Accenture-Data-Analytics-and-Visualization

A free job simulation on Forage. All credits go to Accenture. Check out the simulation here: https://www.theforage.com/simulations/accenture-nam/data-analytics-mmlb

Data analysis and visualization were performed using a Kaggle notebook to streamline and enhance the analysis process. You can find the notebook here: https://www.kaggle.com/code/emmawu155/accenture-navigating-numbers

Quick Project Summary

• Completed a simulation focused on advising a hypothetical social media client as a Data Analyst at Accenture.
• Cleaned, modeled, and analyzed 7 datasets to uncover insights into content trends to inform strategic decisions.
• Prepared a PowerPoint deck and video presentation to communicate key insights for the client and internal stakeholders.
• Data analysis and visualization were performed using a Kaggle notebook to streamline and enhance the analysis process.

Category Analysis and Visualization

This project analyzes and visualizes data related to various categories based on user reactions. The goal is to clean, process, and visualize the data to identify trends and insights, such as the top-performing categories and the distribution of scores.

Project Overview

The project involves:

1. Data Cleaning: Processing raw data to prepare it for analysis.
2. Data Merging: Combining multiple datasets to create a comprehensive view.
3. Data Aggregation: Summarizing data to determine key insights.
4. Visualization: Creating charts to represent data in a meaningful way.

Data Files

1. Reactions.csv: Contains user reactions with columns Content ID, User ID, Reaction Type, and Datetime.
2. ReactionTypes.csv: Contains details about reaction types with columns Type, Sentiment, and Score.
3. Content.csv: Contains content details with columns Content ID, User ID, Content Type, Category, and URL.

Data Processing

Steps Taken:

1. Renaming Columns:

   • Renamed 'Type' to 'Reaction Type' in Reactions and 'Type' to 'Content Type' in Content.

2. Cleaning Data:

   • Removed quotations from the 'Category' column in Content.
   • Dropped rows with missing 'Reaction Type' values in Reactions.
   • Removed 'User ID' and 'URL' columns from Content and Reactions.

3. Merging Datasets:

   • Merged Reactions with Content on 'Content ID'.
   • Merged the resulting dataset with ReactionTypes on 'Reaction Type'.

4. Aggregation and Visualization:

   • Aggregated scores for each category.
   • Grouped the lowest 10 categories under the label 'Others'.
   • Created a pie chart to visualize the proportion of scores for each category.

Analysis

Key Insights:

1. Number of Unique Categories:

   • Total count of distinct categories.

2. Reactions to the Most Popular Category:

   • Number of reactions associated with the most popular category.

3. Month with the Most Posts:

   • Identified the month with the highest number of posts.

Pie Chart

• Description: Displays the proportion of scores for each category, with the lowest 10 categories combined under 'Others'.

Dependencies

• pandas
• matplotlib

Acknowledgments

• This project was part of a free job project simulation offered by Accenture on Forage.
• Data analysis and visualization were performed using a Kaggle notebook to streamline and enhance the analysis process.