Data Analysis Business Analytics

Sibani Mohapatra

Louisville, KY / 502-***-**** / ******.***@*****.***

Eager and dedicated currently pursuing MS in Business Analytics with a solid grounding in data analysis,

statistical methods, and data visualization techniques. Possesses strong analytical skills and technical know-how

to extract valuable insights from large datasets. Enthusiastic about applying academic knowledge to real-world

scenarios and contributing to organizational success.

EDUCATION

University of Louisville Louisville, KY Expected Graduation: September 2025

Master of Science in Business Analytics

BPUT University Bhubaneswar, Odisha, India August 2008

Master in Computer Application (MCA)

Utkala University Bhubaneswar, Odisha, India August 2005

Bachelor of Science in Computer Science

TECHNICAL SKILLS

Proficient in MS-Office (Word, Excel, Power Point, Outlook, Teams)

Data Analytical Skills using IBM SPSS

Proficient in Programming/Data Analysis using Python.

Moderately skilled in data analysis, visualization using “R”.

Proficient in data analysis, visualization using Power BI.

Proficient in Excel Automation using VBA, Built-In Functions and Macros.

Structured Database Design and SQL Query Writing with query optimization.

Data quality validation by applying various Reconciling Models using Excel Pivot, ODBC DSN and SQL

Comfortable in Various Development Styles such as Waterfall and Agile.

EXPERIENCE

QA Analyst March 2011 – November 2011

Numericque Consulting Bangalore, India

“837 Medical Claims Processing”

oAccomplished all the Source (X12) to Target (SQL Server) data validation in a QA Environment.

oValidated the Referential Integrity among the Tables in a Star Schema.

oDocumented Test Cases/ Results for Audit Purpose.

“Hotel Reservation System”

oAccomplished the User Interface Testing with Test data

oValidated the Backend Data stored in MS-Access

oValidated Future Reservation/ Walk-In Reservation Test Scenarios

oValidated Final Invoice at Check-out test scenarios

Data Entry /Teaching Faculty January 2009 – June 2009

SSi Computer Education Institute Dhenkanal, India

Primarily, I was responsible for Student Data upkeeping using MS-Excel, Monthly Data Backup and Monthly report related to Delinquent Students.

Secondarily, I was working as a Teaching faculty, carried the responsibility of educating the College level students in Computer Skills and basic Software skills to carry out basic operations in Windows, MS-Office and Multimedia files.

MSBA (UofL) – Academic Projects/Hands-on:

Analyzing New York City Airbnb Data with R

Goal: To gain actionable insights into the New York City Airbnb market using R, focusing on identifying trends, patterns, and factors influencing listing prices and host behavior.

Data Source: The "Inside Airbnb" dataset for New York City (available at http://insideairbnb.com/get-the-data.html). We will primarily focus on the "listings.csv" dataset, which contains comprehensive information about Airbnb listings.

Project Objectives:

* Explore the geographical distribution of Airbnb listings using maps.

* Investigate the relationship between price and other variables

* Statistical Analysis and Modeling:

* Perform statistical tests (e.g., t-tests, ANOVA) to compare the average prices of different groups (e.g., room types, neighborhoods).

* Build regression models (e.g., linear regression, random forests) to predict listing prices based on various features.

* Evaluate the performance of the models using appropriate metrics.

* Perform sentiment analysis on the reviews to determine if positive or negative reviews have an effect on price.

* Visualization and Reporting of the findings in a clear and concise manner, highlighting key insights and actionable recommendations.

R Libraries to be Used:

* dplyr: Data manipulation and transformation.

* tidyr: Data cleaning and reshaping.

* ggplot2: Data visualization.

* readr: Reading data files.

* data.table: fast data manipulation.

* leaflet: Interactive maps.

* plotly: Interactive plots.

* sf: Spatial data handling.

* lubridate: Date and time manipulation.

* stringr: String manipulation.

* caret: Model training and evaluation.

* randomForest: Random forest modeling.

* tidytext: Text mining and sentiment analysis.

* knitr: Dynamic report generation.

* kableExtra: Enhancing tables in reports.

Potential Research Questions:

* What are the key factors that influence Airbnb listing prices in New York City?

* How do prices vary across different neighborhoods and room types?

* What is the impact of host characteristics (e.g., number of listings, superhost status) on listing prices and occupancy rates?

* How has the Airbnb market in New York City evolved over time?

* Can sentiment analysis of reviews accurately predict price changes?

* Where are the areas with the highest and lowest availability?

* What are the busiest times of the year for Airbnb in NYC?

Deliverables:

* R scripts for data cleaning, EDA, statistical analysis, and modeling.

* Visualizations and interactive maps showcasing key findings.

* A comprehensive report summarizing the analysis and insights.

* A presentation that clearly explains the findings.

American Automobile Sales Campaign Analysis and Prediction

Goal: To analyze historical American automobile sales data, identify key trends, and build a predictive model to forecast future sales for a simulated marketing campaign.

Project Scope:

* Data Acquisition and Cleaning:

* Obtain historical US automobile sales data (e.g., from government sources like the Bureau of Economic Analysis, industry reports, or publicly available datasets on platforms like Kaggle).

* Clean and preprocess the data, handling missing values, outliers, and inconsistencies.

* Transform data into a suitable format for analysis and modeling (e.g., time series data).

* Exploratory Data Analysis (EDA)

* Model Building and Training

* Model Evaluation

* Campaign Impact Analysis

* Compare predicted sales with and without the simulated campaign.

* Quantify the estimated uplift in sales due to the campaign.

* Visualize the campaign's impact on sales trends.

Python Libraries:

* Pandas: For data manipulation and analysis.

* NumPy: For numerical computations.

* Matplotlib and Seaborn: For data visualization.

* Scikit-learn: For machine learning models and evaluation.

* Statsmodels: For time series analysis (ARIMA, SARIMA).

* Prophet: For time series forecasting.

* TensorFlow or PyTorch (optional): For building LSTM models.

* Datetime: For time based data manipulation.

Contact this candidate