Inari Coleman's
portfolio

“Data really powers everything that we do.” — Jeff Weiner. @Inari Coleman

Google Merchadise Store:
Marketing Strategy

The Google Merchandise Store is an online retail store that sells official Google-branded merchandise. In this project, I created a marketing strategy for the Google Merchandise Store by utilizing Google Analytics to gain insights into customer behavior and preferences and performed data cleaning and data visualizations using Google Sheets.The ultimate goal of the project was to create a successful marketing actionable recommendations that result in increased sales and brand awareness for the Google Merchandise Store.

Exploratory Data Analysis:
Asian Hate Crime

The project aims to analyze the rise of anti-Asian hate crimes in New York City during the COVID-19 pandemic, with a focus on helping the incoming mayor, Eric Adams, create a safer environment for everyone. To accomplish this, the project uses the "NYPD Hate Crimes" dataset from Open Data NYC, which contains hate crime incident reports from January 2019 to September 2021. The dataset was cleaned and prepared, and the analysis focused on identifying trends in hate crimes against the Asian community during the pandemic.

Netflix - Exploratory Analysis

The objective of the project was to perform exploratory data analysis (EDA) a Netflix dataset consisting of listings of over 8000 movies and TV shows including details such as cast, directors, ratings, release year, duration, and etc. I used Python was throughout the project, with various libraries such as Pandas, NumPy, and Matplotlib utilized to manipulate, clean, and visualize the data.

NYC 311 Service Requests
SQL exploration

The project aimed to analyze issues affecting New Yorkers by using data from Open Data NYC's 311 service request hotline. With a dataset consisting of 28 million rows of information from 2019-2021, the project's main goal was to identify the most and least frequent 311 complaints, the government agencies that received the most service requests, the months and years with the highest service request volumes, and the most efficient agencies in resolving service requests.To achieve this goal, the project used a set of guiding questions to analyze and clean the data using BigQuery.