Engineer Data Analyst

Vibhor Dharmadhikari

**** ******* *****, *******, ** 443-***-**** *******.**@*****.*** www.linkedin.com/in/vibhordharmadhikari EDUCATION

MS IN Information Systems University of Maryland Baltimore County, MD (GPA: 3.76/4.00) MAY 2020 BE IN Computer Technology University of Nagpur, India (GPA: 3.3/4.0) MAY 2017 TECHNICAL SKILLS

• Databases: Oracle 11g, MySQL, SQL, PL/SQL, MongoDB, Microsoft SQL Server, MySQL.

• ETL and BI Tools: MicroStrategy, Tableau, Qlik sense, Google Analytics.

• Programming Languages: Python, Java, C, C++, HTML, CSS, Wordpress, Wix, XML.

• Tools: Power View, Excel, MS Power Point, Access (MSOffice), Visual Studio, Weka, Jupyter.

• Statistical Analysis: Logistic regression, Neural Networks, polling algorithm.

• Data visualization tools: Tableau, google analytics, MS Viso, Power BI. WORK EXPERIENCE

Data Analyst Index Analytics LLC. SEP’2019 – Current

• Designed company standard template and established security matrix to discuss access levels with client.

• Performed ETL utilizing Script Editor in Qlik Sense tool to combine, clean and transform data from various heterogeneous sources.

• Constructed star schema to illustrate specific entities, attributes, and relationships involved in a business function.

• Visualized data to show analysis by implementing dynamic dashboards and KPIs improving analysis by 20%.

• Constructed project guidelines and best practices for future reference.

• Mentored and guided a team of 6 interns ensuring all were able to learn assigned Qlik Sense exercises. Systems Engineer Vijeet Sales and Services. JUN’2017 – JUN’2018

• Installed new versions of database management systems.

• Maintained and updated the existing systems.

• Developed, organized and tested back-up and recovery plans.

• Worked in Tableau to create interactive dashboards.

• Monitored performance to get faster responses from the database. ACADEMIC PROJECTS

Fairness of machine learning algorithms JAN’2020 – Current

• Evaluated protected features and fair features, created two separate classifiers first for the fair features and second for the complete dataset.

• Used logistic regression to train the fair features, utilized the features of neural networks to train the entire dataset.

• Matched users having similar probabilities based on the output of the logistic regression but of different gender. Compared the result with actual dataset. Found the disparity between the two, found the fairness of the algorithm. Flight Delay prediction AUG’2019 – DEC’2019

• Utilized python libraries to apply three machine learning algorithms to this dataset after cleaning and preprocessing the data.

• First applied regression which gave us an accuracy of 71%, then applied neural networks which gave us an accuracy of 81%.

• We then leveraged the polling algorithm which is an ensemble algorithm to compare the outputs of the previously applied algorithms and get a cumulative output for a better accuracy.

• Finally, we obtained the accuracy of 83.4% utilizing our ensemble algorithm. Analysis of yelp’s dataset and providing ways to improve number of reviews JAN’2019 – MAY’2019

• Acquired access to yelp’s database. Converted json files into csv files to make data handling easier.

• Looked at different aspects that make a user leave a review and suggested changes to yelp! as well as the business owners to increase reviews on their sites.

• Best possible way to make people leave reviews was to provide some incentive for them. Paper Published:

Business Intelligence: Concepts and Explanation

• Paper dealt with data management challenges that companies with a high number of clients face daily, and an effective method to solve such problem.

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