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Data Analyst

Location:
Dallas, Texas, United States
Salary:
80000-100000
Posted:
December 07, 2018

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Resume:

NAVEENA GADDE

469-***-**** ac7wh3@r.postjobfree.com www.linkedin.com/in/naveenagadde 7220 McCallum Blvd Dallas TX 75252 SUMMARY

A highly motivated and passionate analytics graduate with experience working on various projects in the retail, finance, digital and e-commerce fields and with an ability to crunch data and provide actionable insights using various analytical tools and data science techniques

EDUCATION

MS in Business Analytics, University of Texas at Dallas, Richardson, Texas GPA: 3.75 MAY 2018 B. Tech in Civil Engineering, National Institute of Technology, Calicut, India GPA: 7.90 JUN 2015. TECHNICAL SKILLS

BI/ Visualization Tools: Tableau, Excel, Web Intelligence, Qlikview, IBM Cognos, Power BI, Adobe Analytics Data Analysis Tools: SQL, R, Python (scikit-learn, pandas, numpy, keras), SAS, Scala, Hive, Alteryx, STATA Databases: SQL Server, MySQL, DB2, MS Access, Oracle Statistical Skills: Regression, Hypothesis Testing, ANOVA, Pooled OLS, Fixed and Random effects Machine Learning Skills: Classification Methods, Clustering, Neural Networks, Recommender Systems, Ensemble Methods, Support Vector Machines, A/B testing, Principal Component Analysis Certifications: Deep Learning in Python from Data Camp, Text Mining and Sentiment Analysis in R from Data Camp, Python Pandas Foundations from Data Camp, Supervised and Unsupervised Learning in R from Data Camp, Customer Analytics & A/B Testing with Python-Data Camp UTD Graduate Certificate in Business Intelligence and Data Mining WORK EXPERIENCE

Data Analyst, Academic Partnerships, Dallas JUN 2017 - NOV 2017, OCT 2018 - Present

Predicted the likelihood of a student turning into a potential opportunity by building a logistic regression model. The predictions made helped the marketing team in significantly reducing their marketing costs by 80%. (R)

Initiated the process of tracking KPIs that impact the business by developing an interactive dashboard that visualizes the effect of operational process changes over client’s key conversion rates. (Tableau)

Helped improve retention strategies for clients by analyzing their existing data, identifying key trends, and generating key business reports. (SQL, R)

Reduced client’s performance report generation time by automating the process and including key metrics and conversion rates that could later be used for further analysis to improve the services offered. (Excel, Tableau) ACADEMIC PROJECTS

Text Mining and Sentiment Analysis (R)

Analyzed Airbnb customer reviews in Boston area using R’s qdap and tidytext packages to extract insights and establish the popular sentiment using text mining and sentiment analysis.

This analysis helped in identifying what makes a positive or negative rental experience for a property. Predictive Modeling (R)

Predicted the likelihood of loan customer behavior (Active vs Default) of Lending Club by analyzing and processing the loan related data and by building a classification model.

Implemented AdaBoost algorithm to boost the performance of the classifier with an improved accuracy of 85%.

The project findings later helped in profiling the target customers and strategizing the campaigns to attract them. Marketing Analytics for an F&B Product (SAS, Tableau, Excel)

Developed an understanding of the key attributes that played an important role in customer preference for an F&B product by building a choice model with an accuracy of 85%.

Identified and profiled target customers for the product by clustering customers based on key metrics. The process helped in better understanding customers and can later be used for enhancing product portfolios Deep Learning (Python)

Classified the objects appearing in an image as cat and non-cat by training a deep neural network with multiple layers, achieving an accuracy of 80% using Keras.

A/B Testing for a Restaurant Review Application (Python)

Determined the effectiveness of a new ad display algorithm on sales for a restaurant review application by designing an A/B experiment and analyzed its effectiveness using regression analysis with an accuracy of 85%.



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