Social Buzz Job Simulation - Content Trend Analysis

Overview

This project was part of a data analytics job simulation, where I analyzed content trends and engagement for a hypothetical social media client, Social Buzz. The objective was to extract insights and make strategic recommendations for content optimization and user engagement.

Objectives

• Conduct data cleaning and modeling on multiple datasets.
• Identify the top 5 content categories by popularity.
• Provide recommendations for maximizing user engagement based on data-driven insights.

Folder Structure

SocialBuzz_JobSimulation/
│
├── README.md
├── Client_requirementrs/ Data_Analytics Client Brief
├── RawData/
│   ├── Content.csv
│   ├── Reactions.csv
│   └── ReactionTypes.csv
├── DataModel.pdf
├── presentation/
│   └── Presentation.pdf
│   └── video_link.txt (optional)
├── Cleaned_Final_Data.xlsx

Datasets

The project involved seven datasets, containing information on:

1. User interactions - Types of reactions and timestamps.
2. Content metadata - Categories, types, and unique IDs.
3. Reaction scores - Sentiment scores associated with reaction types.

Process

1. Data Cleaning: Removed duplicates and missing values to ensure data quality.
2. Data Modeling: Merged datasets to create a unified dataset for analysis.
3. Data Analysis: Identified top content categories, most common reactions, and trends in user engagement.
4. Visualization: Created visualizations to communicate findings effectively.

Key Insights

• The top 5 content categories were: [Category 1], [Category 2], [Category 3], etc.
• [Insight on Reaction Type]: Example - “Photos received the most positive reactions.”
• [Insight on Posting Trends]: Example - “March had the highest volume of posts, suggesting a seasonal trend.”

Tools and Technologies

• Python for data manipulation and analysis
• SQL for data extraction and merging
• Excel for quick data checks and calculations
• PowerPoint for presenting findings to stakeholders

Deliverables

• Data Cleaning Scripts: Python scripts used to clean and preprocess the datasets.
• Analysis and Visualizations: Jupyter Notebooks with the analysis code and visualizations.
• Presentation: PowerPoint slides summarizing key insights and recommendations.

Recommendations

1. Content Focus: Increase the volume of posts in the top-performing categories to drive user engagement.
2. Posting Strategy: Capitalize on high-activity months to boost content reach.
3. User Interaction: Explore further engagement options around high-interest reaction types.