Data science/analysis

Jon Wilck

804-***-****

**** ****** ****** ********, ** 23221

********@*****.***

www.linkedin.com/in/jonwilck

Education

James Madison University

May 2016 BBA Business Management with Minor in Business Analytics

Major GPA: 3.22

Minor GPA: 3.33

Skills

Power BI, SQL, SSIS, R, Tableau, SAS, NoSQL, Data Visualization, Machine Learning,

Microsoft: Azure, Excel, SharePoint, Visio, Project, Access, PowerPoint, Word

Work Experience

Volunteer Data Analyst Chesterfield County IST

February 2017 May 2017

Spearheaded Power BI dashboard project to visualize and segment department and project manager efficiency and effectiveness using timesheet and project management data

Piloted the Twitter stream analytics project- This helped to analyze the sentiment of various keywords related to the county. After finding an open source sentiment analysis package I set up a data pull into event hub and then a stream analytics job in Azure to transform data into useable format, moved it into long term data lake storage and then created dashboards

Gave one hour demonstration of Microsoft Azure’s Machine Learning Workspace and data mining techniques to deputy CIO of Chesterfield County

Working with big data- created Machine Learning Model using 2.6 million record dataset

Creating SCD (slowly changing dimensional) table packages (historical and changing) in SSIS

Data aggregation, table creation, joins and creating views in SQL server 2016

Executive Assistant l Lawyer Staffing

June 2012 l February 2017

Updated jobs listing page on company’s WordPress and documented process for future use

Analyzed and Summarized candidate resumes and created an Excel Spreadsheet with relevant information

Wiped hard-drives and reinstalled drivers to prepare computers for charitable donation

Prepared and distributed branded gift bags to aid in marketing

Coursework

Descriptive analytic methods- Used pivot tables, descriptive statistics and data visualization to quantify various business metrics

Quantitative business modeling- Final project involved building a model for a high tech internet café to find optimal number of workstations to open and what hourly rate to charge customers in order to maximize profit. First I fit a Poisson distribution to customer arrival data. Then I ran a simulation to assess variation in queue length and minutes a customer spends waiting. From there I ran evolutionary solver to find the optimal values and wrote a managerial style summary report.

Data Mining- Used linear regression, logistic regression, classification/regression trees, neural networks, naïve Bayes, hierarchical analysis, k-nearest neighbors, association rules, etc. to extract potentially important patterns and targetable business opportunities from big datasets.

Multiple regression analysis in R and Excel

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