Unlocking Insights: Data Analysis and Visualization - Data Engineering

Building on our previous exploration of architecting a data warehouse, we now delve into unlocking the insights from our data with data analysis and visualization. In this continuation of our data engineering process series, we focus on visualizing insights. We learn about best practices for data analysis and visualization, we then move into an implementation using a code-centric dashboard using Python, Pandas and Plotly. We then follow up by using a high-quality enterprise tool, such as Looker, to construct a low-code cloud-hosted dashboard, providing us with insights into the type of effort each method takes.

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Note: This is an online event hosted on Bevy. Please register using the event page.

Agenda:

1. Introduction:

•    Recap the importance of data warehousing, data modeling and transition to data analysis and visualization.

2. Data Analysis Foundations:

•   Data Profiling: Understand the structure and characteristics of your data.
•   Data Preprocessing: Clean and prepare data for analysis.
•   Statistical Analysis: Utilize statistical techniques to extract meaningful patterns.
•   Business Intelligence: Define key metrics and answer business questions.
•   Identifying Data Analysis Requirements: Explore filtering criteria, KPIs, data distribution, and time partitioning.

3. Mastering Data Visualization:

•   Common Chart Types: Explore a variety of charts and graphs for effective data visualization.
•   Designing Powerful Reports and Dashboards: Understand user-centered design principles for clarity, simplicity, consistency, filtering options, and mobile responsiveness.
•   Layout Configuration and UI Components: Learn about dashboard design techniques for impactful presentations.

4. Implementation Showcase:

•  Code-Centric Dashboard: Build a data dashboard using Python, Pandas, and Plotly (demonstrates code-centric approach).
•  Low-Code Cloud-Hosted Dashboard: Explore a high-quality enterprise tool like Looker to construct a dashboard (demonstrates low-code efficiency).
•  Effort Comparison: Analyze the time and effort required for each development approach.

5. Conclusion: 

• Recap key takeaways and the importance of data analysis and visualization for data-driven decision-making.

Why Join This Session?

•  Learn best practices for data analysis and visualization to unlock hidden insights in your data.
•  Gain hands-on experience through code-centric and low-code dashboard implementations using popular tools.
•  Understand the effort involved in different dashboard development approaches.
•  Discover how to create user-centered, impactful visualizations for data-driven decision-making.

This session empowers data engineers and analysts with the skills and tools to transform data into actionable insights that drive business value.

Click to Register and Join the session here.