<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[The Data School RSS Feed]]></title><description><![CDATA[The Data School RSS Feed]]></description><link>https://www.thedataschool.co.uk</link><generator>GatsbyJS</generator><lastBuildDate>Fri, 11 Sep 2026 12:41:21 GMT</lastBuildDate><item><title><![CDATA[Connecing to an API in Alteryx]]></title><description><![CDATA[API = Application Programming Interface

API allow different applications to communicate with each other.

APIs are useful because they help systems share data and perform actions automatically. For example, a weather app can use an API to get live weather data, or a business can use APIs to connect systems such as Salesforce or a reporting tool, and so on.

Common API terminology

 1. Endpoint

The URL where the API request is sent

 2. Method

The action being requested such as

 * GET - retri]]></description><link>https://www.thedataschool.co.uk/kaori-ikarashi/api-3</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kaori-ikarashi/api-3</guid><pubDate>Thu, 10 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Kaori Ikarashi</dc:creator><image>https://images.unsplash.com/photo-1594904351111-a072f80b1a71?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fEFQSXxlbnwwfHx8fDE3ODg5Njk1NTR8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Data Engineering in Databricks]]></title><description><![CDATA[What does Data Engineering look like in Databricks?

Data engineering is the practice of designing and building systems that collect, store, and process large amounts of data so organizations can use it to make decisions.

One such system is the Data Pipeline

Data Pipelines are built to ingest(extract) data from a source and transform it before sending it off(load) to a storage destination. You may recognize this process from the acronym ETL (Extract, Transform, Load). It's sibling term, ELT, l]]></description><link>https://www.thedataschool.co.uk/lily-kiziriya/data-engineering-in-databricks</link><guid isPermaLink="false">https://www.thedataschool.co.uk/lily-kiziriya/data-engineering-in-databricks</guid><pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Lily Kiziriya</dc:creator><image>https://images.unsplash.com/photo-1626285094816-39f688104ce0?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fGhpdmV8ZW58MHx8fHwxNzg5MDYxODEwfDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Wide vs. Tall: How Computers Really Want to Read Your Data]]></title><description><![CDATA[Table sizes will always vary but their orientation will always either be wide or long. Commonly businesses that store their data in excel spreadsheets format their tables for human eyes; clean, wide, and laid out like a grid with months or years separated out across the columns.

While that layout works brilliantly for a quick glance in Excel, it creates roadblocks when you drop it into a visual analytics tool like Tableau. For example, you wouldn't be able to build a continuous line chart or tr]]></description><link>https://www.thedataschool.co.uk/kainan-hassan/wide-vs-tall-how-computers-really-want-to-read-your-data</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kainan-hassan/wide-vs-tall-how-computers-really-want-to-read-your-data</guid><pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Kainan Hassan</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/christine-sandu-yo5wIVapll0-unsplash.jpg</image></item><item><title><![CDATA[Calculations in Tableau Desktop Part 1: Basic Calculations]]></title><description><![CDATA[An introduction to the different types of calculations in Tableau Desktop, specifically basic calculations.]]></description><link>https://www.thedataschool.co.uk/amelia-young/calculations-in-tableau-desktop-2</link><guid isPermaLink="false">https://www.thedataschool.co.uk/amelia-young/calculations-in-tableau-desktop-2</guid><pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Amelia Young</dc:creator><image>https://images.unsplash.com/photo-1758685734640-5a539c096aed?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDk3fHxjYWxjdWxhdGlvbnxlbnwwfHx8fDE3ODg5NjI5MjZ8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Workout Wednesday 2026 Week 38 Solution]]></title><description><![CDATA[ To solve this week's challenge you'll need the following tools:

 * boxy-svg.com/app (create free account to download the svg)
 * Text editor tool such as Notepad
 * Power BI Desktop



The SVG in the challenge is made up of two circles which is what allows it to be dynamic (think of it as layers on top of each other). The first circle is static in the background and the second circle acts as the dynamic progress ring with a text at its center.



First let's create the canvas in boxy-svg. Set ]]></description><link>https://www.thedataschool.co.uk/fotiana-yan/workout-wednesday-2026-week-38-solution</link><guid isPermaLink="false">https://www.thedataschool.co.uk/fotiana-yan/workout-wednesday-2026-week-38-solution</guid><pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Fotiana Yan</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Screenshot-2026-09-09-154718.png</image></item><item><title><![CDATA[Switching Rows and Columns in Alteryx (How to do Power BI/Excel's 'Transpose' in Alteryx)]]></title><description><![CDATA[Alteryx offers a powerful set of tools and features with which you can transform data - however, you might find that there's no premade tool for switching/rotating rows and columns.

Users coming from Microsoft software might notice the lack of this feature offered by Excel and PowerQuery's 'Transpose' function.

In Alteryx, this is done with a combination of the Transpose and Cross-Tab functions (Alteryx's versions of 'Unpivot' and 'Pivot', respectively).

We'll also use the Record ID tool to a]]></description><link>https://www.thedataschool.co.uk/ken-burgess/switch-rows-and-columns-in-alteryx</link><guid isPermaLink="false">https://www.thedataschool.co.uk/ken-burgess/switch-rows-and-columns-in-alteryx</guid><pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Ken Ueda Burgess</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/08/Screenshot-2026-08-31-204950.png</image></item><item><title><![CDATA[Creating Radar Chart on Tableau]]></title><description><![CDATA[A radar chart in Tableau is a powerful way to compare multiple performance metrics at once, making patterns, strengths, and weaknesses easy to spot. Creating a radar chart in Tableau is quite simple, so let’s dive in and build one step by step.

Creating calculated fields

4 calculated fields are required to create radar chart

 1. Angle calculation

I use Department to define each category around the radar chart.

RUNNING_SUM(2*PI()/MIN({COUNTD([Department])}))

= Divide a 360° circle equally a]]></description><link>https://www.thedataschool.co.uk/kaori-ikarashi/how-to-make-radar-chart-on-tableau</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kaori-ikarashi/how-to-make-radar-chart-on-tableau</guid><pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Kaori Ikarashi</dc:creator><image>https://images.unsplash.com/photo-1492515114975-b062d1a270ae?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE2fHxzcGlkZXIlMjB3ZXxlbnwwfHx8fDE3ODg1MzY5NjZ8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Fixing Tableau dashboard tab order with XML]]></title><description><![CDATA[When building an accessible Tableau dashboard, it’s worth thinking about keyboard navigation as part of the layout itself.

By default, Tableau generally follows a top-to-bottom, left-to-right tab order across dashboard objects, so elements across the top are reached first, then the next row down, and so on.

That means you can often make the default order work for you simply by designing with it in mind. For example, placing filters and controls across the top of the dashboard can create a sens]]></description><link>https://www.thedataschool.co.uk/holly-andersen/fixing-tableau-dashboard-tab-order-with-xml</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/fixing-tableau-dashboard-tab-order-with-xml</guid><pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Screenshot-2026-09-07-120305.png</image></item><item><title><![CDATA[Design Debt: The Invisible Tax We Pay for Skipping Design]]></title><description><![CDATA[A dashboard can be technically correct and still be a poor product. The data is accurate, the calculations work and all requirements have been implemented, but the dashboard itself may still be difficult to understand or use. This is design debt: the invisible tax we pay for skipping design in dashboard development.

Similar to technical debt, it accumulates when we optimize for immediate delivery and leave problems to be solved later. We often see the result as clutter, inconsistent design or c]]></description><link>https://www.thedataschool.co.uk/janina-grauel/design-debt</link><guid isPermaLink="false">https://www.thedataschool.co.uk/janina-grauel/design-debt</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Janina Grauel</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Portfolio-Title-Images--97-.png</image></item><item><title><![CDATA[How Excel Secretly Inflates Your Files (And How to Fix It in 30 Seconds)]]></title><description><![CDATA[We’ve all been there: you open an Excel workbook expecting a quick glance at a few hundred rows, but you find yourself staring at the loading wheel spinning and spinning. You check the file size, and it's somehow 50 Megabytes! There are no images, no complex macros, and only a couple hundred rows of data.

So where is all that phantom (and problematic) weight coming from?

The Sneaky Culprit: Excel’s "Used Range"

As you're working on a workbook, Excel isn't just tracking the cells with numbers/]]></description><link>https://www.thedataschool.co.uk/bianca-beingolea/how-excel-secretly-inflates-your-files-and-how-to-fix-it-in-30-seconds</link><guid isPermaLink="false">https://www.thedataschool.co.uk/bianca-beingolea/how-excel-secretly-inflates-your-files-and-how-to-fix-it-in-30-seconds</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Bianca Beingolea-Joseph</dc:creator><image>https://images.unsplash.com/photo-1569235186275-626cb53b83ce?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDV8fGhlYXZ5JTIwZmlsZXxlbnwwfHx8fDE3ODg1NTU4NTZ8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[How I Got YouTube Videos Working in Tableau Public with a GitHub Pages Hack]]></title><description><![CDATA[I recently built a Tableau dashboard about Dolly Parton covers. One of the interactions I wanted was a video player so users could click a song title and hear the song.

Simple in theory. In practice, getting a YouTube video to play inside a Tableau Public dashboard turned into a small lesson in browser security, embeds, and the usefulness of having a tiny webpage you control.


The problem

Tableau has a Web Page object that can display a URL inside a dashboard. I already had direct YouTube lin]]></description><link>https://www.thedataschool.co.uk/salome-grasland/how-i-got-youtube-videos-working-in-tableau-public-with-a-github-pages-hack</link><guid isPermaLink="false">https://www.thedataschool.co.uk/salome-grasland/how-i-got-youtube-videos-working-in-tableau-public-with-a-github-pages-hack</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Salome Grasland</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Screenshot-2026-09-04-155454.png</image></item><item><title><![CDATA[One Workflow, Three Ways: Rebuilding KNIME in Microsoft Fabric]]></title><description><![CDATA[A KNIME workflow is easy to follow: data moves from node to node, with each step representing a transformation. But what does the same workflow look like without the nodes? To find out, I took a simple KNIME workflow and rebuilt the same transformation logic three times in Microsoft Fabric: with Dataflow Gen2, SQL and Python.

The goal wasn't to find the best tool, but to see how the same data logic translates between three very different approaches.

All data used in this exercise is synthetic ]]></description><link>https://www.thedataschool.co.uk/janina-grauel/one-workflow-three-ways-rebuilding-knime-in-microsoft-fabric</link><guid isPermaLink="false">https://www.thedataschool.co.uk/janina-grauel/one-workflow-three-ways-rebuilding-knime-in-microsoft-fabric</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Janina Grauel</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Portfolio-Title-Images--91-.png</image></item><item><title><![CDATA[Dashboard Week- Day 5- World Cup Dashboard]]></title><description><![CDATA[Last day of dashboard week! With just a few hours on the clock, we were tasked to design a dashboard using a World Cup Matches dataset.

When I think of the World Cup, the country that immediately comes to mind is France. Therefore, for my dashboard I wanted to explore what has contributed to France’s victory in the Men’s tournament. The main question I had was has France always been a dominant team, or has this been a more recent trend? 

Today’s Limitations: The blockers today were the calcula]]></description><link>https://www.thedataschool.co.uk/carla-villafana/dashboard-week-day-5-world-cup-dashboard</link><guid isPermaLink="false">https://www.thedataschool.co.uk/carla-villafana/dashboard-week-day-5-world-cup-dashboard</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Carla Villafana</dc:creator><image>https://images.unsplash.com/photo-1531752148124-118ba196fc7b?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE5fHxmcmFuY2UlMjBzb2N8ZW58MHx8fHwxNzg4NTUxMDYyfDA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 5: World Cup Draw-ma]]></title><description><![CDATA[Today was our final day of Dashboard Week, and our task was to work with data relating to the World Cup. I decided to focus on matches ending in draws, and specifically looked at draws over time, by tournament type, and by team to see how these factors influenced match outcomes.

The first part of my process comprised data exploration and figuring out the specific factors mentioned above. As I started building out charts for each factor, I wanted to consider best practices, so I would constantly]]></description><link>https://www.thedataschool.co.uk/stefani-hermanto/dashboard-week-day-5-world-cup-draw-ma</link><guid isPermaLink="false">https://www.thedataschool.co.uk/stefani-hermanto/dashboard-week-day-5-world-cup-draw-ma</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Stefani Hermanto</dc:creator><image>https://images.unsplash.com/photo-1527871454777-032ec3f75edc?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDE3fHx3b3JsZCUyMGN1cHxlbnwwfHx8fDE3ODg1NDgxMjd8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week: Day 5]]></title><description><![CDATA[Today’s dashboard day gave us a simple prompt: take the dataset from our most recent applicants’ final interview (which was data based on World Cup matches) and show how much we’ve improved by building our own take on it. I landed on the question of how global the World Cup really is. The idea was a simple one to try and provide a more high level view of how nations participate in the World Cup. However, I quickly ran into my first challenge with the fact that Tableau had no way of knowing which]]></description><link>https://www.thedataschool.co.uk/gerard-najarro/dashboard-week-day-5-51</link><guid isPermaLink="false">https://www.thedataschool.co.uk/gerard-najarro/dashboard-week-day-5-51</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Gerard Najarro</dc:creator><image>https://images.unsplash.com/photo-1527871252447-4ce32da643c6?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEyfHx3b3JsZCUyMGN1cHxlbnwwfHx8fDE3ODg1NDI3OTN8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 5: Home Field Advantage in the World Cup]]></title><description><![CDATA[This was our last day of Dashboard week, and today we were given World Cup data ranging from 1936 to 2022. We were given free rein to create any type of dashboard we wanted; however, the caveat was that we only had till 1:30 pm. 

When looking through the dataset, what intrigued me was whether there truly was a home-field advantage for the host country. I did some data exploration and found significant differences in win rates, goals scored, and Tournaments won by the host country compared to it]]></description><link>https://www.thedataschool.co.uk/ping-hill/untitled-295</link><guid isPermaLink="false">https://www.thedataschool.co.uk/ping-hill/untitled-295</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Ping HIll</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/vienna-reyes-qCrKTET_09o-unsplash.jpg</image></item><item><title><![CDATA[Dashboard Week Day 5: World Cup Wordle!]]></title><description><![CDATA[Today is the last day of Dashboard Week! What a wild ride!

Today, we were given historical World Cup Data, and instead of creating an analysis like I usually do, I decided to be a bit ambitious and create a game!

If you've heard of the game Globle, you essentially guess a country, and the closer you are to the target country, the darker in color.

My idea was quite similar, except the target country would be the World Cup winner for that year.

Because I had to measure distance between countri]]></description><link>https://www.thedataschool.co.uk/sean-fei/dashboard-week-day-5-48</link><guid isPermaLink="false">https://www.thedataschool.co.uk/sean-fei/dashboard-week-day-5-48</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Sean Fei</dc:creator><image>https://images.unsplash.com/photo-1593632717071-218c1d85c663?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fEdsb2JlfGVufDB8fHx8MTc4ODk2OTg2Nnww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week, Day 5: World Cup]]></title><description><![CDATA[Happy Dashboard Week Day 5!



For the final day of Dashboard Week, DSNY12 was given a set of World Cup data that DSNY14 used for their application dashboards as a way of comparing how far we’ve grown ourselves since first starting training.



My first instinct when taking a look at the dataset was to create a dashboard with interaction elements and actions. 



I thought it could be interesting to have a dashboard that uses soccer player marks to move around a field in a scatter plot, and that]]></description><link>https://www.thedataschool.co.uk/helena-reichenvater/dashboard-week-day-5-world-cup</link><guid isPermaLink="false">https://www.thedataschool.co.uk/helena-reichenvater/dashboard-week-day-5-world-cup</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Helena Reichenvater</dc:creator><image>https://images.unsplash.com/photo-1574629810360-7efbbe195018?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDR8fHNvY2NlcnxlbnwwfHx8fDE3ODg1NDAyNDB8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Power BI - Interactive Dashboard]]></title><description><![CDATA[





I built this Sample Superstore dashboard during our Power BI training at The Data School. The aim was to create a clear overview of sales performance while practising several useful Power BI techniques.

The dashboard uses DAX time-intelligence calculations to compare month-to-date results with the previous year, alongside year-to-date comparisons and running totals. I also used measure switching to let users move between sales, profit, quantity and orders, and experimented with formatting]]></description><link>https://www.thedataschool.co.uk/holly-andersen/power-bi-interactive-dashboard</link><guid isPermaLink="false">https://www.thedataschool.co.uk/holly-andersen/power-bi-interactive-dashboard</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Holly Andersen</dc:creator><image>https://www.thedataschool.co.uk/content/images/2026/09/Screenshot-2026-09-04-161826.png</image></item><item><title><![CDATA[Custom themes in Power BI]]></title><description><![CDATA[A guide to quick formatting in Power BI]]></description><link>https://www.thedataschool.co.uk/jude-royall/custom-themes-in-power-bi</link><guid isPermaLink="false">https://www.thedataschool.co.uk/jude-royall/custom-themes-in-power-bi</guid><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate><dc:creator>Jude Royall</dc:creator><image>https://images.unsplash.com/photo-1525909002-1b05e0c869d8?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEwfHxjb2xvdXJzfGVufDB8fHx8MTc4ODUyMDk0NXww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item></channel></rss>