Complete Tableau Desktop Professional Certification Course

Explore advanced table calculations and learn techniques for solving specific business cases, including scope and direction. Apply these techniques to new datasets, with walkthroughs, discussions, and homework to solidify skills.

Explore the theory behind table calculations in Tableau, including how they run on aggregated values, and how scope and direction define boundaries and reset behavior.

Analyze car company stock prices in tableau by comparing Tesla, Ford (F), and Toyota (TM) using adjusted close data, visualizing returns on a common axis relative to a date.

Connect to the Ford, Toyota, and Tesla CSV data, practice refreshing data, and perform a union in Tableau, then rename the data source and set aliases for clarity.

Learn to compare stock performance by normalizing prices to a chosen reference date in Tableau, using a reference date parameter, weekend-aware date lists, and percentage-based visuals.

Learn to create an if statement in Tableau to capture the adjusted close price at a reference date for Tesla, then replicate it across all rows with a table calculation.

Use a window max table calculation combined with an if statement to extract a single value and replicate it across the x-axis, enabling dynamic reference date control.

Verify the scope and direction of a table calculation using specific dimensions to keep the view independent, ensuring correct results across and down with company and date filters.

Learn to compute relative stock prices in Tableau using an if statement and a table calculation, generating a relative adjusted close to a reference date and visualizing it across dates.

Demonstrate how to build a common baseline stock chart in Tableau using if statements, window max, and table calculations to compare relative performance.

Practice building two additional charts in Tableau to compare pledges, backers, comments, and funds across real 2019 projects, extracting insights and identifying top supporters.

Unite Walmart, Amazon, and Alibaba in a Tableau data source and rename fields. Use a data source filter, set a reference date, and compute adjusted close with table calculations.

Learn to edit table calculations, set specific dimensions, and compute window max and cross-tab views, then derive relative stock prices using adjusted close and if statements.

Improve data visualization skills by using table calculations to align 2019 Kickstarter campaigns on a single baseline, creating a cumulative funds chart that compares daily pledge progress across campaigns.

Union multiple project datasets from Legend of Vox Machina, Snap Maker, and Travel Tripod to visualize daily pledges, backers, and comments across projects.

Apply a running total with quick table calculation to compare funding progress charts by aligning multiple series to a common baseline using an index table calculation.

Create a common baseline by using a day number calculated with index in Tableau, compute across day and project, and place it on the x-axis.

Master running total table calculations by restricting scope to the day of date and resetting at each new project. Use specific dimensions to ensure correct accumulation and generate valuable visualizations.

Analyze Kickstarter campaign data by comparing daily pledges, growth patterns, and end surge, using charts to reveal how pre-campaign hype and campaign length influence funding.

Complete the homework assignment by building two additional charts alongside pledges, analyzing backers, comments, and funds over time to reveal which projects drew the most backers and excitement.

Explore how to build calculated fields for backers and total backers, apply day-based table calculations, and visualize Kickstarter data with line charts to compare backers, funds, and comments.

Analyze the Kiva loans dataset in Tableau to visualize funded amount over time by borrower gender. Build a running total and convert each period to a percentage of the total.

Create a running total by gender in a stacked bar chart in Tableau, converting date to quarter, filtering nulls, and applying a running total followed by a percentage of total.

Add a secondary table calculation for percent of total, set to table down, and format as a percentage with one decimal to show quarterly running totals by gender categories.

Compare ordinary percent of total with running total using funded amounts per quarter, and explore table calculations and percent-of-total visuals in stacked bar charts.

Explore how specific dimensions shape running totals in Tableau by toggling table calculations across year, quarter, and gender, and observe how order and resets affect the visualization.

Develop a running total and percentage in Tableau by sector, not by date, exploring left-to-right versus right-to-left calculations on funded amount, in preparation for the Tableau certified professional exam.

Recreate a chart using two table calculations in Tableau: running total and percent of total. Learn how sort order and data sequence affect results, and when to avoid running totals.

Build an advanced bump chart in Tableau to visualize international soccer results, tracking wins by national teams from 2000 to 2019 using a public Kaggle dataset.

Create a bump chart by deriving the winning team from home and away scores with a calculated field, then filter 2002 to 2019 and count wins by country.

Create a simple bump chart in Tableau by applying a table calculation rank across winning team, reverse the axis for top rankings, and filter non-null values to handle missing data.

Duplicate the bump chart to a dual axis, then synchronize axes. Convert the second axis to circles, adjust sizes, and apply unique tie handling for clean rankings.

Build an interactive dashboard with a bump chart to compare England, Northern Ireland, Scotland, and Wales over 20 years. Customize headers, filters, and sorting to reveal each team's progression.

Learn to build a bump chart dashboard with highlighting in Tableau, adjust headers and tooltips, and manage table calculations to track ranks over time.

Explore bump charts to rank a limited set of participants, compare trends, and use table calculations with synchronized and reversed axes for clear insights.

Explore home versus away soccer matches using a 41,586-match dataset spanning about 150 years to determine win likelihood at home, excluding neutral venues, via calculated fields and a pie chart.

Create a bump chart in Tableau to rank five countries by total points across years, handling data from two columns to aggregate scores.

Learn to build a bump chart by unioning the data with itself, extract home and away teams and scores via calculated fields, and manage aliases in table calculations.

Verify data in Tableau by filtering table one for England as home and table two for England as away, then sum to 2208 before building the bump chart.

Create and rank a bump chart in Tableau by preparing data, applying year ranges, and summing goals. Customize with colors, shapes, axes, and interactivity for dashboards.

Create a Tableau bump chart dashboard to rank teams by goals scored, apply year filters and interactive highlights, and customize headers, tooltips, and sheet filters.

Explore world internet usage analysis by weighting regions via land area divided by total land area, then multiply by the share not using the internet to yield a priority score.

Learn to create custom territories in Tableau by building a region-based map from country data, using hierarchy and geocoded fields to color, label, and filter by year.

Plan calculated fields to measure regional internet access using country data and a population-weighted approach. Apply filters and table calculations to compute averages, and differentiate data source and dimension filters.

Apply row-level calculated fields to derive population from gdp and gdp per capita, then compute individuals using the internet as a percentage of population for 2017.

Learn how to build aggregate calculations in Tableau to compute regional internet usage by weighting country data, using sums of individuals and populations, and distinguish row-level from aggregate calculations.

Create calculated fields for land area and weights, then use a window-sum and total-percentage table calculation to rank regions by the share of people without internet.

Create a calculated field to compute a priority score by dividing regional people not using the internet by land area weight, revealing South Asia as the highest-need region.

Add region, income group, and country detail in Tableau, compute totals and not-using-internet percentages within each region, and derive region-specific priority scores via region-scoped table calculations.

Develop a region-specific calculation of regional people not using the internet using table calculations and a land-area weight to yield a priority score across income groups.

Explore grouping by a calculation in Tableau to split industries by whether their median base pay is above or below the overall average, using a Glassdoor dataset.

Learn to compute a window average with a table calculation, add a reference line, and create a field to separate above versus below average with color coding.

Create a calculated field with window average, classify median base pay as above or below average using if statements, and color by group with a table-wide reference line.

Explore how table calculations depend on the view, leverage drag-and-drop for viewing, and distinguish discrete measures from dimensions and measures to understand granularity in Tableau.

Create a category-level pay visualization in Tableau by unioning multi-tab data, computing category averages, and highlighting above or below average job titles with sorted, colored bands.

Learn to connect data, create unions, name fields, and build a per-category window average in Tableau Desktop to compare median base pay by category with color and reference lines.

Create a calculated field above or below average using a window average, and color by category to show whether each job title is above or below average.

Explore how table calculations determine above and below average across colors by editing table calculation and setting compute using to job title.

Learn to configure a reference line in Tableau using a window average across the whole table, choose per pane or per cell options, and interpret pay data across health sector.

Explore a Tesla sales dataset in Tableau by building a six-month moving average chart across model S, X, and 3, and practice data preparation and union techniques.

master data preparation in Tableau by fixing headers, setting field names from the first row, pivoting data into year, month, sales, and model, and creating a date field for analysis.

Learn to overlay a moving average on a dual-axis line chart in Tableau using quick table calculation, synchronize axes, and fine-tune previous, next, and current values for the moving average.

Create a parameterized moving average in Tableau by building a calculated field and a period parameter, controlling the window of past values plus the current bar.

Add all models to a Tableau chart, apply independent axis ranges for each row or column, and compare model S, model X, and model three with accessible color choices.

Produce a Europe chart with a six-period moving average including the current month, and compare it to the US chart; parameterize the moving average so managers adjust the period.

Demonstrates building a Europe Tesla sales chart in Tableau, including calculated date fields, pivoted model categories, an area chart with dual axis, and a parameterized moving average for all models.

Compare Marvel and DC per-year new character introductions using Tableau, leveraging Kaggle data to analyze first appearance years and decade-based trends.

Utilize Marvel Wikia data in Tableau to build a characters by year chart, explore gender distributions, and create a decade-based date hierarchy with table calculations comparing yearly and decade averages.

Learn to compute the difference from the global average in Tableau by using count minus window average, edit table calculations, and color-coded labeling across panes and charts.

Practice calculating the difference from the pane average in Tableau by editing a table calculation, adjusting scope, and resetting when the view leaves a decade.

Format the Marvel data chart by renaming axes to 'number of new characters' and 'respective decades average,' and analyze differences from global and decade averages.

Create a chart similar to the Marvel example, but using DC characters with the provided DC Wiki Data CSV dataset, completing the homework challenge.

Create a DC characters chart by sourcing DC new characters data, converting dates to year, calculating decades, and applying table calculations and an average line to compare trends.