Table of Contents

Social Buzz Case Study


Currently nearing the completion of my Google Data Analytics certificate, I have selected the Social Buzz data set for a comprehensive case study. The main purpose is to exhibit my abilities in data cleaning and analysis, showcasing my mastery of the "Ask, Prepare, Process, Analyze, Share, and Act" methodology.


About Social Buzz

Social Buzz is a content-centric social media platform founded by former engineers from a major social media conglomerate. Emphasizing anonymity and diverse user reactions, the platform prioritizes trending content over individual identities. With over 500 million monthly active users, Social Buzz has experienced remarkable growth, necessitating expert guidance to manage their scaling process effectively. Due to their rapid growth, they are asking Accenture to audit their big data practice, give recommendations for a successful IPO, and analyze their content categories– they are interested in the analysis of their content categories, but to also find out what their most “popular” ones are based on a qualitative scoring system.

Questions for Analysis

Business Task

The task focuses on analyzing the most popular content categories on the social media platform to identify optimal data practices for handling the substantial daily data influx. This analysis allows for the recognition of opportunities to leverage.


Data Set

The data source for our case study is Accenture. The dataset is stored on Forage to complete an analysis, presentation, and with the final step to present the findings.

Data Credibility

It’s a small sample size of over 25,000 reactions, labeled by 16 qualitative reaction types. The data set focuses on 16 content category types. Social Buzz’s platform has over 100 categories of content, which might allow for better analysis if there are overlapping and duplicate category types once the entire data structure is reviewed.

Importing Data Sets

For this case study, I will be using Excel to aggregate the data. So I import the CSV files into a workbook to review and begin.

Data Sets

The data sets provided are within 3 CSV files, but for my analysis I will need to aggregate them into 1 with my insights on the data on a separate sheet. The originating CSV files were Reactions, Content, and ReactionTypes, which I imported into Excel to be able to aggregate them properly. I renamed my final aggregate data to “Aggregate Data” and had a separate “Insights” tab for my final findings.


Cleaning Data Sets






First Insights

While this was a small data set, there does seem to be an opportunity to streamline the content categories– for example, removing “dogs” as its own category and rolling those in under “animals”. While there are similar topics like cooking, healthy eating, food, and veganism are related, they potentially could have vastly different target demographics. For instance, veganism does incorporate food but it also encompasses lifestyle.



Visual Data Telling




Animals and science are among the top 5 categories based on popularity score, showing that there is a high engagement with “factual” and “real-life” content. Additionally, healthy eating, along with other food and related topics such as culture, cooking, and travel, score high in popularity. This suggests a strong interest in food and lifestyle content among Social Buzz’s user base.

Actionable Items