Projects
Python: Text File Analyzer Program
The "Text File Analyzer" is a Python program that allows users to analyze text files through a command-line interface. It imports a 'TextAnalyzer' class from 'text_analyzer.py' to provide various analysis options, including searching for specific terms, calculating word frequencies, & generating summary statistics. 'TextAnalyzer' is a custom class encapsulates that file handling, formatting, and analysis methods, making it a versatile tool for extracting insights from text files. This program is valuable for content analysis, research, and data-driven decision-making, streamlining the process of extracting and presenting information from source files.
Python + Tableau: Data-Driven Insights for Disney's Strategic Decisions: A Comprehensive Marketing Analytics Study
This project explores Disney's marketing and strategic decision-making through various data analysis techniques. It includes data visualization using Tableau to showcase Disney's cinematic history, exploratory data analysis to examine movie performance, customer segmentation for Disney's park visitors, conjoint analysis for a new Hawaiian hotel, strategic recommendations for Disney's theme parks, net income forecasting, customer behavior classification, and A/B testing for popcorn bucket sales. The project aims to provide valuable insights and recommendations to enhance Disney's future success across various aspects of its business.
Data Mining + Machine Learning in R: Analysis of Airbnb Data
This data science project, conducted using R and R Studio, encompasses a comprehensive exploration of the New York City Airbnb market. It encompasses essential data preparation and exploration steps, including data preprocessing and feature engineering, as well as the generation of summary statistics and data visualization. Machine learning techniques, including Multiple Linear Regression, k-Nearest Neighbors classifier, Naive Bayes classifier, and k-Means clustering, are applied to predict rental prices, categorize listings, and cluster neighborhoods. The outcomes of this project provide valuable insights for stakeholders, serving as a foundational framework for urban market analysis.
Bushwick Real Estate Insights: Power BI Analysis
This project utilizes Power BI to conduct a detailed analysis of the residential real estate market in Bushwick, Brooklyn. Through interactive visualizations and key performance indicators (KPIs), this project provides valuable insights into sales volumes, average prices, market growth trends, and revenue potential within the Bushwick neighborhood.
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By leveraging Power BI's capabilities, this project offers a comprehensive examination of the Bushwick real estate landscape, comparing its performance against neighboring areas and highlighting investment opportunities for stakeholders. The analysis is presented through an executive summary, demonstrating proficiency in data analysis, visualization, and strategic decision-making for real estate business ventures.
Exploratory Data Analysis in R: Austin 311 Service Requests
This data science project involved utilizing R programming to conduct exploratory data analysis on the 311 service requests dataset for Austin, TX. Tasks included data cleansing, date handling, and visualization to extract insights about service request patterns, seasonality, and common request types. The project showcases proficiency in data exploration and visualization techniques for informing business analytics decisions.
Data-Driven Predictive Analytics in R: Salaries and Song Preferences
​This data science project encompasses two tasks utilizing R programming. Firstly, it predicts high-tech worker salaries using techniques like simple linear regression and correlation analysis. Secondly, it employs k-Nearest Neighbors to predict song preferences based on song attributes. The project demonstrates proficiency in data mining, modeling, and evaluation techniques, providing valuable insights into salary determinants and song likability.
Market Basket Analysis in R: Uncovering Grocery Shopping Patterns
This data science project leverages R programming to explore the Groceries dataset, identifying association rules that offer valuable insights for a supermarket retailer. The analysis included item frequency analysis, generation of association rules, and visualization using arulesViz. The findings provide actionable information that can aid a retailer like Star Market in optimizing product placement and marketing strategies.
Fizz-Buzz Python Challenge Solver Program
This Python program is a spin-off on the fizz-buzz challenge and was created as part of an academic assignment. The program uses a constant variable to define a range from 1 to 30 and checks each number in the range for divisibility by 2, 3, and 5. Depending on the divisibility, it prints "foo," "bar," and "baz" individually or in combination. The program utilizes both for and while loops to display the correct output order, with a separator line between the two loops.
Foundations of SQL
This project serves as an introductory journey into the world of Structured Query Language (SQL) and relational database management systems (RDBMS). It covers fundamental SQL commands and concepts in three sections:
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Absolute Fundamentals: Creating, populating, and manipulating a "Cars" table.
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More Precise Data Handling: Refining SQL skills with an "Apartments" table, including constraints, null values, and targeted operations.
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Data Anomalies and Formats: Exploring anomalies in table design and comparing data access between relational tables and file formats.
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Overall, this project aims to equip individuals with foundational SQL skills essential for effective database management.
DinoDiscover SQL: Aggregation & Visualization of Fossil Data
This database project involves SQL to aggregate data within a simplified dinosaur discovery schema. Key tasks include creating and populating tables, performing various data aggregation tasks such as counting, finding highest and lowest values, and grouping results. Additionally, the project explores data visualization by creating bar charts and scatter plots to convey insights derived from the database. Ultimately, the skills acquired in this project are valuable for making data-driven decisions in fields such as paleontology and museum management.
