (Pandas, scikit-learn, Numpy)
- Linear and logistic regression, decision trees
- Machine Learning, Natural Language Processing, Time Series, A/B Testing
- Data Visualization: Qlik Sense, Power BI, Seaborn, Matplotlib, Looker Studio
- Data Management Solutions: Google Cloud Platform
Projects
- News Categorization: Utilized natural language processing to classify news based on a headline or short description. Processed the dataset consisting of 200,853 rows and 41 different categories of news which was reduced to 12 categories based on similarities. The model performed with an f1 score of 62% on the test data and 66% on the training data.
- Predictive Modeling for League of Legends, Implemented scikit-learn's supervised learning algorithms to create Logistic Regression, Decision Trees, Bagged Trees, Random Forest Classifier, Gradient Boosting Classifier, Ada Boosting Classifier, XGBoost, and K Nearest Neighbors to predict the outcome of a League of Legends game., Reduced the model from using 40 features to 19 features. The values of these 19 features could be imported into the model by a coach at the 10 minute mark of the match., 72.71%
Timeline
Data Analyst
The Signatry
07.2022 - 07.2024
Data Science Intern
O3 Solutions
09.2021 - 12.2021
B.S. - Marketing and Management
Birmingham Southern College
Data Science Certificate -
Flatiron School
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