Tableau Advanced: Master Tableau in Data Science

Master groups and sets in Tableau to boost efficiency; learn creation, differences, static and dynamic sets, set combinations, and parameter-driven control through a live business problem.

Explore Tableau advanced by joining the overview and financials tabs on ID for thousand startups, assessing 2015 revenue, expenses, and growth for venture investments, and organizing with year folders.

Learn to create and edit groups in Tableau to combine years and industries, and understand how grouping affects metadata and aggregations like the average across grouped categories.

Explore the difference between groups and sets in Tableau, create static sets, apply them for filters and color, and preview limitations that dynamic sets address.

Use dynamic sets in Tableau to replace static sets, creating a top startups by growth dynamic set and a high revenue set for a revenue and expenses scatterplot.

Learn how to combine sets in Tableau by using sets based on the same dimension, then create a dynamic target quadrant to show high revenue with low expenses.

Explore how to control dynamic sets with parameters in Tableau, adjusting growth, revenue cut off and expenses cut off to update treemaps and dashboards in real time.

Create a Tableau dashboard called startup quadrant by combining revenue, expenses, and growth in a single scatter plot, using sets, color, and shapes to highlight top growth startups for executives.

Explore advanced Tableau dashboard techniques by refining visuals: adjust scatter plot size and color consistency, add reference and drop lines, and craft informative tooltips for executive presentation.

Create groups and dynamic sets to control metadata and visuals in Tableau. Compare sets, use parameters and formulas to design powerful dashboards.

Optimize heavy machinery utilization at a coal terminal using data science in Tableau, mastering table calculations from basics to stacking advanced calculations.

Analyze coal terminal utilization using Tableau to identify reclaimers' maintenance needs at the Dalrymple Bay coal terminal, using idle capacity and eight-hour averages to predict future underperformance.

Master multiple left joins in Tableau by building a dummy timeline column, joining machine tabs on date time, and pivoting metadata for a unified data view.

Explore advanced table calculations in Tableau, using quick table calculations, running total, and compute down to measure idle capacity by machine from nominal capacity and actual tonnes.

Learn how to save Tableau table calculations by converting a table calculation to a calculated field, flip the axis to positive, and implement a 10% reference line for intuitive analysis.

Explicitly specify the direction of computation for table calculations in Tableau by selecting compute using with date time and machine fields to ensure correct results across swaps.

Learn to write your own table calculations in Tableau, starting from creating a first table calculation, exploring formulas like index, lookup, running sum, and window-based moving averages.

Apply an eight hour moving average to idle capacity data in Tableau using window average across the date time field to identify machines needing maintenance beyond a 10% threshold.

Apply quality assurance to table calculations in Tableau by validating moving averages, handling missing data, and excluding incomplete windows to deliver accurate maintenance insights.

Use Tableau to analyze moving averages and trend lines, flag underperforming machines, and forecast maintenance needs by revealing capacity spikes above the 10% line and planned capacity sacrifices.

Learn to build a Tableau storyline by turning an analysis chart into a dashboard, tailoring tooltips, axis formatting, and machine-specific sections for executive stakeholders.

Explore the executive report in Tableau: analyze idle capacity for five machines against a 10% threshold using moving averages, identify maintenance needs, and publish for executive review.

Explore advanced data preparation and analytics in Tableau, including box plots, trend lines, forecasting, data source level filter, blending, and handling large data sets for masterful data science insights.

Explore advanced data preparation and analytics in Tableau using ABS retail and demographic data, competitor research, and a state-level assessment of New South Wales, Queensland, Victoria, and Western Australia.

Advanced Tableau course teaches building box plots from pivoted data, visualizing net profit margins by state, and using the analytics pane to derive insights.

Analyze box plots by identifying quartiles, medians, interquartile range, and outliers, compare distributions and variability, and infer business environment insights from Tableau visuals.

Learn to prepare and analyze large data sources in Tableau by cleaning an ABS data set, hiding unused columns, and using data source filters to speed pivots.

Learn to pivot columns and split data in Tableau, turning turnover information into separate state and industry fields for ready-to-analyze data.

Create a month-based turnover visualization in Tableau, analyzing industries by state with independent axes to reveal seasonal patterns like December peaks and February dips.

Apply data source filters to remove unused categories at the source, so only clothing retailing data is analyzed across all worksheets, and filter dates from 2000 onward to speed processing.

Learn to apply and customize trend lines in Tableau, filter by state, compare state-level trends, and plan to normalize by population to estimate per capita consumption.

Master data preparation in Tableau with the data prep exercise on Australian population statistics by state and gender, learning pivot, split, and data source filters on quarterly ABS data.

Master advanced timeseries blending in Tableau by linking a primary industry data source with a secondary demographics dataset using custom year and quarter relationships, a left join, and March filtering.

Learn to create a cross-dataset calculated field in Tableau by dividing turnover by population to measure sales per capita, using blends, aggregations, and normalization for state comparisons and insights.

Learn to apply Tableau's forecast tool, incorporating trend and seasonality to predict future sales per capita, adjust forecast length and prediction intervals, and customize models.

Learn to present a data story in Tableau by building a storyline with dashboards, box plots, and sales per capita with forecast, and deliver an executive presentation.

Master Tableau animations by building your first animated dashboard that combines five dimensions in an ergonomic, intuitive layout, enabling seamless insights with practical tips and animation hacks.

Create an animated Tableau dashboard showing world population trends over 50 years, with fertility, life expectancy, and country-level drill downs using World Bank data.

Edit blending relationships in Tableau by connecting country metadata with population, fertility rate, and life expectancy data, aligning on year and country code, and building pivots for animation.

Learn to build an advanced Tableau visualization by blending a population dataset, sizing bubbles, coloring by region, filtering by year, and adjusting marks and transparency for country-level insights.

Add animation in Tableau by moving year to the pages shelf to create a time-based timeline that animates fertility rate, life expectancy, population, and region color across years.

Learn how to sort by a column from a secondary data source in Tableau by using default properties and a manual sort to reveal blended data clearly.

Explore advanced animations in Tableau using the show history feature to trail country trajectories and drill down into individual histories, comparing fertility rate, life expectancy, and population trends.

Finalize the world demographics dashboard by embedding the animation, optimizing the control panel and legend, adjusting fonts and tooltip, applying the workbook theme classic, and preparing for executive presentation.

Explore level of detail calculations that integrate data from multiple granularity levels within a single data set, revealing both granular detail and the bigger picture.

Identify how to recreate a regional profit visualization in Tableau using state colors for average city profit and circle sizes for each city's profit contribution using level of detail calculations.

Prepare a Tableau workbook by joining orders and breakdowns on order_id, validate row counts, and build a geography hierarchy (state, city, country, postal code) before saving.

Learn how aggregation and granularity interact in Tableau, using measures and dimensions to control the level of granularity, and see how adding state or city dimensions changes totals.

Explore the three types of level of detail calculations in Tableau—include, exclude, and fixed—and learn to perform city-level calculations and aggregate them at the state level without changing the visualization.

Explore include level of detail calculations in Tableau by summing city profits and averaging them across states, while keeping the visualization at state level.

Learn how the attr() function in Tableau returns a single value when values are unique across rows, or a star when they vary, with city, country, and region examples.

Explore exclude type 2 LOD in Tableau to move state-level profit to city granularity, using city, state, and country dimensions as part 1.

Use exclude LOD calculations in Tableau to bring state profit into city-level visuals, compute each city's share of state profit, and normalize comparisons across states with color and size cues.

Learn how to use fixed lod in Tableau by explicitly specifying country, state, and city to compute city-level profit and compare it to include and exclude methods.

Finalize a dual-axis Tableau visualization by combining city and state maps, using level of detail calculations to compare average city profit across states, and refine tooltips.

Explore custom polygons, map backgrounds, and images on maps in Tableau, and calculate distances between points while tackling two business challenges using polygon data.xls, New York stores, and coffee.png.

Learn how to draw custom polygon shapes in Tableau by using x and y coordinates, path order, and unique shape IDs to create polygons such as rectangles, pentagons, and triangles.

Learn to visualize meeting room utilization in Tableau by mapping floor plans as polygons with background images, blending two data sources to compare booked versus actual usage across two floors.

Explore building interactive floor views in Tableau by using a floor parameter to switch backgrounds and a utilization parameter to compare booked versus actual usage, enhancing dynamic visualizations.

Explore the drawing tool for Tableau to generate coordinates from images or maps as background images, creating points, paths, or polygons and exporting data for use in Tableau.

Master Tableau in data science by using custom images as map marks to visualize Manhattan coffee stores with turnover and months open, and identify half-mile gaps using logos as shapes.

Calculate store distances using latitude and longitude with the great circle formula, joining the data to itself to find minimum distances and flag those over 0.5 miles on a map.

Learn to customize Tableau maps with Mapbox, design Mapbox Studio styles, and integrate them as map services in Tableau using third-party and WMS options.

Master Tableau polygon drawing with coordinates and paths, create map backgrounds from images, blend data, use parameters and calculated fields, explore custom shapes, distance calculations, and Mapbox integration.

Master advanced Tableau techniques across data, conceptual, and visualization skills, including joins, groups, sets, table calculations, data preparation, time series, blending, and animations.

Celebrate completing this course and reflect on the journey as you enjoy Tasmania, Cradle Mountain and Crater Lake, then rate this five star course to help others, happy analysing.