The program focuses on using Tableau for data work: connections, transformations, and dashboard creation. Emphasis on clear visualization and data storytelling.
Language of course:
Ukrainian
Format of the event:
Online
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Course overview
Description generated based on course syllabus and open data.
This course focuses on how to use Tableau for data analysis and visualization in daily work: from connecting multiple sources and preparing data to publishing interactive dashboards. Lecturer: Yevhenii Mianovskyi (Data Analyst at SQUAD), 5+ years in Home Security analytics with 40M+ active users and experience handling large data streams (up to 1B records per hour).
Lecturer and Tableau application context
Yevhenii Mianovskyi specializes in analytical support for product feature development, works with large-scale datasets, and turns metrics into clear visualizations for teams and stakeholders.
Who Tableau for data analysis and visualization fits, and who it doesn’t
Suitable for
- Analysts and data specialists who need quick metric overviews and flexible BI dashboards.
- Product/Project managers tracking metrics, trends, and insights through visualization.
- Professionals combining multiple sources (CSV, Excel, databases, services).
Not optimal for
- Tasks requiring complex ML models or full data pipeline engineering (better with Python/R/SQL platforms).
- Workflows performed exclusively in code without a GUI.
From problem to outcome in Tableau for analysis and visualization
- Unshaped datasets → transformations, joins, aggregations, and calculated fields.
- Slow metric review → dashboards with filters, maps, charts, and quick views.
- Scattered insights → data storytelling, highlighting what matters, and cross-chart interactions.
- Local files → publishing selected outputs to Tableau Public for a portfolio sample.
Comparison with alternatives for data analysis and visualization
- Excel/Google Sheets: easy start and familiar UI, but limited for complex dashboards and larger volumes.
- Power BI: strong Microsoft ecosystem integration; Tableau is often more flexible in visualization and interactivity.
- Python/R (code): full control and customization; Tableau offers speed of build and interactivity without coding.
- SQL + custom solutions: strong backend; Tableau covers the interactive exploration and visualization front end.
Learning outcomes in Tableau for data analysis and visualization
- Connecting to multiple data sources and common connectors.
- Data transformations, calculated fields, and type handling.
- Building interactive visualizations and dashboards with filters and actions.
- Applying data storytelling to emphasize key insights.
- Publishing a sample analytical report to Tableau Public.
Course Description
Lecturer
Eugene Myanovsky
Data Analyst at SQUAD, 5 + years of experience as a data analyst at a company that creates products in the field of Home Security
- accompanied by analytical reports products that have 40 + million active users- works daily with huge amounts of data: more than 1 billion records in tables every hour- the main professional direction - analytical support for projects aimed at expanding the functionality of the product
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