KALPITA HARIHAR RAUT
+1-202-***-**** firstname.lastname@example.org linkedin.com/in/kalpita-raut github.com/kalpitaraut24 SUMMARY
Strategically minded data analyst with experience in interpreting, analyzing and reconciliation of large datasets obtained from a variety of sources; skills involve development and enhancement of the existing processes and methods to meet both internal and external analysis requirements, strong written and verbal communication skills with the ability to translate complex problems into simpler terms, and effectively influence both peers and senior leadership. EDUCATION
University of Maryland, College Park May 2020
M.S in Information Management (STEM Certified Degree), Specialization– Data Analytics- GPA 3.9/4 University of Mumbai, India 2013 - 2017
Bachelor of Engineering (Computer Science)- GPA 4/4 TECHNICAL SKILLS
Languages: Python(Pandas, NLTK, Spacy, NumPy, Scikit, Seaborn, Matplotlib, Plotly, TensorFlow), R, SQL, Excel Technologies: Hadoop, HIVE, Pig, Spark, Office 365, Alteryx, Power-Query, Moodle, Sharepoint, AWS, Visual Studio, Microsoft SQL Server, Tableau, Microsoft Power BI, Spotfire, Jigsaw, Google Analytics, WEKA, Teradata, Salesforce, A/B Testing, SAS Certificates: Specialization in Python: University of Michigan-Ann Arbor by Professor Charles Severance Certification in Programming in Python (License: 39DMRF4Q5U73), Certification in Python Data Structures (License: 7RWX6YA6S7F4) WORK EXPERIENCE
Data Analyst, Bozzuto Group, Maryland 2019 - Present
Built optimized data models to support dashboard requirements, integrating data from data warehouses by working closely with estimators on data model construction and data analysis. Currently, predicting tenant loyalty based on user demographics to analyze which tenants are liable to continue their lease and what factors influence their decision.
Wrangled 100 GB structured and unstructured data and performed ETL operation to build a data warehouse using Microsoft SQL Server.
Provided internal clients with accurate information that increased the planning efficiency by 60% .
Assisted Finance team in estimating and forecasting complex budgets by building financial models, which involved acting as the liaison between subcontractors and other key stakeholders such as the Accounting and Legal departments involved in the deal structuring process.
Designed and built statistical analysis models by performing quantitative and qualitative analysis using Python and PowerQuery boosting contract confirmation by 17%.
Proposed solutions in an agile environment to improve operational efficiencies, leading to a 15% reduction in costs saving the company $32k/year.
Improved the existing reporting dashboards and the functionality of planning tools using PowerBI that reduced data processing time by 95%.
Teaching Assistant, College of Information Studies, University of Maryland 2019 - Present
Collaborated with instructor in to facilitate both supervised and unsupervised learning, risk analysis and business simulation models and data-informed control approaches for a class of 70+ students.
Engaged one-on-one with students with behavioral problems in both classroom and extracurricular settings, resulting in an improvement of more than 20% in their grades.
Graduate Assistant, College of Information Studies, University of Maryland 2018 – 2019
Researched and summarized over 1500 articles referencing Educational Data Mining, Learning Analytics and Data Driven Decision Making to create a database on Microsoft Access.
Performed statistical analysis, computed complex measures in Excel using VBA Macros. Harnessing this data for the National Science Foundation(NSF) Research Proposal grant worth $2M PROJECTS
Predicting Listing Price & Host Loyalty
Implemented a model to predict listing price and host loyalty by performing feature engineering.
Performed time series analysis of around 120GB data from the past 2 years by using Machine Learning algorithms such as Regression, Decision Trees and Random Forest, Support Vector Machines in R
Secured first position amongst a cohort of 50 students/professionals, with a model accuracy of around ~85%. Social Network Analytics based on Amazon Reviews
Predicted helpfulness of customer reviews by web-scraping data using API and using algorithms such as Naïve Bayes Classification in Python (Jupyter Notebook).
Computed the sentiment analysis of the reviews using Linguistic Inquiry and Word Count (LIWC) that calculated sentiment score for each emotion with an accuracy of 68%
Hypothesis Testing – Topic : Which gender is better at multitasking?
Analyzed the hypothesis using Multivariate Analysis (MANOVA) in R.
Performed calculations to find effect size(Eta-squared method) and power of the analysis with an accuracy of 95% using SPSS tool.