Overview

Data Wrangling in Python

This represents the process of cleaning, transforming, and organizing raw data into a usable format using Python libraries such as Pandas and NumPy. It involves handling missing data, filtering out unnecessary information, and restructuring data for analysis.

SQL for Data Retrieval

This refers to using SQL (Structured Query Language) to extract, manipulate, and manage data from relational databases. Key operations include writing SELECT queries, JOINs, subqueries, and aggregating functions to gather insights from large datasets.

Data Visualization with Power BI and Tableau

This signifies the creation of interactive and insightful visual reports and dashboards using Power BI. It includes importing data, building visualizations, applying filters, and sharing dashboards to provide stakeholders with clear and actionable insights.

Exploratory Data Analysis in R

This placeholder involves the initial exploration of datasets using R to understand their underlying patterns, structures, and relationships. Techniques include summarizing statistics, visualizing distributions, and identifying outliers, often using libraries like ggplot2 and dplyr.

Portfolio Projects

Data Cleaning in SQL

In this project we take raw housing data and transfor it in SQL Server to make it more usable for analysis.

COVID 19 Data Exploration

In this project we use SQL Server to explore global COVID 19 data.

Tableau Dashboards

Tableau Dashboards for projects on COVID 19, Financial Forecasting, and Fantasy Football.

Movie Correlation with Python

In this project we look at what variables effect the gross revenue from movies.

Amazon Web Scraper with Python

In this project we scrape data from Amazon to analyze price data for products.

PowerBI Dashboard

Power BI dashboard for visual representation of data, created by importing, cleaning, modeling, and visualizing data, then sharing it for interactive, data-driven decision-making.