Real time Twitter Analytics Power BI Project
Introduction
This report is about a project revolving around Twitter data analysis using Power BI. In these given tasks I tried to visualize tweet performance with various metrics and filters.
Background
The objective was to analyze Twitter data through Power BI to identify valuable patterns and trends within tweets using 3-4 types of graphs and filters.
Learning Objectives
I become familiar with the interface and data modeling in Power BI.
Prepared visual representations from Twitter’s real-world data.
Use filtering and DAX formulas which I learned during the online training videos, and provide the insights needed.
Activities and Tasks
Scatter Chart Analysis:
Graphically represented media engagements and media views.
Implemented filters on replies to tweets more than 10, engagement rate greater than 5%, and specific tweet time (6 PM to 11 PM IST).
Odd Date Tweets:
Had to filter tweets corresponding to odd dates using a DAX formula.
All filtered tweets also had to exceed 50 words.
Highlighting High Engagement Tweets:
Custom measure to highlight all tweets with engagement rate greater than 5 percent was created.
Skills and Competencies
Power BI filters and scatter chart visualizations.
DAX formulas for date and word count filtering.
Use of data models and data cleansing.
Challenges and Solutions:
The time filter is in IST: Resolved by DAX for converting UTC to IST.
Logic of word count: Applied DAX with formula based on LEN and SUBSTITUTE.
Outcomes and Impact:
Realized enhanced understanding of data visualization with practical applications.
Strengthened belief in building custom filters and extracting filters using Power BI.
Conclusion:
The project provided me with opportunities to understand the interface of Power BI deeply, especially in filtering, analyzing, and visualizing Twitter data. I am more confident in using Power BI for future analysis.